So, we have Arjun here, hello, and he is going to talk about how I hard coded core machine learning algorithms from scratch in Python, what I learned and why you should do, okay. Right. So, welcome everyone, welcome to the first talk of the day. My name is Arjun and I will be talking about how I hand coded the core machine learning algorithms from scratch in Python, what I learned and why you should do. So, let's get started. My name is Arjun and I am an independent software developer and hacker from New Delhi, India. I was a Google Summer of Code student in 2016 with JQuery Foundation. I also worked at a high growth startup in Bangalore before moving back to Delhi and pursuing my other interests in computer science and music. My current interests include Python, machine learning, full stack development, lower level kernel stuff, Rust, and Lisp. You can find me on the social links on the right. So, the premise of the talk or let's start with the why. So, machine learning just like any other field of science has certain basic concepts and algorithms that act as the fundamental building blocks of everything wonderful that you see around you from your YouTube algorithms to your phone's camera. It's everywhere. Unfortunately, these algorithms have been provided to us as wonderfully packaged, optimized, and easy to use abstractions. Think scikit-learn and friends, but we essentially use them as black boxes. So import a classifier from your favorite library, throw your training data at it and boom, you have a predictive model. You can start making predictions and believe it or not, that's a magnificent thing. It's a massive win for practicality, pragmatism and performance. But to me, it seemed a little bit of too much of magic. So I decided to pop open the hood and look inside and see what happens under it. So this talk gives you a crisp take on that. Okay, so demystifying machine learning. So, what is machine learning? I'll do you one better. Who is machine learning? I'll do you one even better. Why is machine learning? So machine learning is a very overloaded term these days and there's an entire spectrum of beliefs about what machine learning is. So what one extreme of the spectrum believes what machine learning is. So they think it's magic. You just give data and you get predictions. And what the other extreme of the spectrum believes what machine learning is. So they just say it's the statistics man, it's the glorified statistics. So well, yes and no, it's somewhere in the middle. The fundamentals of ML are still true to core statistics. But in practice as of today, it has grown into something much bigger. In a simplest form, when you zoom out and tune out all the noise, machine learning pertains to extraction of knowledge from data by building intelligent agents which are often self-learning in order to make predictions. So ML is a wide field, we'll just be talking about the supervised learning part of the field in this talk. So supervised learning in a nutshell, we have labeled data, we give it to a machine learning algorithm which gives us the predictive model. We give new data to the predictive model and we can make predictions. Simple, right? So chapter two, the algorithms. We'll be covering the perceptron classifier, the Adeline or the adaptive linear neuron classifier and the ever so popular logistic regression classifier. We will cover the maths behind them, how they work and how you can write your own classifiers. So let's get started. So you can't do data science without data, right? The data set we'll be using is the Iris flower data set. It's very popular. It has 150 samples of the Iris flower. It has three different species, Virginica, Cetosa and Versicolor. And we have 50 samples of each, with four measurements of each sample. This is what a data looks like, right? And when we say x, 150 top, one bottom, we mean the first column of the 150th sample. Now when scientists began with the field of artificial intelligence, this started with the neuron.  So how the biological neuron works is it gets signal from one end of the extremities called dendrites and then those signals propagate through the cell body. And if the collective strength of the signal is greater than a threshold and output is generated on the right side of the axiom and so it propagates. Now scientists modeled this behavior as a logic gate with binary output such that if the input exceeded a certain threshold, the output was 1, else it was 0. And it was done by two gentlemen named Warren McClough and Walter Pitts. And this came to be known as the MCP or McClough Pitts model. So shortly after, a gentleman named Frank Rosenblatt, he proposed the perceptron algorithm based on the MCP model. And the perceptron algorithm was the first algorithmically described ML algorithm in which he proposed that the algorithm would automatically learn the weight coefficients which when multiplied by the input samples would decide whether the neuron, the MCP neuron would fire or not. And this could be used for the classification purposes such that if it fires, you can classify it A and B, else B, which is often numerically encoded as 1 or minus 1. So we'll be talking about vectors a lot in this talk. Let's take a minute to properly understand what we're talking about. So there are basically two major systems for understanding vectors. First is the physics perspective and second is the computer science perspective. The physics perspective defines vectors as arrows in space and these arrows have magnitude and a direction. And as long as those two are same, we can move around the arrow anywhere in the space and it's still the same vector. So an arrow or a vector in a 2D plane is a 2D vector and an arrow in a 3D space is a 3D vector. Well the CS perspective defines vectors as, get ready for it, ordered list of numbers. That's it. In CS, vectors are just a fancy name for ordered list of numbers. And what makes a vector 2D is the length of the list is 2, mind you, right? So if you want to model a house as a vector, you can do so if you only care about the house number, the price and the square footage. And if you change the order of these measurements is no longer the same vector because they are ordered, the ordered matters. You can add two vectors and you can multiply a vector by a scalar. That scales the vector, therefore it's called a scalar. Now how the perceptron worked without getting into the math is first you initialize a weight vector or a random set or an order list of weight values which is and the length of the weight vector is equal to the number of columns in your data set. And for each training sample, mind you, each training sample, this is the schematic of the perceptron model, right? You can, we'll handle the W0 term in a minute. We, for each data sample, we calculate the linear combination of the net input and the weight vectors which goes to a decision function which, if the input is greater than a threshold, we predict 1 else minus 1. And if we have an error, this is used to correct the weights so that the next prediction is better. And this is done for each of the sample. So the terms that we'll be using is the input vector or an order list of your data. The weight vector or an ordered list of weights which are often random numbers. And the net input which is the linear combination of the weight vector and the input vector which we can denote as w1x1w2x2wmxm is w is the weight vector and x is the input vector. Vectors are just ordered less, mind you, don't get... So a refresher on matrix multiplication. So when you have to multiply matrices, the inner dimensions have to be same, right? So if you have a matrix of n cross m, so other matrix has to be of m cross p. So you'll get n cross p matrix. Now, so in perceptron, the decision function is a variant of the unit step function. As you can see from the yellow circle definition, if the input, net input is greater than some threshold theta, we predict 1 else minus 1. For simplicity, we bring this theta to the left side of the equation which now becomes minus theta plus z greater than equal to 0. This minus theta term is also known as the bias unit and we denote it by w0. So now our net input becomes w0x0w1x1 tilde w2x2. Now x0 is 1 and w0 is minus theta. We also call it the weighted 0 and this has to be treated separately. Now the length of our weight vector is 1 plus the number of columns in data because we added the bias unit. So the model also learned the threshold. Now perceptron learning rule states that you have to update the weight according to weight plus some weight update delta w such that the weight update is defined by the learning rate eta multiplied by the target minus the prediction that you make into the feature that you're calculating the weight for. Now, as you can see from the pink circle here, if you predict correctly, if you predict the class was 1 and we predicted 1, the weight update is 0. We do not update the weight if we made the correct predictions, right? Now as you can see from the pink circle above, if we make wrong predictions, the weights are shifted to the side of the correct class. For example, if we predict 1 and it was minus 1, if we predict minus 1 and it was 1, which means the input did not cross the threshold, which means we need to increase the weight so that next time it crosses the threshold and we predict 1. And also the amount of weight update is proportional to your misclassification. The bigger the misclassification, the bigger the weight adjustment. So let's get started. So this is the Python code to import the perceptron from the scikit-learn library and this is how most of us write it. We import the perceptron classifier, we give it three arguments, the n iterations, the eta and the random state. Then we have the instance of which we use the fit method to pass our training data which fits our data on the model. Then we can use the predict method of this instance to make predictions and we have to have some accuracy measure to test how this model perform. So from this we learn that we need a class perceptron that has three attributes, the learning rate eta, the number of iterations and one random state and the instance that we use of this class must have a fit method, must have a predict method and must have some way to calculate the efficiency. So let's get started. With all this knowledge, we know everything to write our own perceptron classifier. So we'll be using numpy because all this operation will be backtrice. Now we initialize a class perceptron, then we give the three parameters as we just saw eta and iterations and random state with some default values and we assign them to the self-attributes of the same name. We all write. So now we have the net input method which is the linear combination of the data and the weights plus the bias unit. So this is the linear combination that becomes a net input and we have the predict method which says that if the linear combination is greater than 0, predict 1 else minus 1. Now the fit method is where the stuff happens, right? We give it our data set and our training label set. We generate a random set of weight vectors with random numbers and the random state was used to seed the random number generator so it's reproducible. Then we maintain an errors array to see how a model performed. Then we loop over the data set for n iterations and during each iteration we loop over each sample and update the weight according to the perceptron learning rule. eta into target minus prediction into the value of the sample, right? And we update the bias unit separately and we maintain how many errors we got per iteration and it returns that instance, right? Now this is the code to plot and train how a perceptron model performed. We just import data, we pass it to perceptron and we use a function to model it. So just to visualize, this is what our data looked like. We just, for simplicity, we just took two samples of flowers and we just took two dimensions. So this is the data looked like. This is how the official scikit model performed and this is what the model performed that we wrote. As you can see, it's pretty close, right? But the logic must be similar. Now this is the error graph. As you can see, after sixth iteration, there are no errors, which means the model converged, which means it find an optimal decision boundary. The drawbacks of perceptron model is it's actually a very simple model. It works well for linearly separable data but not so much for non-linear because the decision boundary is linear and at any point of time at least one sample is misclassified, which means the weights will never stop updating, right? So that's why we need a number of iterations to stop the model after those iterations, even we have misclassifications. So based on this, we can learn about the adaptive learning neuron or the Adeline model. Now Adeline model is pretty similar to the perceptron model except it illustrates the key concept of defining and optimizing a continuous cost function, which really lays the foundational groundwork for understanding the most sophisticated works like machine, like logistic regression and support vector machines. So it's pretty similar to perceptron as we can see, we have this new term called activation function whose output is now used to update the weights and not the output of the threshold function. This is how it compares to perceptron, right? You can visualize it nicely. We have the activation function as output correcting the weights and we have the threshold function, which predicts based on the output of the activation function. So the difference between Adeline and perceptron is the weights in Adeline are updated by the activation function and what is this activation function? In case of Adeline is just the identity function phi of z equal to z. Basically whatever you give, it just gives you back. Basically it does nothing for now, but we need the concept here. So perceptron compares the true class labels, which is the correct class label in label data against the predicted class labels, which is also one or minus one, one or zero, one or minus one, sorry, they are binary outputs. But Adeline compares the true class label with the activation from true class label from the output of the activation function, which is real value. It may not be an integer because it's just forwarding the linear input, the linear combination of the weights. Adeline uses cost function minimization and gradient descent, which is also another very overloaded term. We'll be explaining, we'll be exploring this. In perceptron, while weights are updated after each sample, in each iterations, you could understand this would get tiresome if you have a data set of million items. You'll be updating weights after each weight, which is not ideal. So Adeline updates the weights collectively after each iteration, not after each sample. That is why it is also known as batch gradient descent. There's a variant called stochastic, which is used for deploying web, but we'll be talking about bad gradient descent. Now, what is cost function minimization? One of the central features of the machine learning algorithm, specifically supervised ML, is a well-defined objective function that has to be optimized while the algorithm learns. Basically, the cost function is how the algorithm knows, it tells the algorithm how to learn. For example, when you're traveling, you could optimize for time. Then you would take the actions that it'll take to your destinations, the fastest, no matter the cost. But if you're optimizing for cost, you'll probably take cheaper means of transport and they'll take more time. So you could optimize. So the cost function is the degree of what we have to optimize while learning. So the cost function for Adeline is sum of square of errors, where error, y is the true class label, phi of z is the activation output, which is just your net input. And why do we choose this function and this function only? Why not any other function? Because it has the following benefits. First, it's continuous, which makes it differentiable. Second, the cost function is convex. It's sum of square of errors. You remember the graph of x square, right? It's a parabola. And both of these factors enables us to use a very powerful optimization technique called gradient descent, right? Now gradient, the gradient descent can be thought of as going downhill until you reach a local or global maxima. Gradient is, by the way, just a fancy word for slope. And how you optimize, you remember how you minimize a function. You differentiate it, which gives you the slope. And if it's zero or closer to zero, that would be your minima. Now we differentiate the function, we find the gradient and we take steps opposite in the direction of the gradient. Since it's a convex function, it was always bring us towards the minima, right? Now, in each pass, we take a step in the opposite direction of the gradient. And the size of the step is determined by the learning rate eta that we choose, which makes the learning rate important. Now, the addling learning rule is pretty similar to the perceptron learning rule. Update the weights, w plus delta w, but now delta w is defined as the opposite step in the direction of the gradient. And step size is proportional to the learning rate eta. The cost function, when we differentiate, when we have to partially differentiate with respect to each weight, by the way, then long story short, it comes down to the one on the bottom, the pink circle, right? And which is just true class label minus the activation function output into the sample one, which is pretty easy to code, right? Now, we can write a addling classifier. The in dunderinite method is pretty similar to the perceptron classifier. We take eta and iterations random state and we assign them to the self variables of the same name. Then we have the net input, which calculates the linear combination plus the bias unit, of course. Don't forget the bias unit. And the activation function is now just an identity function. You can see, we pass a network, it just outputs unit input. Then we have to predict method, which is also similar. The output from the activation function has to be greater than equal to zero for the neuron to classify it as one. Else we classify it as minus one. Now, the fit method is where the changes happen. We start in a similar fashion. We see the random number generator. We initialize a weight vector plus one for the bias unit. Then we maintain a cost array. We loop over the entire data set. You can notice we don't loop over the e-sample here. We calculate the net input from our data matrix. Then we calculate the output, which is the activation functions output. Then we calculate the errors, which is the true class labels y minus the outputs of the activation functions. These are NAMPA errors, by the way. So all this is vectorized. We update the weights based on the Adeline learning rule and we do it for the entire sample, which is mathematically represented as feature matrix transposed and we take dot product with the errors. And we update the bias with the sum of errors because it's for the entire data set, not one sample. And we maintain a cost of how this iteration performed and we maintain it on an array. So this is what our data set look like. Now, we'll be training, just to show you how the learning rate affects it, we'll be training the Adeline model with two data, two learning rates, one of which is too big, one of which it is too small. Right, 0.1 and 0.0001. So the one which was too big, you can see the costs are increasing, which means because the step size is too big, we are always overshooting the minima and the weight correction is proportional to the amount misclassified. So it will increase. And you can see on the right hand side, when it was too small, we are actually going down on the cost, but it's very slow. It would need many number of iterations more. This is how the one with learning rate one performed and it did perform, but it did not optimize our cost function. And this is how the learning rate was too slow performed. As you can see, it would have converged given more iterations. Now, we get to the ever so popular logistic regression model. So you can see everything builds up on the previous thing. So logistic regression also builds up on the concepts we have learned till now. It's very popular. That would be an understatement, right? Now, in spite of the name, it's not a regression model. It's a classification model. Now, why it is so popular? One, it's easy to understand and implement. Number two, it deals with the probabilities of a sample belonging to a certain class given certain features, not binary yeses or noes. When your weather channel says it's 80% chance of raining, they probably are using logistic regression among other things, right? It's pretty similar to Adeline, but we have a different activation function and we have different cost function. Now, the terms that you need to be familiar with to understand logistic regression, first is the odds ratio, which is the odds in favor of the particular event, right? And the odds ratio can be written as P divided by one minus P, where P is the probability of the positive event. By positive, we mean the event that we're looking for, not necessarily positive in context, like presence of a disease. Now, the logit function is just the natural log of the log odds. What it does is it takes a probability measure, which is between zero to one and converts it over the entire real number range minus infinity to infinity. This is how the graphs look like. As you can see, the input is between zero to one and the output is spread over the real number range. We actually want the opposite of this. We want real valued inputs. That would be a proxy for a net input, which is the linear combination. And we want an output between zero to one, which would be could treat as a probability measure that a sample belongs to that class. Now, the opposite or inverse of logit function is the sigmoid function. It is defined as one, you can see on the green dot. Phi of z is one divided by one plus e to the power minus z, where z is again our linear combination of the input vector and the weight vector. Now, as we can see, the input domain is minus infinity to infinity and the range is zero to one. It takes real input converts it to zero to one and there's an intercept on zero point five, right? So now instead of the identity function, this is our activation function. This is the schematic of Adeline versus logistic regression. As you can see, the activation function is sigmoidal, which whose output is now fed to the threshold function. You can use that output as a probabilistic measure, instant of a binary yes or no. But if you need a binary yes or no, those are also present. Now, the cost function of logistic regression long story short is the one on the bottom, right? So we want to maximize the likelihood that a sample belongs to a particular class. So in practicality, in practice, it's easier to maximize the log of that likelihood, which we call the log likelihood. And since we have to, since which is equal to minimizing the negative of log, maximizing log likelihood is equal to minimizing the negative of log likelihood. And we know how to minimize, right? Gradient descent, gradient descent, gradient descent. So this cost function where yi is the true class label and y hat is the probabilistic output from the activation function. Just to give you an idea, when y is zero, the first half becomes zero. When y is one, the second half becomes zero. And this function has been chosen for many reasons. Now, also because the gradient comes out to be similar to that of Adeline when we differentiate. Yeah man. So the logistic regression classified is pretty similar. We start with the init method. Then we have the same three attributes. Then we have the activation function, which is now sigmoidal, as you can see. Then we have the net input, which is linear combination of weights plus the bias unit. Then we have the predict method, which now predicts that if the probability is greater than 0.5, we predict one. As we predict zero or minus one. Now the fit method is pretty similar. We generate, we seed a random number generator. Then we make a weight vector with random weights plus one for the bias unit. Then we maintain a cost array. Then we loop over the each data set. We calculate the net input. We calculate the output from the activation function. We calculate the error vector. These are also numpy errors. So this is vectorized. We update the weights according to the gradient descent model, which is similar, x transpose dot time errors. That's why also that function was chosen. And we maintain the bias, we update the bias unit as sum of errors because it's for the entire data set and not one sample. And we maintain the cost. Now you can see the cost function is different. It's not sum of square of errors like Adelaide. And we append those costs. So this is how you train the logistic regression model, which you impaired from scikit-learn. You use, you seed the classifier. The C term here, by the way, comes from a concept from support vector machines that we will not be talking about. But to understand the logic that we studied was enough. The fit method, we can pass our data and we can use the predict method to make predictions and then we can calculate the accuracy score. So this is how the official logistic regression model perform. And this is the one how we wrote perform. Identical. So to summarize, you learn the very core fundamental machine learning algorithms. You learn the math behind them. You understood how they worked and how you can actually write your own. You learn the perceptron, Adeline, and logistic regression and powerful techniques like gradient descent. You learn the necessary math. You implemented them in Python. And you learn how the data wraps, goes inside the classifier. So the learnings were that why you should also do this? Like why? First is you understand that machine learning is much less of the magic than media makes out to be. It's backed by hard science. Second, data is hard. Data is a really hard one. You can structure your data in any ways, number of ways possible. And if you know how the data is going to be utilized by your classifier, you can structure it that much better. And your model, by the way, is as accurate as the data you feed it. So even though black boxes exist to save you time, it's all right to sometime look inside them. People often give the example of driving a car and not knowing how the engine works. And because they don't need to, which is fine. But think about the days that your car won't start or breaks down in the middle of the commute. If you know how the engine works, you can probably figure out the latest. You can probably figure out or narrow down what is wrong and get it fixed that much quicker. And libraries, which is in my humble opinion, always a better position to be in. So libraries like scikit-learn have done a tremendous job by giving us highly optimized tools. But when you really understand what's going on, on the day that your model does not work, you can debug it that much better. And that, according to me, makes us better engineers for it. So thank you for listening. So acknowledgements. I couldn't have done this with the help or knowledge of these wonderful people. And the Pycon organizes Abhishek and my reviewer, Mr. Ashok Govind Rajan. Thank you. And yeah, question and answer. We just take two questions. Hello. Hi. I had this question. You said that for the algorithm to work, the function, it needs to be convex. If it is not a convex, because if it is a non-convex region, then how will we deal with that? So we chose the function that we want to optimize, right? We chose the Samo square of errors. That's our cost function. We decide what the cost function is. And it may not be convex, but then probably you won't be able to use gradient design. We wanted to use gradient design. Therefore, we chose a convex function. So if we have non-convex. The cost function, it does not come with the algorithm. Basically, the people making the algorithm decided on that cost function, right? Thank you. Are you on this? Suppose, if I have a less amount of data, so how you can train the model and what will happen in predictions means? Less data. So your predictions quality depends on your data, right? So if you don't have enough data, it would probably have high variance. So it won't generalize to new data better. The solution is either live with it or get better data. If you have low data, you'll get a quality of the model, which is proportional to the quality of the data. The only way to increase the quality of the data, you can optimize your hypermarameters, but they only go so much. They can only do so much for you. The quality of data is directly proportional to the accuracy of your prediction model, right? So my answer would be get better data. If you can't get better data, work on your hyperparameters. All right, everyone, we've run out of time. So thank you for being here. Right. Meet me outside if you have any other question, right?


Before I start talking about some of the plant breeding and some of the other aspects we do on developing crops for the organic, just a little bit about the Department of Plant Sciences. It's the largest department on campus. We have 42 faculty working in areas from agronomy, plant breeding, genetics, weed science, horticulture, cereal science and food science. So we really have a diverse group of faculty and I think that really helps when you think of taking that food from the field to the plate because we're looking at all aspects of those type of activities. As Steve said, I was hired in 1988, my gosh, that's 33 years ago and I was hired to develop Six Road Barley for the large brewing companies. Basically, he had two customers, you can figure out who those are. And that industry is very, very particular and it's really true of all the large food companies. Since they have exact parameters that they want you to develop for in those new varieties for them to be accepted. It's going to have the right protein, it's going to have the right plumps, enzyme levels, extract the proteins to make the foam in the beer, name it, they have traits for it. And if you're a farmer and tried to sell your barley, doesn't it seem like some years they just find something wrong with it, right? So they don't have to buy it, like the proteins too high. In a year like this, in a drought condition, they're going to be buying high protein. You guys have long memories, they tend to have very short memories, right? So we're just as challenged and sometimes get just as frustrated as you do when it comes to developing these new varieties. It was about less than 15 years ago, my colleague Paul Schwartz and I, we were getting a lot of calls from the craft brewing industry and the craft malting industry. And one of the biggest questions they had is why aren't you developing varieties for us? And so we had conversations with them, what are you looking for? And they started saying, well, we want lower protein, well, we can do that. But then they started, what was really interesting was they wanted lower enzymes, lower proteins that go into foams. And a lot of these other traits that when you ask the large breeding companies, especially about enzymes, give us more, give us as much as you can give us for these enzymes because they have corn and rice in their beer. And they need those enzymes to help break down that corn or rice. But if you're looking at a craft beer, it's 100% malt. They don't need all those enzymes to break down the corn and the rice. And so they wanted lower enzyme levels. And what was interesting is we had this conversation, it came to be that I was throwing out stuff that would work for them. And it was just, it was kind of, you got to be kidding me. I just tossed this stuff. And so that's been really exciting, working with the craft industry because we just released a variety this year called Bruceke. And the farmer we work with is Greg Kessel. And he owns part of a malt company outside Bismarck called Two Track Malt. The name, you know, a lot of people want to know, how do you come up with variety names? Well, his kids had a Facebook contest and they came up with the name Bruceke. So that was pretty cool. And, but this is specifically our first variety release for that craft industry. It's going only to the small maltsters and it's only going to the smaller brewers, three to seven barrel systems, though there's some others. And it's really, he's built such a following with this new variety in a single year that he's actually paying another malt company in Butte Montana to produce malt for him. So it's taken off very quickly. And so I started thinking as a breeder, you know, if you can do this in barley, why can't you do this in wheat? Why can't you do this in potatoes and dry beans and, you know, just name the crop? Because if we're throwing out stuff in barley that probably would work the local foods. What about our other crops? And that's about the same time Steve introduced me to Dan Barber and Fred Kirschman. And we started having this conversation as like my God, we're asking for the same thing. You know, we want those culls. We want those varieties that you discard. And when we started looking at wheat, exactly, our dose, you know, half our wheat in this state is exported to Asia. And because it's considered an additive, they want to combine our very, very strong high protein wheat with their locally produced wheat to bring the quality up of their local wheat. And when you try to do that with artists and breads, it just doesn't work. It's way too strong. And so we started, Andrew Greener, a wheat breeder, started sending the baker at Blue Hill at Dan's restaurant, you know, varieties, Glen, nope, too strong. Some other varieties from NDSU, still too strong. Fowlering and prosper, we consider those weak wheats, not too strong. This is how we started thinking, oh my God, we have to go really fine. And so it's these kind of conversations you have with these artisan bakers and these local foods that you start identifying what really works. So we're still working on that project. But working through Dan on the Oat Breeding Program, we identified our Mike McMullins, our oat breeder. He developed this hull-less oat that's, it's really a replacement for Paul Oates. And what was unique about it was high in lipids.  Why? Lower your cholesterol, right? That's the heart-healthy part of oats. Well, the thing with this oat variety is that it couldn't get that heart-healthy label because it was over 8% fat or 8% lipids.  Why don't you release it? We've got to find something out of it. If you know Dan, you can just hear him doing that and Burton's laughing because he's seen it. And it got to the point where he believed in the variety. It's done very well on the farms. And so Row 7, his seed company, licensed the variety. So they're just trying to find a market. Again, that's the biggest challenge, finding a market for those crops. And he's designing different food products and so forth. Another example we had for an NDSU release that didn't make it in the conventional market is our potato program. This is Susie Thompson, our potato breeder. One of the biggest markets is Russet's for your local fast food restaurants. And that potato's got to be a certain shape, certain size, certain weight, certain starch, because every French fry has to be the same, right? Well, there's a lot of things that come out of her program that just don't work, but they're really cool. She had this pinto, or this potato was pinto colored. On the flesh it was, or on the skin it was reddish and bluish and yellow. And like a pinto pony, just a beautiful potato. And then you cut it open, it's just yellow inside. Great flavor, but this is not going to go to your large food companies. But you can surely see that variety in your local foods, your farmer's markets, your local supermarket. And so this is another variety we're trying to find a home for it. And Row 7's going to be licensing that variety from us too. We're not going exclusive to Row 7, but you have to look for those opportunities to market those new varieties. Because not only with our large commodity markets, but that local foods is the same challenge, variety acceptance. The last example, I'll go back to Farm Breeder Club. They developed Dillon Wheat. And we had some calls last, some conversations with some people that there might be a big test wanted for this variety. They were, it could go maybe to a larger processor. And the big fear was, we don't have any seed. We don't have any production. If they ask us for four or five trucks of this stuff, I don't think we had, what do you have, Steve? 50 pounds, we hardly any. So is that chicken in the egg? Do you have the seed to do the processing? So those are things that we're struggling with on all levels of plant breeding. Not only at the university, but the small breeders as well. And so those are some of the things we're doing on the plant breeding side working. At the department, Burton talked about the land expansion we had on campus. That's just shocks people that we have organic land on campus. Like really? And I'm gonna give Greta a lot of credit. Greta Grammick, she'll be talking a little bit on the weeds that is the biggest problem. And she wanted to expand it organic. We had no certified organic land on campus. So we put her out at Absaraca about 30 miles from campus on the Hort farm. And that's the only place we could do it at the time. And now we've expanded on campus. And there's four research sites across the state now, where we can do organic research here, Dickinson, Absaraca, and Fargo. So I'll finish up there. Are there any questions? You mentioned licensing. So do they have to pay a licensing fee to the university? So anytime we develop any varieties at the university, they're provided to the research foundation. It has NDSU in its name, but it's not part of NDSU. And they do the licensing agreements. On the specialty releases, any of these small releases would be specialty releases because they would die without anyone there supporting them. And so that's just, it's a negotiation that takes place between the research foundation and the companies. So it could be anything from a royalty fee on the seed, or possibly maybe the royalty is on the amount of flour produced or the amount of malt produced. So they take different shapes depending on the crop. The thing we, you know, you would love to see free seed, but we need to make our breeding programs more sustainable because they're disappearing. And our farm breeders, they have costs as well, and we want to make and be able to support those programs. But organic seed is usually significantly cheaper than conventional seed? It is, if you can find it. That's the challenge. Steve and I were having a discussion yesterday. The challenge is finding foundation seed of organic. And so the far... I think what she's referred to mostly is the corn and soybeans. Exactly, yeah. Corn and soybeans are cheaper because of the traits. Yeah, oh yeah. Yeah, that's the... We work in no traded varieties, no GM varieties. So that makes it much, much cheaper. Yeah. As farmers, we've kind of gotten used to, especially at these types of crops, cheap seed. And yet we turn around and spend $100 an acre for corn seed, soybean seed, canola seed, whatever, and don't blink an eyeball. And that's always kind of been a frustration. It's like, I don't mind the licensing fee. I think it needs to be there. We should be paying... We should be helping pay for our own research. Yeah, and if you know that, that licensing fee is going back to support the research that developed those varieties, I think that makes it a little bit easier to understand. So that's definitely... That's why we're looking at, on the breeding programs, these opportunities for these varieties that would have been discarded, looking for opportunities to market those. I want to introduce one person, John McVoy. He's here from Microbiological Sciences. He talked about the agribiome projects at NDSU. That's another expansion of an area into organic. And he's got a graduate student and a technician here from the program. There's a couple of them. There's a technician that we got. He came in Sakshi here from Dr. Banerjee's lab. Sam Banerjee does agribiome research. He looks at the soil microbiome. We've also got Barney Geddes, who's looking at soil microbe interactions. He looks at soybeans and rhizobia as one of these key areas he looks at. Both of those faculty are doing research out of the research extension centers and collaborating with people out here and looking at the sustainability. So that's one thing we can look at on the breeding side. How can you breed those varieties that work in different microbiomes? And so there's really some cool things you can do. So we're looking to expand to that area. Richard, I have a quick question for you. So you mentioned about this blend variety that had a blander taste when it was grown on the east coast a little bit. Is that right? Yeah, it was in Maryland. So why do you think that was or what? Flavor, I have no idea. One of my biggest fears a few years ago, and I went to a barley meeting, a Malster and Brewer meeting out east, and I got accused of breeding the flavor out of barley. That was kind of interesting. And I won't even go into that story. But yeah, it's one of the hardest things you can breed for is flavor, because it's so subjective. Is it genetics or is it terroir? You look at grapes. That's the beauty of the mystery of grapes, right? The different years, different flavors, different locations. If you're developing a malt barley for a large company, man, it's got to taste the same every day, every year. You don't have that freedom. So one of the things we're gonna look at wheat is to look at the tannic acid levels. That's what you do in grapes. Will that impact the bitter flavor in wheat? So it's things we haven't really thought about. We're gonna, we're all in on this. So yeah. Any other questions? So do you think it's soil related between Maryland and North Dakota, or is that climatic? We just don't have enough data yet. And that's why we wanna grow these wheats at more locations. So next year we're gonna, right this year, I said we're in Vermont, New York, New Jersey, and Fargo. And we wanna expand it here with Steve, maybe some other Farm Breeder Club members. We're gonna go into Arizona next year and we're gonna look at that question. Is it terroir or is it genetics? And even the way you mill the wheat, we found stone milled Glen was much stronger in flavor than roller milled Glen, the traditional milling method. So there's a lot of questions. Like I said, if you can do it in craft beer, why can't you do it in wheat? Why doesn't every grocery store have a stone mill? And give you fresh, you grind coffee in the supermarket, why can't you do that with flour? I mean the best high-end bakeries and the best high-end restaurants have the mills right there in the restaurants. So at NDSU we invested in a stone mill and so did the Northern Crops Institute. So we're gonna be looking at those kind of things. And the Northern Plains, they own a stone mill and it's at Nicole's in Fargo. So we wanna include her in that research project too. You know, does a state mill in the elevator have a stone mill? I don't know. So. But that's a big, a new and up and coming thing is craft milling. There's a company in Vermont, new American mills that's selling these four-foot mills and it's becoming quite the thing, especially out east. We need some here. So, okay. Anything else?  I don't know if they'll touch that one. I hope so. So. The Potential is a school lunch program. They want higher fiber. So, white wheat, we're looking at white wheat too. Yep, a little bit. Any other questions? Well, thank you. This is fun, you know, getting these kind of questions and thinking about these kind of activities and we're definitely excited about working in this area and expanding into that.


All right, everybody, we'll do a quick round of introductions and then we'll get into it. My name is Marie Norden. I am Fedora's Community Action and Impact Coordinator. But I've been involved in Fedora way before I was the F-cake working on Fedora badges, as Ben mentioned. And a part of the design team went to all the flocks and stuff like that. I've eventually made my way here supporting the lovely Fedora community. Anurabh, would you go ahead? Thank you, Marie. Hello, everybody. My name is Anurabh. I'm from Fedora Mindshare Elected Members. I'm also working with Fedora KVAC as well. I'm also a member over 10 years. I'm doing a lot of different contributions, various contributions around Fedora. And so, they're hopefully going to talk about how to join Fedora and contribute Fedora as well. Thank you. Cool. So, we are definitely just winging this, as you might have heard. One of our presenters couldn't make it today, which is very sad. Some airport plane, you know, all that stuff. So, I've done this presentation before but me and Anurabh haven't shared it. So, we're going to go back and forth a little bit, our best to make it as coordinated as possible. And Anurabh, if there's something I'm missing you want to add in, just feel free to interrupt and I'll kind of go the same. Sure, sure. I'll try to convey your sentences at the finishing. Cool. So, what are we talking about today? We're going to talk about who and what Fedora is, how Fedora is organized, how to join Fedora, how to start contributing, and a couple of tips on becoming a successful contributor. We hope that there's also questions that we can answer for people. So, don't be shy. Use the Q and A tab and we will get to those at the end. So, first we're going to talk about who and what is Fedora. So, we are a huge global community. We're from all over the world and we come together to develop tools, software, and materials to promote free and open source software and very specifically our operating system, Fedora. Here's a nice picture from the before times of all of us at Flock. I think this was Poland. Poland's correct. I have my side note. I was just behind in the picture to picture closely. We were together in that picture. So, I'll say something. Anaralp and I didn't meet until we went COVID times and we were like, we're at the same event. How did we not meet? And we are in the same, we are even together in that picture. It's quite possible to go to these conferences and not even meet everybody. All right. So, the vision, this is our vision statement and the Fedora council puts together the vision for Fedora. And what they have drafted is the Fedora project envisions a world where everyone benefits and free and open source software built by inclusive, welcoming, and open-minded communities. So, we truly try to walk that talk every day in the decisions that we get, we make as a community together and the work that we do together. And next is our mission. So, vision, big picture, missions like the very concrete, this is what we do. So, Fedora creates an innovative platform for hardware, clouds, and containers that enable software developers and community members to build tailored solutions for their users. Cool. So, do you want to talk about the four foundations? Anaralp? Of course. Our foundation is actually one of my oldest knowledge about Fedora as well because when you join Fedora, four things you see if you get into the website, when you try to explore what is Fedora, you will see four foundations, which is one of our four foundations. Because we are not just an operation system, we are not just a distribution, we are also a community. And community means people. And it's also about technologies and the futures we bring into the Fedora and we try to do also as first as possible as we can. And this four foundation is freedom, which is we are open source and open community and open minded. We discuss everything openly and freely in a friendly environment. And friends is about being a community doesn't mean we just have to work and do the work and do just coding or designing. No, no, no. It's also about making friends. And when I joined Fedora, I have many friends, like even Marie mentioned, we got this COVID situation now become a good friends as well. And we talk each other, then I joined MindShare thanks to her. So this is also one of the outcomes. And one of the good outcomes is just a tiny memory as well I would like to share. Some of the people from Europe also visited me in Turkey and I have shown them around. They are trusting me as a friend, as a further contributor to share our culture, to share our values as well. So it just, as I said, doesn't mean just a distribution. It's about being friends, being in the team and feeling you have another family. You have to be welcome and help you out every steps. And if we talk about futures, it's about always about bringing the latest and stable releases of every single Fedora releases as you may know. And when it comes up, we got a new GNOME desktop distribution. GNOME desktop has been landed. And we have new cool features from PDA. We have another cool features from the back scene and all kinds of other stuff. And we also bring so many new futures like pipe fire, valence, and all kinds of stuff is also about our future. And next addition to that, we are also trying to do it as a first distribution, trying to put it in stable and a good environment and be shared with the people, get their feedback and harness them and make it even better. That's why our foundation is very core of the Fedora and very important to everyone. You want to take this slide too? Yeah, yeah, of course. Well, when it comes to Fedora deliverables, I see many pins or alternative into the Fedora. Of course, when you get into the website, as you can see, Fedora Workstation on the main page, but we are not just the workstation, we are also a server IoT or always silver blue. And recently, you know, I also has been released PDA variant of silver blue. And we have also scenes and last one, thank you Neil, we have also clouds on that list as well. And Fedora has, if I remember correctly, approximately 39 different desktops and a lot of them also have become, really become a spince as well. And if you check the list in here, you see EDA, XFC, LXQT, MADE, Cinnamon and more. And some of them is just as a specific target in Fedora. For example, Neuro is about Neuro technology. For design suite is about designing and also drawing and all kinds of stuff. When it comes to gaming, you can choose games as well. And this is all kinds of different options that exist in Fedora. So you can, based on your selection and choices, you can make your desktop or server even better. Cool. So I just want to follow up on that and say, there's groups behind each one of these deliver, or these additions, the spins and labs. So if you're interested in a specific one, you can get involved with that, what we call special interest group to start contributing. And it could just be, you know, bugs with your reporting bugs, or it could be, hey, I would really like to see this feature because of XYZ reason, or you could suggest another piece of software to add to one of the spins or labs. I'm not sure which one that's appropriate for, but you can get involved and kind of help to shape what these deliverables are. Exactly. All right. So I'll take this one. So there are a ton of benefits to contributing to Fedora. Personally, I've grown so much from being a part of the Fedora community. It really opened my eyes to so many different things, but I'm from the US. I hadn't really had friends internationally before I got involved in Fedora. And that really just opened my mind to how the world works in a bigger way. Sometimes we just kind of live in our own little boxes. But being a part of Fedora, you connect with people, you learn new things, you expand your network professionally, or just friends-wise. It can give you a sense of fulfillment, giving back to your community. It's something you can put on your resume. You know, if you're trying to get ahead or just, you know, get that job that you really want, putting, putting contributions to open source communities can take you a very long way there.  I hope everyone's having fun at this release party. And I hope that, you know, working on Fedora stuff brings you joy. It has always brought me an immense amount of joy. So that's one of the reasons I'm also involved. And I was talking about this earlier in the wallpaper session. Part of my background is a designer. And I'll keep it short. In my experience in art school, it was like very competitive, right? But when I came into the open source design world, it was not that. It was like accepting and we're all doing this together and we're handing back and forth files, which if you know about competitive graphic design or the art world, that's not something that happens a lot. So you have a chance to collaborate and learn from people with all different experience levels. And of course, swag. People love swag. Yeah, people love swag. And there's no shame in coming and hanging out to get some swag because, hey, it just promotes Fedora even more. Cool. So we can move on to our next section here. How is Fedora organized? So I'll talk a little bit. And on a rope, if you want to add in, you can too. Yeah, sure, sure. You take the sound and I'll take some slides. Cool. So Fedora is huge. And this map here might seem a little overwhelming. But I promise that all of these people who are running these teams are accessible to you. So right in the middle there, you have the Fedora Council. The Fedora Council is making high level decisions for the project. And then coming off of that, you have Mindshare, Tesco, objectives, and the diversity and inclusion. So those are all very much connected to the council. And I guess you would say that they're the next level below the council, right? So Mindshare focuses on Fedora's promotion, marketing. Everything that's not coding pretty much goes onto the Mindshare side of Fedora. So you're going to see over there are commops, which is ambassadors, the joint SIG, advocates, marketing. Then you also have like mentor projects. You have our internationalization efforts. You have the podcast, the community blog, the magazine, all these different places. These are all places for non coders to get involved. And I'll say this, we need you so much. We need people who are coders, obviously, we're always looking for more people to do development and packaging and all that stuff. But not a lot of people like it's slowly, but surely coming to light that we need more than just those technical contributors. We need every part of an organization, a regular organization, we also need that Fedora. I'll also point out that diversity and inclusion team, we're actually rebranded to diversity, equity, and inclusion. So this doesn't need an update. And those groups are focused on a couple of different events that we do yearly. There's Fedora Week of Diversity, Fedora Women's Day, we just kicked off and did our first Fedora Mentor Summit. And they also support some of the mentor projects as well. And then I'm going to leave the objectives and fest go to you. Hey, well, objectives are basically is what are we going to do in next releases and years in Fedora. It's not just about technologies we would like to think about to bring it in. It's about also bringing about which contributors and other federal community objectives you would like to target of that as well. So it's also very important that these objectives are is basically is coming from federal contributors. And based based on collective idea, we would like to basically put it out there and get the feedback from the community. And based on that action, it also harness it and make it better. And also try to do it and complete the task. And it's also definitely feels good, because once we got an objective and completed, and when you become a federal contributor, it definitely feels good to do that objective as well. About fest go is for the engineering. Living committee is about is federal technical side of the war. So when it's come about federal release releases, changes is like if something's going to be definitely critical in Fedora, that's required for training committee statement. And it's also about packages that is if you're active or inactive on all kinds of technical side of the federal situations goes over there. And you can even run for federal engineering steering committee as well and become part of it. And if you want to have an agenda, have a better idea, you want to put it out there, put your words, or you can do it as well. Any federal contributor can become test member as well if they wanted to run for it, of course. Being a physical has a lot of responsibility. It's not just doing some coding, you have to also do a lot of work around people as well and decisions is also very important. It's all connected in main point in federal council and which is the all basically one giant community. Awesome. As I said, don't forget to put any questions in the Q&A. We're definitely open to them. So we talked a little bit about them on the last slide. We don't need to go too deep into this one, but it is good to just reiterate these are the governing bodies in Fedora, right? So if you're like, I think something that I think Fedora should be focused on this as a whole, you would go to the Fedora council and say, hey, I have this idea and I think it should be something that you keep in mind. Say you want to put something into a Fedora, like the next Fedora release, right? You would go to the Fedora engineering steering committee and change to them. And then say you might want to run an event or get them swag for your local Linux user group, right? You would go to the mindshare committee to ask for something like that. So, right, these groups are accessible to you and you are welcome to come to them and ask for things for Fedora. And I hope that people feel comfortable doing that. Also, just one side point I would like to add is if any committee you ask if your question could be on top topic or something unrelated, don't afraid. Just got any part of the member will try to point you the right direction as well. Every question doesn't have to be true. We understand you. It's a big community. You have to understand a lot of stuff. We won't judge you. We was trying to point to the right direction. Don't be afraid. We'll always be here for you. Yes, absolutely. Cool. So how do I join the Fedora community? So we have this awesome team called Fedora Join and they are there just to greet newcomers. So they're just hanging out on a channel hoping and waiting for new contributors or interested people to show up. And they're going to help you do a couple of things, right? They're going to help you get your Fedora account. They're going to help you like introduce you to documentation in your areas of interest. They're going to potentially help you find a mentor to, you know, someone kind of directly to ask questions to because it can be a little overwhelming asking questions and just like a public channel. So sometimes it's nice to have a mentor. And then just a channel that's just for newbies. So that's like the ask any question you need support with channel. There's no wrong questions anywhere like on or off the thing. But this is the place specifically for newcomers to ask those beginner questions that all of us have. So next. Okay. So we're looking for potential not polished. You don't have to come with a lot of skills to Fedora. Like you can just say, Hey, this this community inspires me. The foundations behind this community inspires me someone I met inspires me to be here. And you don't have to come with like, you know, I can code in these 10 languages. And I, you know, I'm familiar with all this software for design or editing or whatever, like you can just come as you are. And, and we can help you find a place. So if you think this sounds great, what are the next steps? Alright, so the first thing you want to do is make a Fedora account. I bet a lot of people here already have Fedora accounts. But I hope that for the badges. I hope that people see this on YouTube later too. So first step is to make a Fedora account. You can do that at you can just search Fedora accounts. And it should pop right up for you. And that's used to, you know, you can log into all the Fedora sites with that Fedora account, you know, discussion and element now chat, pejor the wiki. So that's, that's your key to get into all of these Fedora platforms. And after you have a Fedora account, you can go speak with the joint team. And the joint dig will open a ticket for you to basically kind of have your introduction, right? So you can ask questions on that ticket. They're going to give you links to documentation. You know, that's kind of a great place to just have a starting base, right? And after a little bit, they will actually check in with you and say, how's it going? Is there anything we can help you with? So yep, that's kind of what I was just saying. I didn't realize this was coming up next. But yes, we do the introduction. We help you do the exploration. We check in, see how it's going. If you need some more support or information, happy to give that to you. So from there, what happens? So there's all kinds of roles in Fedora. So this is broken out into coding roles and non-coding roles. Arnold, do you want to take the coding roles? Go for it. Of course, the coding roles is about for the, for example, we have further packaging. Packager is basically bringing the source application of Fedora. Sorry. We'd like to say you have found an application that doesn't exist in Fedora and you would like to bring it in into the Fedora. What you can do is you can make an RPM package in Fedora in certain roles and you can basically package it for us and you can submit a review. And one of the experienced packages is going to be reviewed for you. Once your review is complete, you have to also find the sponsor. Once you've got the sponsor, you can ask in around the development list. Another list, basically, you can bring it in Fedora. I think we need a brief description just because we won't get, there's like 16 of these. So go ahead. Go ahead, Arnold. Okay, I will handle that. Once you complete your all kind of steps, you will basically have a package in Fedora. You have to maintain it. So let me encrypt some pieces. What is in packages? Packagers are basically a person who's responsible of the package you bring in in the Fedora. And the proven packages is basically an experience package. It has a more power for all the Fedora packages. And submit, sorry, reviews is basically you have to go to the certain websites in Fedora, in Vaxilla, and you have to open it in certain format to submit your package. There are a lot of details about it. If you're going to cover them all, it's going to be an entire talk. So I will not go into much of a detail. This is just a brief explanation of the packaging. And the list engineering is basically about is how to maintain different tools, the utilities, also checking packages stability and upstream situations and all kinds of technical technical topics. Quality assurance is basically one of our key points in Fedora. When we do every single releases, we also have to test every federal releases. And also we have to check, we have also certain packages, has to work properly, and has to be a stable state. If we don't stabilize it, we are basically delaying releases. But in the same time, we're trying to fix that problem, if possible, internally, not internally in Fedora, or we go to the upstream, try to fix it. So once we complete this certain box and problems, Fedora will be released. And as you, some people may notice that this release is slightly delayed because we have a certain box, we have to fix it before presented to people because we want have the best and clean experience in Fedora so everyone can basically install it without any problem or upgrade without any problem. Marie, do you want me to take this one as well? Sure, but I give a little bit less background. We've got to get, we're, we have six minutes left. Sure. Fedora infrastructure is basically about Fedora servers. If you want to helping on that, you can go there. If you want to make our websites maintain, and in front-end or back-end, we have website and app development, if you want to translate application or certain Fedora tools or beyond that, you can join Fedora internalization team and help about it to translate the language is basically. Cool. So I'll do that. Yep, absolutely. So we're talking about internationalization, which works with bloodlates. We also have translation. So that's just, you know, folks going to our documentation and translating it into their, into a language that they know. So it's less to do with the operating system, more to do with our documentation and all of the stuff that surrounds the community. We also have community outreach and there's multiple teams that work on that. But off the top of my head, ambassadors is the number one outreach team. And then we have advocates who run small events, and we have the community operations team, which kind of supports all of those. We have the diversity, equity and inclusion team. As I mentioned, they work a lot on events, but we also try to help influence how Fedora is basically moving. And we want to make sure that accessibility and inclusion are something that we're always keeping in mind. We also have very strong graphic design team. And we also have more and more folks showing up who are interested in doing UI, UX work, that's front-end development websites. We have a podcast team. We have the Fedora magazine. So if you're into writing, we have the magazine. And then we also have the community blog. So you they're kind of like user focused is the magazine. And then community focused is the comp blog. We have a documentation team. That's more of the technical writing and updating documentation. We have a program management team run by our lovely program manager, Ben Cotton. And they come and help assist with projects or initiatives that you might be working on. We have a great mentorship program. We're actually looking for more other programs to be involved in. But in the past, we've done GSOC and we've done outreach for, I don't know, like 20 sessions in a row. We really do well with outreach programs. So, right. So those are a general overview of the non-coding and coding roles. But if you saw something you're interested in that wasn't there, those are just the overviews. Those are like the most common ones. So if you have something you want to do in Fedora, don't be shy, ask where those people are doing that. All right. So how do I become a successful contributor? So we all started as volunteers. We all started as newbies at one point, whether it was 20 years ago, like some of us or six months ago or two months ago. And so all it takes is practice and observation, right?  You know, something that makes a community great is the trust that we build with each other. And part of that trust is saying, hey, I'm going to do this thing and then doing it, right? So there is some of a somewhat of a time commitment there. Also, observation. How do the team, how do the teams work? What's the team communication style? And if you ever run into a problem, there is a code of conduct, just want to point that out. So you're seeing things that you don't think should be happening. There is a way to report that. But hopefully everything observed will be good. So but it's good to know how a team works, how they communicate together. And also, you want to pick something that's important to you, right? So something that's going to excite you, something that's going to bring you joy. You want to pick something that's going to make you happy and motivated, right? So don't try to jump into the deep end by picking up like a huge task, right? That's not, that's kind of a way to ask, to sign up for failure. Try something small to start, right? And then from there, you can kind of build and grow upon that. You want to potentially look for a mentor or a sponsor. Someone you can ask questions to in a like a very non-mental or at least a more private venue where you can ask these questions and then try to stay busy, you know, hang out, come to our events, hang out in the chat channels and stay involved. So, right. Oh yeah, go for it. Go for it. Also, keep it in mind a couple of stuff.  Don't be afraid from making a mistake. Everybody can make a mistake. Experience contributors will definitely help you out. And don't be afraid. Say your mistake if you do something wrong or slightly think it could be wrong or of course you can do it through correct as well. That's, that's of course also awesome. But that doesn't mean we're going to make you feel forced or not good. As Dan said, experience is also about experience. Making mistakes also gives you more experience and more talents based on the knowledge you want to learn about it. Ben's coming on screen to tell us we're almost at a time. But since the next one's in AMA with a little bit longer. But I will say I just reiterate on our own point, don't let yourself be burnt out. There's nothing in Fedora that is more important than your health, both mentally and physically. Also, if you don't have a time, if you think something doesn't going to finish at the time you think, try to stay it. Try to pass on your team contributor because we will just basically doing an all free time, doing on our time. It doesn't mean you have to finish it. I understand people have a life as well. Don't afraid of it. So right, we pointed out the code of conduct already, but because we're from all over the world, we have all different cultures, backgrounds, beliefs, and beliefs about free software as well. You know, sometimes conflicts happen. It's inevitable and it comes down to each of us to make sure that we keep our community happy and healthy. So how do we do this? Be considerate and be respectful. You know, you don't know what kind of day someone's having. You don't know how much work someone put into something. You don't know what's going on in somebody's life behind the scenes. So just try to approach with consideration and respect for your fellow contributors. And thank you so much for listening. I know we're just slightly over time and I don't see any Q&A. So if there's none, we could wrap it up. We did the best talk done. We can absolutely share links to the slides. I think we're going to add them into the wiki. This does need to, so apparently we need to do some updates here. Oh, Fredo's not here. It's on Libra chat. We shared everything properly. All right. I hope everyone had learned a lot or was just like nostalgic the whole time because you've been a part of the door for so long. And thanks again for listening.


Originally, when I was in high school, I had committed to play soccer and attend the United States Military Academy at West Point, and I attended there after graduating high school. However, once I decided I wanted to take a different path from the army, I wanted to go in to try to become a professional soccer player. I transferred back home to Florida International University here in Miami. And living at home, I wanted to have some type of way to make money for myself and stuff like that. So I looked into doing personal training for soccer and I had one of my good friends that we ended up starting it together and working together. And mainly, it was just seen as something to do on the side. While I was playing soccer at FIU at the Division I level. And that's how it started. Perfect. So tell us a bit about your business then. What does your company specialise in? So as I said before, I had started with my best friend at the time and we grew a company together called Prodigy Soccer Training. And I worked there for about three years. And then towards the end, we had a tech kind of a falling out. So I decided to go on my own and do my own thing. And that's where I created a Premier Soccer Academy. And my business specialises in one-on-one group training for soccer. Very technical-based training with game-like experiences and a lot of position-based training in order to improve the player on and off the field. Not only make them a better player, but also make them a better person and try to improve all aspects of who they are as a player and as a person. Love that. So you that have been coaching and training for a while now, what does a high-quality session look like for you then? What should it include? Well, for me, I think to high-quality session always starts with a good warm-up, some type of just active stretching, active warm-up, to then go into some type of agility, speed and agility, some type of drill like that. And then a lot of 20 to 30 minutes of technical work, focused on first touch, passing, dribbling, small spaces, big spaces, ball mastery. And then towards the end, bring some of the stuff that you did in that technical training into something that's position-based. If a player is a right back, how can you work on their first touch going forward to play down the line? Or if they're centre-mid, can you work on having them turn and play into a space? For me, that's my high-level training. Love that. So with your current clients, what type of clients do you work with? Are they normally beginners? Are they intermediate or are they players that are striving to play at a higher level? I think for the most part, I have players that are striving to play at a higher level. I think I have mainly intermediate to those players striving to be at a higher level. Even at a very young age, I have kids that are eight, nine years old, but that already have that mindset of wanting to go pro, wanting to go try out in different places. But I also have older players that are 14, 15 that want to just be better on their team to then possibly go play at a college level or something like that. But for the most part, I don't only choose them, but I've been thankful and having the clients tell to be always at a good high level in the sessions. What's the number one thing you look for when you bring on a new client into your business? I think the hunger to improve the commitment and the discipline. I think those are the three main things I look for in a client because I rather much rather have a player that's a beginner to intermediate that's willing to put in the work and willing to get better and willing to commit to the process rather than a high level player that thinks he knows it all and just there because his parents want him to be or something like that. Love that, love that. So let me take you back to when you first started a Premier Soccer Academy. What was your biggest obstacle when you started and do you currently have obstacles today? So my biggest obstacle was definitely managing my time between being a Division 1 athlete and running the business. I think that trying to find the time and the energy to do it after being in training, lifting weights, going to class and then having to go and put in a high quality session was one of my main obstacles. And then I started right after COVID hit. So that was another obstacle for me, having to deal with COVID and having to deal with all the regulations at the time easing into normalcy and then having to maintain all the CDC guidelines and stuff like that while trying to be a coach, having a one-on-one session was quite difficult. So how do you manage playing and managing a business? So at the beginning it was very difficult because it's not just being a coach. It's a big part, but it's not the only part. I've realized over the last few months that being very active on social media is very important. Being active with the parents, communicating with them, going to see the player play, dedicating that undivided attention to them is very important, which I didn't have before. And now that I do, I've seen a very big difference in my amount of clientele and then the commitment of the clientele. Awesome. So how many clients are you currently working with at the moment? I think about 20 to 30 clients per week. That's awesome. Excellent. Congrats. I hope that continues to grow as well. Yes, I hope so as well. Awesome. So Andrei, where do you see private training going in the US in the next two to five years then? I think it's only going to get bigger, get more people trying to get into this business. It's a very high risk, high reward. If you're willing to put in the effort into yourself and you're willing to believe in yourself and put in the work, and I think it's very rewarding. I think people are starting to see that more and more and are starting to get that. So I think it's only going to increase, especially in those high populated areas, high soccer areas in the US. Love that. So for any coach watching this interview or listening to it, what would you say to them if they want to start a business, but they're either hesitant or they have a fear of starting, what would be your number one advice for them? I think the number one advice to me for myself when I was doing this, I had just moved into a new place. I just had a one-year-old son. So I had a lot of things, a lot of people depending on me. And for me, this is something I love. And if it's something that the other coaches love and they've devoted their almost their entire lives to the sport that they have been doing, I think, what do you have to lose in betting on yourself? It's something that you've loved since you were a kid, possibly. And in my situation, I've been playing soccer since I was four. And I never imagined myself doing anything else.  Love that. Love that. So what skills that you've developed in coaching, have you taken into business then? I think definitely being a leader, I brought in a few other coaches to work with me. Learning how to lead these kids and how to manage them is helping me manage the coaches I work with. And also just the discipline of keeping up with planning sessions, reflecting on my sessions, and trying to become a better coach every day is the same thing I do with business. I graduated an international business at Florida International University, and that's helped me out as well, you know, having that background and going into my own business. Love that. So how many coaches do you have working for you then at the moment? I have two other coaches. One is with works with me in this location in Miami, and then I have another coach in Fort Lauderdale. Oh, awesome. And how have you found managing other coaches then? I think it's been easy for me at least, because they're my friends. One of them was my teammate at FIU, and then one of them was an old teammate of mine when I played in club soccer when I was younger. So they're not strangers to me. At the end of the day, you have to keep a business relationship with them. You have to put the friendship aside for certain things, but if for other things, it's easier to talk to them. It's easier for them to understand where they're coming from because they know you, they know me at least on a personal level. Love that, love that. So let me take you back to the beginning when you first started. How did you get your first client? So my first client, my very first client when I got back from West Point, was a friend of mine, his little brother was playing soccer. And he just told me, like, look, you want to, you know, I know you're trying to start training. You want to train my brother? And I was like, Yeah, sure. He just, yeah, he offered me 20 bucks to train him. And I was like, Yeah, I'll train him. And then he started liking it a lot and he started to improve. And then I started to realize that I liked it as well. And then from then on, it was just, you know, just trying to look for more clients and trying to build my name. Love that. And what would you say is the number one thing kids today need? I think depending on the age level and, you know, their knowledge of the game and stuff like that, most of all, they need, you know, open-minded number, open-minded coach, a motivational coach. And then someone that understands what it's like to be in their shoes. I think that's very, you know, have that empathy for them.  And being going to school, having friends, you know, realizing and also realizing that soccer isn't everything. I think trying to teach them that is very big. Yeah, I try and teach myself that every day, but I struggle. Trust me, I do, I do too. There's a part of me that understands it. And there's another part of me that doesn't understand it at all. Yeah, exactly. 100%. Perfect. So let me ask you a bit about your sales and marketing process then. So how do you sell and market your business then? It's come to a point where a lot of my references lately have been, you know, word of mouth. You know, the kids I've been training from before talk to the other people and recommend me, which has been very good. That's kind of what you want to look for as a personal trainer. But also, I've been very active on social media.  You know, I try to keep always a steady price. And I think my prices have been very fair and manageable. You know, going with the society that we live in, especially here in Miami, that everything's only getting more expensive. But I'm trying to, you know, market myself, posting my sessions on social media.  Love that. Love that. So something we teach our coaches in our program is how to add value away from the training session. So what's a couple of things that you do in your business to add value to your clients away from training? So apart from training, I do also, for example, game film analysis. I, you know, I'll have a kid send me, you know, his game film and try to analyze it for him, see what he could do better, see what he did well. I'll go to games to watch.  Just have a nice conversation. As the post school went, you know, what do they like to do outside of soccer? Just have them not just feel like, you know, their only value is as a player, but teach them that their value is as a person as well. I love that. So now you mentioned communication. How important is it to have good communication with parents? Oh, it's vital. I think it's the most important thing with your, with, if you want to keep those clients, you know, if you want to really keep those clients and see the clients stay for a long time, I think the number one thing is to have a good relationship with the parents, especially for players that are, you know, high, like, you know, about eighth grade and below where the parents are still, but like, you know, they have a very big relationship. Like I have certain kids that, you know, I will pick them up and take them to practice because they're already, you know, in high school, they're homeschooled, you know, and they're already more independent. So the relationship is more important with them. But I have eight or nine year olds that, you know, I have a very good relationship with their parents and, you know, the kids, the parents are always going to want, you know, talk to other parents about me. And that's where, that's where it gets even better. Perfect. Love that. Love that. So where, where do you see your, your business in the next five years from now then? Five years from now. God willing, I'll have my own facility, my own indoor facility because Miami is too hot. But yeah, it's, I hope to have my own indoor facility where I can have, you know, training sessions day and night. And then to also have, you know, my own leagues, you know, like a six to six, five to five leagues being played at my facility as well.  Yeah, love that. And we've, we've a lot of air conditioning, right? Yes. Especially in Miami. Yes. That's awesome. Love that. So two, two last questions for you. These are more of a personal ones and these are ones that I like to ask. So the first one is what does failure mean to you? And the second one, how important is risk in business? So failure to me means that you're not learning from your mistakes because I don't, I don't see failure as, you know, you're making a mistake. I think it's a failure when you don't learn from your mistake. I feel that, you know, it's normal. It's not, no one's perfect. And, you know, you're not, you're, everyone's going to make mistakes. But being a failure, you know, that, that connotation to it, I feel like is something that you're not learning from your mistakes and you continuing to make mistakes. And utterly you think you're over yourself as a failure. For the second, I think it's, it's, you know, it all depends on how much you want to grow. If you want to grow, then there's going to be a lot of risk because no scared money doesn't make money. And if you, if you're not willing, if you're not willing to put in the risk, if you're not willing to put better on yourself and go all the way, then you're, you're going to plateau and you're going to stay at a certain level. If you want to get to the highest level, then you're going to have to work in the risk. Love that. Love those two answers. Perfect. All right, Andre, it's been an absolute pleasure. You've got a really fantastic story. Now, if any coach watching this or listening wants to follow your business or wants to get in contact with you, what would be the best way to do so? You can follow my Instagram page, Premier Soccer Academy. And I'll show my email is deco917 at gmail.com. No, if you have any questions that they have or want to get in contact with me, those are the best two. Awesome. Fantastic. All right, Andre, I wish you the very best in the future with your business. I hope your program grows and I hope you get your indoor facility with lots of air conditioning. Thank you very much. I really appreciate it. All right. Take care. We'll connect soon. Thank you. Have a great day.


Excellencies dear friends, today we mark a milestone in the history of human rights. It takes place at a time of crisis and rising conflicts, war and division. In Ukraine, in Yemen, in Israel and Gaza, in the Sahel region. The world of today sadly does not reflect the dream we had 75 years ago. In times like these, it is important to remember our shared vision from the past. And even more important, our shared wishes for a better future. One of the visionary people who drafted the Declaration on Human Rights 75 years ago were actually a dame. She left a solid mark, insisting that the declaration should not only refer to men but to everyone. This strong focus on women's rights has been a key priority for Denmark ever since. Today, I am proud to announce the pledges of Denmark.  Secondly, we will launch a new national action plan against racism and hate crimes next year. The current rise in anti-Semitism underlines our obligation to fight against racism and discrimination in whatever form it may take. Thirdly, human rights will remain the foundation of our development corporation. Fourth, we will remain committed to safeguard equal rights and opportunities for all regardless of their sexual orientation or gender identity. And finally, it is a key priority for Denmark to make tech work for human rights, not against them. Earlier this year, we launched the Digital Democracy Initiative to help civil society promote democracy and human rights in today's digital age. Human rights can never be taken for granted. And we need to work hard every day to keep them alive. Thank you.


Wireless charging technology is becoming increasingly popular as it offers a convenient way to charge electric vehicles without the need for cables or plugs. However, there are several challenges associated with its implementation, such as coil misalignment, which reduces the efficiency of the system. To address these issues, this paper reviews the existing literature on wireless charging systems and proposes solutions to optimize the charging process. This article was authored by Benito Albert Ryan, Umashankar Subramaniam, and S. Balam, Oregon.


Fy rhaid i'r awrsus.... y mae'n gweithiau ar gyfer hynny ac mae'n gweithio'r awrsusur yma yn ystod, yn ystod Llefwyr Ddorog Geebi, sydd eisiau ei hwyl i'r minister i'r Prifysgwr FarmhMR, sy'n meddwl i'r qurtaf. Rwy'n credu i'r peryfridd. Cymdeithas digwydd. Mae myfyrdd yn hynny i unrhyw o'r dweud drwy'r panffordd i ddwy i'r adnod. My wife and I can certainly testify to that, as it's only been a year since we moved into the man's that we currently reside in. And in a week's time, we move again into our church's new mines. However, anxiety and stress comes in many forms. None more so than the anxiety and stress that has been felt by thousands of people throughout Scotland today. In every town, city and village in Scotland, anxiety and stress has now become an everyday condition for thousands of Scots because, well, because there are so many things in society these days for people to stress about. Fuel costs, food prices, the price of clothes and so much more. Life is hard and becoming harder and not just for those who are unemployed or on lower incomes. Everyone is feeling it. Food banks are reporting that middle income families are now regularly using food banks to help them get through the week. In St Paul's letter to the Philippines, Christians are given these words to live by. Do not be anxious about anything but in every situation by prayer and petition, with thanksgiving present your request to God and the peace of God which transcends all understanding will guard your hearts and your minds in Christ Jesus. Those are words of comfort and I pray that they help Christians cope. However, would it not be ideal if we could get to a point where our anxiety was diminished enough that, as a nation, we could live wisely and thoughtfully no matter the decisions we face, whatever the challenges before us as peoples of this nation. Strong, wise and compassionate leadership at times such as we are all experiencing right now is essential. The leadership displayed by Jesus embodies the forms of leadership that are needed in difficult times. Servant leadership, a leadership of acceptance, leadership that begins in the heart and then emanates to every corner of society, leadership that calms the anxious heart and relieves the stressed mind, that welcomes the unloved, that closes the naked and feeds the hungry. These are the qualities that all nations and communities look to their leaders for. Thank you.