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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 MNIST Dataset
Also known as digits if you're familiar with sklearn
Step1:  Basic data analysis on the dataset
Step2:  Display Images
Let's now display some of the images and see how they look
We will be using matplotlib library for displaying the image
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Python Code:
import numpy as np
import keras
from keras.datasets import mnist
# Load the datasets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
Explanation: MNIST Dataset
Also known as digits if you're familiar with sklearn:
```python
from sklearn.datasets import digits
```
Problem Definition
Recognize handwritten digits
Data
The MNIST database (link) has a database of handwritten digits. 
The training set has $60,000$ samples. 
The test set has $10,000$ samples.
The digits are size-normalized and centered in a fixed-size image. 
The data page has description on how the data was collected. It also has reports the benchmark of various algorithms on the test dataset. 
Load the data
The data is available in the repo's data folder. Let's load that using the keras library. 
For now, let's load the data and see how it looks.
End of explanation
# What is the type of X_train?
# What is the type of y_train?
# Find number of observations in training data
# Find number of observations in test data
# Display first 2 records of X_train
# Display the first 10 records of y_train
# Find the number of observations for each digit in the y_train dataset 
# Find the number of observations for each digit in the y_test dataset 
# What is the dimension of X_train?. What does that mean?
Explanation: Basic data analysis on the dataset
End of explanation
from matplotlib import pyplot
import matplotlib as mpl
%matplotlib inline
# Displaying the first training data
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
imgplot = ax.imshow(X_train[0], cmap=mpl.cm.Greys)
imgplot.set_interpolation('nearest')
ax.xaxis.set_ticks_position('top')
ax.yaxis.set_ticks_position('left')
pyplot.show()
# Let's now display the 11th record
Explanation: Display Images
Let's now display some of the images and see how they look
We will be using matplotlib library for displaying the image
End of explanation
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