what does that mean "vector augmented to 1"?
<p>I am new to machine learning and statistics (well, I've been learning math in my university but that was about 10-12 years ago)
Could you please explain the meaning of following sentence from 4 page (in a book 5 page) from book here ( <a href="https://www.researchgate.net/publication/227612766_An_Empirical_Comparison_of_Machine_Learning_Models_for_Time_Series_Forecasting" rel="nofollow noreferrer">https://www.researchgate.net/publication/227612766_An_Empirical_Comparison_of_Machine_Learning_Models_for_Time_Series_Forecasting</a> ):</p>

<blockquote>
  <p>The multilayer perceptron (often simply called neural network) is perhaps the most
  popular network architecture in use today both for classification and regression (Bishop
  [5]). The MLP is given as follows:
  N
   H
  y ˆ = v0 +
   j=1
   X
   vj g(wj T x′ )
  (1)
  <strong>where x′ is the input vector x, augmented with 1, i.e. x′ = (1, xT )T</strong> , wj is the weight
  vector for j th hidden node, v0 , v1 , . . . , vN H are the weights for the output node, and y ˆ is
  the network output. The function g represents the hidden node output, and it is given
  in terms of a squashing function, for example (and that is what we used) the logistic
  function: g(u) = 1/(1 + exp(−u)). A related model in the econometrics literature is</p>
</blockquote>

<p>For instance, we have a vector x = [0.2, 0.3, 0.4, 0.5]
How do I transform it to get a x′ vector augmented to 1
x′ = (1, x) </p>