title: how to adjust the weights in gradient descent
body: <p>I am currently trying to teach me something about neural networks. So I bought myself this book called Applied Artificial Intelligence written by Wolfgang Beer and I am now stuck at understanding a part of his code. Actually I understand the code I just do not understand one mathematical step behind it...
  The part looks like this:</p>
  
  <pre><code>    for i in range(iterations):
          guessed = sig(inputs*weights)
          error = output - guessed
          adjustment = error*sig_d(outpus)
          #Why is there no learningrate?
          #Why is the adjustment relative to the error
          #muliplied by the derivative of your main function?
          weights += adjustment
  </code></pre>
  
  <p>I tried to look up how the gradient descent method works, but I never got the part with ajusting the weights. How does the math behind it work and why do you use the derivative for it?
  Alo when I started to look in the internet for other solutions I always saw them using a learning rate. I understand the consept of it but why is this method not used in this book? It would realy help me if someone could awnser me these questions...</p>
  
  <p>And thanks for all these rapid responses in the past.</p>