We started with *Neural Networks and Deep Learning* in the hope that after
reading this short textbook, our team members would have a similar foundation
of the most basic concepts behind deep learning and neural networks. The first
chapter explains neurons, neural networks, and the process of adjusting weights
and biases to find some optimal solution through *gradient descent*. It talks
about the sigmoid function and why it’s a good choice for an activation function.
Finally, it contains some code that shows how to write a neural network from
scratch using just Python and vanilla numpy.