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.