Exercises from Prof. Andrew Ng's Neural Networks and Deep Learning course on Coursera.
This course gives an excellent understanding of the theory and fundamentals behind neural networks, including implementation of logistic regression, and deep neural networks from scratch. This gave me great insights into all the steps involved in building a neural network - initialization of weights and biases, understanding of the shapes of W,A,Z and b matrices in each layer, forward propagation (linear and activation function), cost function computation, back propagation, parameter optimization with gradient descent and using trained parameters to predict labels