The basics of Deep Neural Networks

With the rise of libraries such as Tensorflow 2.0, PyTorch and Fastai, implementing deep learning has become accessible to so many more people and it helps to understand the fundamentals behind deep neural networks. Hopefully this article will be of help people to people on the path of learning about deep neural networks. Back when I first learnt about neural nets and implemented my first, they were always represented as individual artificial neurons, essentially nodes with individually weighted inputs, a summed output and an activation function. When first returning into learning about deep neural networks, the concept of how this equated to matrix multiplication didn't appear obvious. Also, linked to this is why Graphics Processing Units (GPUs) and their spin-offs have helped advance deep learning results so much.