Deep Learning From Scratch I: Computational Graphs - deep ideas

This is part 1 of a series of tutorials, in which we develop the mathematical and algorithmic underpinnings of deep neural networks from scratch and implement our own neural network library in Python, mimicing the TensorFlow API. I do not assume that you have any preknowledge about machine learning or neural networks. However, you should have some preknowledge of calculus, linear algebra, fundamental algorithms and probability theory on an undergraduate level. If you get stuck at some point, please leave a comment. By the end of this text, you will have a deep understanding of the math behind neural networks and how deep learning libraries work under the hood.