Many people are using TensorFlow and Keras to build cool Deep Learning-based applications, but few understand what is really going on, and even fewer understand the math behind why this process works. In this workshop, we will build Deep Neural Nets from Scratch using Python, illustrate that these nets can solve complex problems as we'd expect, and cover the math that explains why this works.
New applications of neural nets are constantly being conceived and built using libraries like TensorFlow and Keras. However, few people building these applications understand how neural nets work under the hood, and even fewer understand the math that explains why they work. Many tutorials out there explain some of the details here, but none both explain the math and connect the math to concrete code. In this tutorial, we'll work carefully through how to build Deep Neural Nets from Scratch using Python.
This tutorial will be split into several parts:
The second half of the workshop will be transitioning from using a function-based method of building simple neural nets to using a class-based method of building Deep Neural Networks. This section will involve: