In this series of posts, we will learn about Deep Learning and neural networks, starting from the basics.
Here is a nice visualisation of what’s happening in an ANN during training.
Part 1: The Simplest Neural Network
In this section, we will describe the building block of ANNs from the theory.
Part 2 – Adding a Hidden Layer
We expand the concepts from part 1 and describe the backpropagation algorithm.
Part 3 – The Single-layer Perceptron
We implement our first neural network in pure Numpy.
Part 4 – The Multi-layer Perceptron
We add a hidden layer to our neural network.
Part 5 – Multi-class Classification (coming soon)
Let’s see how to deal with multi-class classification problems with ANNs
Part 6 – Deep Learning Libraries (coming soon)
We implement our neural network using some popular deep learning libraries such as Pytorch and Tensorflow / Keras.
Part 7 – Install TensorFlow 2.0
Installing TensorFlow 2.0 can be troublesome. We will show a method to install it on Ubuntu 18.04 with GPU support using Docker.