Artificial Neural Networks

In this series of posts, we will learn how Artificial Neural Networks (ANN) work, 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.

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