Artificial Neural Network Fundamentals

An Artificial Neural Network (ANN) is a supervised learning algorithm that is loosely inspired by the way the human brain functions. Similar to the way neurons are connected and activated in the human brain, a neural network takes input and passes it through a function, resulting in certain subsequent neurons getting activated, and consequently, producing the output.

There are several standard ANN architectures. The universal approximation theorem says that we can always find a large enough neural network architecture with the right set of weights that can exactly predict any output for any given input. This means that for a given dataset/task, we can create an architecture and keep adjusting its weights until the ANN predicts what we want it to predict. Adjusting the weights until the ANN learns a given task is called training the neural network. The ability to train on large datasets and customized architectures is how ANNs have gained prominence in solving various relevant tasks.

One of the prominent tasks in computer vision is to recognize the class of the object present in an image. ImageNet (https://www.image-net.org/challenges/LSVRC/index.php) was a competition held to identify the class of objects present in an image. The reduction in classification error rate over the years is as follows:

Figure 1.1: Classification error rate in ImageNet competition (source: https://www.researchgate.net/publication/331789962_Basics_of_Supervised_Deep_Learning)

The year 2012 was when a neural network (AlexNet) won the ImageNet competition. As you can see from the preceding chart, there was a considerable reduction in errors from the year 2011 to the year 2012 by leveraging neural networks. Since then, with more deep and complex neural networks, the classification error kept reducing and has surpassed human-level performance.

Not only did neural networks reach a human-level performance in image classification (and related tasks like object detection and segmentation) but they have enabled a completely new set of use cases. Generative AI (GenAI) leverages neural networks to generate content in multiple ways:

• Generating images from input text
• Generating novel custom images from input images and text
• Leveraging content from multiple input modalities (image, text, and audio) to generate new content
• Generating video from text/image input

This gives a solid motivation for us to learn and implement neural networks for our custom tasks, where applicable.

In this chapter, we will create a very simple architecture on a simple dataset and mainly focus on how the various building blocks (feedforward, backpropagation, and learning rate) of an ANN help in adjusting the weights so that the network learns to predict the expected outputs from given inputs. We will first learn, mathematically, what a neural network is, and then build one from scratch to have a solid foundation. Then we will learn about each component responsible for training the neural network and code them as well. Overall, we will cover the following topics:

• Comparing AI and traditional machine learning
• Learning about the ANN building blocks
• Implementing feedforward propagation
• Implementing backpropagation
• Putting feedforward propagation and backpropagation together
• Understanding the impact of the learning rate
• Summarizing the training process of a neural network

All code snippets within this chapter are available in the Chapter01 folder of the Github repository at https://bit.ly/mcvp-2e.

We strongly recommend you execute code using the Open in Colab button within each notebook.