Neural networks

Dive into the world of neural networks, the building blocks of modern artificial intelligence. Learn how these computational models mimic the structure and function of the human brain to process data, recognise patterns, and make predictions.

What is a neural network?

Discover how the interconnected nodes of neural networks mimic human learning and revolutionize technology.

Feedforward neural networks: everything you need to know

Feedforward neural networks (FNNs), often referred to as multi-layer perceptrons (MLP), are one of the simplest forms of neural networks.

What are Convolutional Neural Networks (CNNs)?

Explore the architecture of Convolutional Neural Networks (CNNs), their ability to learn spatial hierarchies, and their role in artificial intelligence and computer vision.

Introduction to Recurrent Neural Networks (RNNs)

A class of artificial neural networks specifically designed to handle sequential data, making them highly effective for tasks where the order of data points is important.

Neural networks: Introduction to generative adversarial networks

GANs consist of two neural networks - a generator & a discriminator - locked in a creative game. Learn how this leads to stunningly realistic data generation.

Neural networks: How to optimize with gradient descent

Learn the different gradient descent algorithms and practical tips for effective optimization.

Transformer models: What are they, and how do they work?

Transformers have redefined how machines understand and generate human language by processing data sequentially. Learn what they are, how they work, and their applications.

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