About this guide
A concise exam companion for neural networks. It builds from the perceptron and multilayer perceptrons through activation and loss functions to backpropagation worked in detail, then optimisers, initialisation, regularisation and batch normalisation, convolutional and recurrent networks, and practical training and debugging. Every chapter ends with a worked example carried through step by step, so you can check the reasoning yourself.
Inside the guide
- Then optimisers
- Initialisation
- Regularisation and batch normalisation
- Convolutional and recurrent networks
- Practical training and debugging
Questions students ask
What does Neural Networks cover?
Neural Networks covers the core exam topics.
Who is Neural Networks for?
It is written for university students revising neural networks, and for lecturers and librarians choosing a clear, exam-focused reading-list text.
What format does it come in?
Kindle ebook and paperback on Amazon.
Can I read part of it for free?
Full Marks Press offers a free sample chapter at fullmarkspress.com/free, and the full guide is free to read with Kindle Unlimited.