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. Worked examples are marked the way an examiner marks them, so you can see why an answer earns the grade.
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 get a free copy?
Yes. Full Marks Press offers free review copies to students and educators at fullmarkspress.com/free.