Deep Learning and Generative AI
Transformers, LLMs, Alignment and Prompting
About this guide
A concise exam companion for deep learning and generative AI. It moves from tensors and backpropagation through attention and the transformer architecture to tokenisation, pre-training, large language models and their scaling and fine-tuning, alignment through instruction tuning and RLHF, prompt engineering, retrieval-augmented generation and evaluation. Every chapter ends with a worked example carried through step by step, so you can check the reasoning yourself.
Inside the guide
- Pre-training
- Large language models and their scaling and fine-tuning
- Alignment through instruction tuning and RLHF
- Prompt engineering
- Retrieval-augmented generation and evaluation
Questions students ask
What does Deep Learning and Generative AI cover?
Deep Learning and Generative AI covers the core exam topics.
Who is Deep Learning and Generative AI for?
It is written for university students revising deep learning and generative AI, 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.