Название: Building LLMs with PyTorch: A step-by-step guide to building advanced AI models with PyTorch
Автор: Anand Trivedi
Издательство: BPB Publications
Год: 2025
Страниц: 534
Язык: английский
Формат: epub (true)
Размер: 22.4 MB

PyTorch has become the go-to framework for building cutting-edge large language models (LLMs), enabling developers to harness the power of deep learning for natural language processing. This book serves as your practical guide to navigating the intricacies of PyTorch, empowering you to create your own LLMs from the ground up.

You will begin by mastering PyTorch fundamentals, including tensors, autograd, and model creation, before diving into core neural network concepts like gradients, loss functions, and backpropagation. Progressing through regression and image classification with convolutional neural networks, you will then explore advanced image processing through object detection and segmentation. The book seamlessly transitions into NLP, covering RNNs, LSTMs, and attention mechanisms, culminating in the construction of Transformer-based LLMs, including a practical mini-GPT project. You will also get a strong understanding of generative models like VAEs and GANs.

A single idea drove this book: How can anyone who wants to start their journey into AI begin? How can someone understand the complex concepts of LLMs, Generative AI, and Diffusion Models? How can those who want to change existing AI programs and models, or even build new models from scratch, get started?

This book begins with very basic examples and explains how simple problems can be solved. PyTorch is used as it is the most programming-friendly AI framework. You will feel like you are simply learning another Python framework. No prior AI theory is assumed. Simplicity is key, presenting only the theory required for understanding, without unnecessary mathematics. While mathematics can simplify understanding, this is true mainly for those who work with mathematical formulas daily. Application engineers, architects, students, analysts, and entrepreneurs don't typically use complex mathematical formulas daily. Therefore, formulas have been simplified where needed. Special attention has been paid to making the material easy to read.

The book consists of 14 chapters, starting with an introduction to Python variables and data types, and gradually progressing from linear regression to real-life examples of RNNs and CNNs, such as live object detection and specific models for medical fields. It then transitions to generative AI models, which are built from scratch, including translators and a mini large language model.

By the end of this book, you will possess the technical proficiency to build, train, and deploy sophisticated LLMs using PyTorch, equipping you to contribute to the rapidly evolving landscape of AI.

What you will learn:

- Build and train PyTorch models for linear and logistic regression.
- Configure PyTorch environments and utilize GPU acceleration with CUDA.
- Construct CNNs for image classification and apply transfer learning techniques.
- Master PyTorch tensors, autograd, and build fundamental neural networks.
- Utilize SSD and YOLO for object detection and perform image segmentation.
- Develop RNNs and LSTMs for sequence modeling and text generation.
- Implement attention mechanisms and build Transformer-based language models.
- Create generative models using VAEs and GANs for diverse applications.
- Build and deploy your own mini-GPT language model, applying the acquired skills.

Who this book is for:
Software engineers, AI researchers, architects seeking AI insights, and professionals in finance, medical, engineering, and mathematics will find this book a comprehensive starting point, regardless of prior Deep Learning expertise.

Contents: