In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks.

Build a Large Language Model (from Scratch) teaches you how to:

- Plan and code all the parts of an LLM
- Prepare a dataset suitable for LLM training
- Fine-tune LLMs for text classification and with your own data
- Use human feedback to ensure your LLM follows instructions
- Load pretrained weights into an LLM

Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you'll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant.

Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.

About the technology

Physicist Richard P. Feynman reportedly said, I don't understand anything I can't build. Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning.

About the book

Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you'll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you'll really understand it because you built it yourself!

What's inside

- Plan and code an LLM comparable to GPT-2
- Load pretrained weights
- Construct a complete training pipeline
- Fine-tune your LLM for text classification
- Develop LLMs that follow human instructions

About the reader

Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs.

About the author

Sebastian Raschka is a Staff Research Engineer at Lightning AI, where he works on LLM research and develops open-source software.

The technical editor on this book was David Caswell.

Table of Contents

1 Understanding large language models
2 Working with text data
3 Coding attention mechanisms
4 Implementing a GPT model from scratch to generate text
5 Pretraining on unlabeled data
6 Fine-tuning for classification
7 Fine-tuning to follow instructions
A Introduction to PyTorch
B References and further reading
C Exercise solutions
D Adding bells and whistles to the training loop
E Parameter-efficient fine-tuning with LoRA

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.

Purchase of the print or Kindle book includes a free eBook in PDF format.

• Learn applied machine learning with a solid foundation in theory
• Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
• Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices

Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.

Why PyTorch?

PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.

You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).

This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

• Explore frameworks, models, and techniques for machines to 'learn' from data
• Use scikit-learn for machine learning and PyTorch for deep learning
• Train machine learning classifiers on images, text, and more
• Build and train neural networks, transformers, and boosting algorithms
• Discover best practices for evaluating and tuning models
• Predict continuous target outcomes using regression analysis
• Dig deeper into textual and social media data using sentiment analysis

If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.

Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.

1. Giving Computers the Ability to Learn from Data
2. Training Simple Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using Scikit-Learn
4. Building Good Training Datasets - Data Preprocessing
5. Compressing Data via Dimensionality Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
7. Combining Different Models for Ensemble Learning
8. Applying Machine Learning to Sentiment Analysis
9. Predicting Continuous Target Variables with Regression Analysis
10. Working with Unlabeled Data - Clustering Analysis
11. Implementing a Multilayer Artificial Neural Network from Scratch

(N.B. Please use the Look Inside option to see further chapters)

If you're ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast and easy for you, without a lot of mucking about.

Born out of questions often fielded by author Sebastian Raschka, the direct, no-nonsense approach of this book makes advanced topics more accessible and genuinely engaging. Each brief, self-contained chapter journeys through a fundamental question in AI, unraveling it with clear explanations, diagrams, and hands-on exercises.

WHAT'S INSIDE:

FOCUSED CHAPTERS: Key questions in AI are answered concisely, and complex ideas are broken down into easily digestible parts.

WIDE RANGE OF TOPICS: Raschka covers topics ranging from neural network architectures and model evaluation to computer vision and natural language processing.

PRACTICAL APPLICATIONS: Learn techniques for enhancing model performance, fine-tuning large models, and more.

You'll also explore how to:
- Manage the various sources of randomness in neural network training
- Differentiate between encoder and decoder architectures in large language models
- Reduce overfitting through data and model modifications
- Construct confidence intervals for classifiers and optimize models with limited labeled data
- Choose between different multi-GPU training paradigms and different types of generative AI models
- Understand performance metrics for natural language processing
- Make sense of the inductive biases in vision transformers

If you've been on the hunt for the perfect resource to elevate your understanding of machine learning, Machine Learning Q and AI will make it easy for you to painlessly advance your knowledge beyond the basics.

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.

Purchase of the print or Kindle book includes a free eBook in the PDF format.

• Third edition of the bestselling, widely acclaimed Python machine learning book
• Clear and intuitive explanations take you deep into the theory and practice of Python machine learning
• Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.

This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

• Master the frameworks, models, and techniques that enable machines to 'learn' from data
• Use scikit-learn for machine learning and TensorFlow for deep learning
• Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more
• Build and train neural networks, GANs, and other models
• Discover best practices for evaluating and tuning models
• Predict continuous target outcomes using regression analysis
• Dig deeper into textual and social media data using sentiment analysis

If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

1. Giving Computers the Ability to Learn from Data
2. Training Simple Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using scikit-learn
4. Building Good Training Datasets - Data Preprocessing
5. Compressing Data via Dimensionality Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
7. Combining Different Models for Ensemble Learning
8. Applying Machine Learning to Sentiment Analysis
9. Embedding a Machine Learning Model into a Web Application
10. Predicting Continuous Target Variables with Regression Analysis
11. Working with Unlabeled Data - Clustering Analysis
12. Implementing a Multilayer Artificial Neural Network from Scratch
13. Parallelizing Neural Network Training with TensorFlow

(N.B. Please use the Look Inside option to see further chapters)

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.

Purchase of the print or Kindle book includes a free eBook in PDF format.

• Learn applied machine learning with a solid foundation in theory
• Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
• Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices

Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.

Why PyTorch?

PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.

You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).

This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

• Explore frameworks, models, and techniques for machines to 'learn' from data
• Use scikit-learn for machine learning and PyTorch for deep learning
• Train machine learning classifiers on images, text, and more
• Build and train neural networks, transformers, and boosting algorithms
• Discover best practices for evaluating and tuning models
• Predict continuous target outcomes using regression analysis
• Dig deeper into textual and social media data using sentiment analysis

If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.

Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.

1. Giving Computers the Ability to Learn from Data
2. Training Simple Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using Scikit-Learn
4. Building Good Training Datasets - Data Preprocessing
5. Compressing Data via Dimensionality Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
7. Combining Different Models for Ensemble Learning
8. Applying Machine Learning to Sentiment Analysis
9. Predicting Continuous Target Variables with Regression Analysis
10. Working with Unlabeled Data - Clustering Analysis
11. Implementing a Multilayer Artificial Neural Network from Scratch

(N.B. Please use the Look Inside option to see further chapters)

Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.

Key Features

• Second edition of the bestselling book on Machine Learning
• A practical approach to key frameworks in data science, machine learning, and deep learning
• Use the most powerful Python libraries to implement machine learning and deep learning
• Get to know the best practices to improve and optimize your machine learning systems and algorithms

Book Description
.
Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.

Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.

Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library.

Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities.

If you've read the first edition of this book, you'll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.

What You Will Learn

• Understand the key frameworks in data science, machine learning, and deep learning
• Harness the power of the latest Python open source libraries in machine learning
• Explore machine learning techniques using challenging real-world data
• Master deep neural network implementation using the TensorFlow 1.x library
• Learn the mechanics of classification algorithms to implement the best tool for the job
• Predict continuous target outcomes using regression analysis
• Uncover hidden patterns and structures in data with clustering
• Delve deeper into textual and social media data using sentiment analysis

Who this book is for

If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics

Key Features

Book Description

Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.

Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.

What you will learn

Who this book is for

If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.

Leverage benefits of machine learning techniques using Python.

Key Features

• Improve and optimise machine learning systems using effective strategies.
• Develop a strategy to deal with a large amount of data.
• Use of Python code for implementing a range of machine learning algorithms and techniques.

Book Description

Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project.

The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems.

The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it's time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems.

At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering.

What you will learn

• Learn to write clean and elegant Python code that will optimize the strength of your algorithms
• Uncover hidden patterns and structures in data with clustering
• Improve accuracy and consistency of results using powerful feature engineering techniques
• Gain practical and theoretical understanding of cutting-edge deep learning algorithms
• Solve unique tasks by building models
• Get grips on the machine learning design process

Who this book is for

This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts.