Beginning Python 3 With Claude 3

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Beginning Python 3 With Claude 3

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Author : Oswald Campesato
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2025-02-03

Beginning Python 3 With Claude 3 written by Oswald Campesato and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-03 with Computers categories.

This book is a comprehensive guide designed to teach the fundamentals of Python programming while introducing the exciting possibilities of Generative AI. Whether you’re a novice or a developer looking to integrate Claude 3 into your workflow, this book offers a clear, step-by-step path to mastering Python and leveraging AI-driven code generation. It begins by covering Python fundamentals, including data types, string manipulation, loops, conditional logic, and exception handling. It then introduces Python collections, such as lists, dictionaries, and sets, along with their practical applications. Readers will explore essential Python libraries like NumPy and Pandas, learning how to manipulate data and perform advanced operations. The last two chapters cover Generative AI and Claude, distinguishing it from conversational AI and provides hands-on examples of Claude-generated Python code to solve various programming tasks. Readers will find a balanced mix of theory, practical examples, and Claude-generated code to build both foundational programming skills and an understanding of AI-driven development. FEATURES • Covers Python programming basics and popular libraries like NumPy and Pandas, with a focus on practical applications • Introduces Generative AI concepts and Claude, showcasing its use in generating Python code • Includes companion files with code and images -- available from the publisher for downloading (with proof of purchase)

Beginning Python 3 With Grok 2

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Author : Oswald Campesato
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-12-31

Beginning Python 3 With Grok 2 written by Oswald Campesato and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-31 with Computers categories.

This book is a comprehensive guide designed to teach the fundamentals of Python programming while introducing the exciting possibilities of Generative AI. Whether you’re a novice or a developer looking to integrate Grok into your workflow, this book offers a clear, step-by-step path to mastering Python and leveraging AI-driven code generation. It begins by covering Python fundamentals, including data types, string manipulation, loops, conditional logic, and exception handling. It then introduces Python collections, such as lists, dictionaries, and sets, along with their practical applications. Readers will explore essential Python libraries like NumPy and Pandas, learning how to manipulate data and perform advanced operations. The last two chapters cover Generative AI and Grok, distinguishing it from conversational AI and provides hands-on examples of Grok-generated Python code to solve various programming tasks. Readers will find a balanced mix of theory, practical examples, and Grok-generated code to build both foundational programming skills and an understanding of AI-driven development. FEATURES • Covers Python programming basics and popular libraries like NumPy and Pandas, with a focus on practical applications • Introduces Generative AI concepts and Grok, showcasing its use in generating Python code • Includes companion files with code and images -- available from the publisher for downloading (with proof of purchase)

Python 3 And Machine Learning Using Chatgpt Gpt 4

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Author : Oswald Campesato
language : en
Publisher: Stylus Publishing, LLC
Release Date : 2024-05-22

Python 3 And Machine Learning Using Chatgpt Gpt 4 written by Oswald Campesato and has been published by Stylus Publishing, LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-22 with Computers categories.

This book is designed to bridge the gap between theoretical knowledge and practical application in the fields of Python programming, machine learning, and the innovative use of ChatGPT-4 in data science. The book is structured to facilitate a deep understanding of several core topics. It begins with a detailed introduction to Pandas, a cornerstone Python library for data manipulation and analysis. Next, it explores a variety of machine learning classifiers from kNN to SVMs. In later chapters, it discusses the capabilities of GPT-4, and how its application enhances traditional linear regression analysis. Finally, the book covers the innovative use of ChatGPT in data visualization. This segment focuses on how AI can transform data into compelling visual stories, making complex results accessible and understandable. It includes material on AI apps, GANs, and DALL-E. Companion files are available for downloading with code and figures from the text. FEATURES: Includes practical tutorials designed to provide hands-on experience, reinforcing learning through practice Provides coverage of the latest Python tools using state-of-the-art libraries essential for modern data scientists Companion files with source code, datasets, and figures are available for downloading

Python 3 Using Chatgpt Gpt 4

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Author : Oswald Campesato
language : en
Publisher: Stylus Publishing, LLC
Release Date : 2023-12-12

Python 3 Using Chatgpt Gpt 4 written by Oswald Campesato and has been published by Stylus Publishing, LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-12 with Computers categories.

This book is intended primarily for people who want to learn both Python 3 and how to use ChatGPT with Python. Chapter One begins with an introduction to fundamental aspects of Python programming, including various data types, number formatting, Unicode and UTF-8 handling, and text manipulation techniques. Later, the book covers loops, conditional logic, and reserved words in Python. You will also see how to handle user input, manage exceptions, and work with command-line arguments. Next, the text transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including ChatGPT, GPT-4, and their competitors, are presented to give readers an understanding of the current AI landscape. The book also sheds light on the capabilities of ChatGPT, its strengths, weaknesses, and potential applications. In addition, you will learn how to generate a variety of Python 3 code samples via ChatGPT using the “Code Interpreter” plugin. Code samples and figures from the book are available for downloading. In essence, the book provides a modest bridge between the worlds of Python programming and AI, aiming to equip readers with the knowledge and skills to navigate both domains confidently. FEATURES Includes a chapter on how to generate a variety of Python 3 code samples via ChatGPT using the “Code Interpreter” plugin Covers basic concepts of Python 3 such as loops, conditional logic, reserved words, user input, manage exceptions, work with command-line arguments, and more Includes companion files for downloading with source code and figures

Python 3 And Data Visualization Using Chatgpt Gpt 4

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Author : Oswald Campesato
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2023-12-15

Python 3 And Data Visualization Using Chatgpt Gpt 4 written by Oswald Campesato and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-15 with Computers categories.

This book is designed to show readers the concepts of Python 3 programming and the art of data visualization. It also explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, leading into data visualization using prominent libraries such as Matplotlib. Chapter 6 includes Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. Further, the book covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. Chapter 7 covers information about the main features of ChatGPT and GPT-4, as well as some of their competitors. Chapter 8 contains examples of using ChatGPT in order to perform data visualization, such as charts and graphs that are based on datasets (e.g., the Titanic dataset). Companion files with code, datasets, and figures are available for downloading. From foundational Python concepts to the intricacies of data visualization, this book is ideal for Python practitioners, data scientists, and anyone in the field of data analytics looking to enhance their storytelling with data through visuals. It's also perfect for educators seeking material for teaching advanced data visualization techniques.

Introduction To Python And Large Language Models

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Author : Dilyan Grigorov
language : en
Publisher: Springer Nature
Release Date : 2024-10-22

Introduction To Python And Large Language Models written by Dilyan Grigorov and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-22 with Computers categories.

Gain a solid foundation for Natural Language Processing (NLP) and Large Language Models (LLMs), emphasizing their significance in today’s computational world. This book is an introductory guide to NLP and LLMs with Python programming. The book starts with the basics of NLP and LLMs. It covers essential NLP concepts, such as text preprocessing, feature engineering, and sentiment analysis using Python. The book offers insights into Python programming, covering syntax, data types, conditionals, loops, functions, and object-oriented programming. Next, it delves deeper into LLMs, unraveling their complex components. You’ll learn about LLM elements, including embedding layers, feedforward layers, recurrent layers, and attention mechanisms. You’ll also explore important topics like tokens, token distributions, zero-shot learning, LLM hallucinations, and insights into popular LLM architectures such as GPT-4, BERT, T5, PALM, and others. Additionally, it covers Python libraries like Hugging Face, OpenAI API, and Cohere. The final chapter bridges theory with practical application, offering step-by-step examples of coded applications for tasks like text generation, summarization, language translation, question-answering systems, and chatbots. In the end, this book will equip you with the knowledge and tools to navigate the dynamic landscape of NLP and LLMs. What You’ll Learn Understand the basics of Python and the features of Python 3.11 Explore the essentials of NLP and how do they lay the foundations for LLMs. Review LLM components. Develop basic apps using LLMs and Python. Who This Book Is For Data analysts, AI and Machine Learning Experts, Python developers, and Software Development Professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks.

Illustrated Guide To Python 3

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Author : Matt Harrison
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-11-03

Illustrated Guide To Python 3 written by Matt Harrison and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-03 with Python (Computer program language) categories.

Introducing Your Guide to Learning PythonIllustrated Guide to Learning Python is designed to bring developers and others who are anxious to learn Python up to speed quickly. Not only does it teach the basics of syntax, but it condenses years of experience. You will learn warts, gotchas, best practices and hints that have been gleaned through the years in days. You will hit the ground running and running in the right way.Learn Python QuicklyPython is an incredible language. It is powerful and applicable in many areas. It is used for automation of simple or complex tasks, numerical processing, web development, interactive games and more. Whether you are a programmer coming to Python from another language, managing Python programmers or wanting to learn to program, it makes sense to cut to the chase and learn Python the right way. You could scour blogs, websites and much longer tomes if you have time. Treading on Python lets you learn the hints and tips to be Pythonic quickly.Packed with Useful Hints and TipsYou'll learn the best practices without wasting time searching or trying to force Python to be like other languages. I've collected all the gems I've gleaned over years of writing and teaching Python for you.A No Nonsense Guide to Mastering Basic PythonPython is a programming language that lets you work more quickly and integrate your systems more effectively. You can learn to use Python and see almost immediate gains in productivity and lower maintenance costs.What you will learn: Distilled best practices and tips How interpreted languages work Using basic types such as Strings, Integers, and Floats Best practices for using the interpreter during development The difference between mutable and immutable data Sets, Lists, and Dictionaries, and when to use each Gathering keyboard input How to define a class Looping constructs Handling Exceptions in code Slicing sequences Creating modular code Using libraries Laying out code Community prescribed conventions

Large Language Models For Developers

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Author : Oswald Campesato
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-12-26

Large Language Models For Developers written by Oswald Campesato and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-26 with Computers categories.

This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them with the skills to utilize LLMs in practical applications. Designed for developers with a foundational understanding of machine learning, this book covers essential topics such as prompt engineering techniques, fine-tuning methods, attention mechanisms, and quantization strategies to optimize and deploy LLMs. Beginning with an introduction to generative AI, the book explains distinctions between conversational AI and generative models like GPT-4 and BERT, laying the groundwork for prompt engineering (Chapters 2 and 3). Some of the LLMs that are used for generating completions to prompts include Llama-3.1 405B, Llama 3, GPT-4o, Claude 3, Google Gemini, and Meta AI. Readers learn the art of creating effective prompts, covering advanced methods like Chain of Thought (CoT) and Tree of Thought prompts. As the book progresses, it details fine-tuning techniques (Chapters 5 and 6), demonstrating how to customize LLMs for specific tasks through methods like LoRA and QLoRA, and includes Python code samples for hands-on learning. Readers are also introduced to the transformer architecture’s attention mechanism (Chapter 8), with step-by-step guidance on implementing self-attention layers. For developers aiming to optimize LLM performance, the book concludes with quantization techniques (Chapters 9 and 10), exploring strategies like dynamic quantization and probabilistic quantization, which help reduce model size without sacrificing performance. FEATURES • Covers the full lifecycle of working with LLMs, from model selection to deployment • Includes code samples using practical Python code for implementing prompt engineering, fine-tuning, and quantization • Teaches readers to enhance model efficiency with advanced optimization techniques • Includes companion files with code and images -- available from the publisher

Interpretability And Explainability In Ai Using Python

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Author : Aruna Chakkirala
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-04-15

Interpretability And Explainability In Ai Using Python written by Aruna Chakkirala and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.

TAGLINE Demystify AI Decisions and Master Interpretability and Explainability Today KEY FEATURES ● Master Interpretability and Explainability in ML, Deep Learning, Transformers, and LLMs ● Implement XAI techniques using Python for model transparency ● Learn global and local interpretability with real-world examples DESCRIPTION Interpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning, making it easier to debug, validate, and trust. Interpretability and Explainability in AI Using Python takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models. You’ll start with foundational concepts in interpretable machine learning, exploring different model types and their transparency levels. As you progress, you’ll dive into post-hoc methods, feature effect analysis, anchors, and counterfactuals—powerful tools to decode complex models. The book also covers explainability in deep learning, including Neural Networks, Transformers, and Large Language Models (LLMs), equipping you with strategies to uncover decision-making patterns in AI systems. Through hands-on Python examples, you’ll learn how to apply these techniques in real-world scenarios. By the end, you’ll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standards—giving you a competitive edge in the evolving AI landscape. WHAT WILL YOU LEARN ● Dissect key factors influencing model interpretability and its different types. ● Apply post-hoc and inherent techniques to enhance AI transparency. ● Build explainable AI (XAI) solutions using Python frameworks for different models. ● Implement explainability methods for deep learning at global and local levels. ● Explore cutting-edge research on transparency in transformers and LLMs. ● Learn the role of XAI in Responsible AI, including key tools and methods. WHO IS THIS BOOK FOR? This book is tailored for Machine Learning Engineers, AI Engineers, and Data Scientists working on AI applications. It also serves as a valuable resource for professionals and students in AI-related fields looking to enhance their expertise in model interpretability and explainability techniques. TABLE OF CONTENTS 1. Interpreting Interpretable Machine Learning 2. Model Types and Interpretability Techniques 3. Interpretability Taxonomy and Techniques 4. Feature Effects Analysis with Plots 5. Post-Hoc Methods 6. Anchors and Counterfactuals 7. Interpretability in Neural Networks 8. Explainable Neural Networks 9. Explainability in Transformers and Large Language Models 10. Explainability and Responsible AI Index

Practical Deep Learning 2nd Edition

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Author : Ronald T. Kneusel
language : en
Publisher: No Starch Press
Release Date : 2025-07-08

Practical Deep Learning 2nd Edition written by Ronald T. Kneusel and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-08 with Computers categories.

Deep learning made simple. Dip into deep learning without drowning in theory with this fully updated edition of Practical Deep Learning from experienced author and AI expert Ronald T. Kneusel. After a brief review of basic math and coding principles, you’ll dive into hands-on experiments and learn to build working models for everything from image analysis to creative writing, and gain a thorough understanding of how each technique works under the hood. Whether you’re a developer looking to add AI to your toolkit or a student seeking practical machine learning skills, this book will teach you: How neural networks work and how they’re trained How to use classical machine learning models How to develop a deep learning model from scratch How to evaluate models with industry-standard metrics How to create your own generative AI models Each chapter emphasizes practical skill development and experimentation, building to a case study that incorporates everything you’ve learned to classify audio recordings. Examples of working code you can easily run and modify are provided, and all code is freely available on GitHub. With Practical Deep Learning, second edition, you’ll gain the skills and confidence you need to build real AI systems that solve real problems. New to this edition: Material on computer vision, fine-tuning and transfer learning, localization, self-supervised learning, generative AI for novel image creation, and large language models for in-context learning, semantic search, and retrieval-augmented generation (RAG).