Oswald Campesato

This book explores the synergy between Claude3, a cutting-edge AI model, and Web technologies such as HTML, CSS3, and SVG, providing both novices and experienced developers with the tools to create visually stunning Web graphics and animations. It introduces the fundamental concepts of Generative AI, the key players in the field, and the distinctive features of Claude3, while also mastering the art of prompt engineering to interact effectively with AI models. The book covers creating dynamic 3D animations with CSS3, including effects like glowing, image fading, and rotating, and demonstrates how to leverage Claude3 to generate sophisticated SVG graphics and animations, thereby enhancing Web designs with advanced visual effects.

FEATURES

• Covers generative AI fundamentals to advanced CSS3 and SVG techniques, offering comprehensive material on modern web development technologies
• Features both manually created and AI generated code samples, security issues, crafting prompts, and accessibility needs
• Balances theoretical knowledge and practical examples, so readers gain hands-on experience in implementing AI-driven design solutions using Claude-generated code
• Includes companion files with code, datasets, and images from the book -- available from the publisher for downloading (with proof of purchase)

TABLE OF CONTENTS

This book is a fast-paced introduction to using data structures with Java. Numerous code samples and listings are included to support myriad topics. The first chapter contains a quick introduction to Java, along with Java code samples to check for leap years, find divisors of a number, and work with arrays of strings. The second chapter introduces recursion and uses code samples to check if a positive number is prime, to find the prime divisors of a positive integer, to calculate the GCD (greatest common divisor) and LCM (lowest common multiple) of a pair of positive integers. The third chapter contains Java code samples involving strings and arrays, such as finding binary substrings of a number, checking if strings contain unique characters, counting bits in a range of numbers, and how to compute XOR without using the XOR function. Chapters 4 through 6 include Java code samples involving search algorithms, concepts in linked lists, and tasks involving linked lists. Finally, Chapter 7 discusses data structures called queues and stacks, along with additional Java code samples.

Features:

• Extensive topics, code samples, and scripts related to data structures
• Covers strings, arrays, queues, and stacks, linked lists, computing the XOR function, checking for unique characters, and more
• Includes companion files with code samples from the book (available for downloading from the publisher)

1. Extensive Python 3.x code samples generated using ChatGPT and GPT-4, covering diverse programming tasks and challenges.
2. Comprehensive exploration of data visualization techniques using popular Python libraries such as Matplotlib and Seaborn.

This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an assortment of Python 3.x code samples that were generated by ChatGPT and GPT-4. Chapter 1 provides an overview of ChatGPT and GPT-4, followed by a chapter which contains Python 3.x code samples for solving various programming tasks in Python. Chapter 3 contains code samples for data visualization, and Chapter 4 contains code samples for linear regression. The final chapter covers visualization with Gen AI (Generative AI) and DALL-E. Companion files with source code and figures are available for downloading.

This book provides relevant information about intermediate Python 3.x for a variety of topics, such as comprehensions, iterators, generators, regular expressions, OOP, queues and stacks, and recursion. Each chapter contains an assortment of code samples that illustrate the topics in the chapter material. It assumes the reader is already familiar with Python programming and wants to expand programming skills in order to use Python in a variety of subject areas and disciplines such as business, mathematics, science, and engineering. Knowledge of other programming languages (such as Java) can also be helpful because of the exposure to programming concepts and object-oriented programming. This book includes companion files with numerous code samples, figures, and applications from the text.

• Encourages readers to compare and contrast hand-written code with ChatGPT-generated code. This approach fosters discussions on code efficiency, readability, and maintainability, enhancing understanding of programming paradigms and techniques.

This book is designed for those new to Java and interested in understanding how ChatGPT/GPT-4 can enhance programming. It offers a unique approach to learning Java, combining traditional hand-written code with cutting-edge ChatGPT-generated examples. The book covers the basics of Java programming and development environments, including understanding recursion, strings, arrays, fundamental data structures, algorithm analysis, queues and stacks, and follows with the role of ChatGPT in generating, explaining, and debugging code. Companion files with source code and figures available for downloading. It’s an essential resource for those starting Java programming and for anyone curious about the applications of ChatGPT in coding.

This book contains a fast-paced introduction to data-related tasks in preparation for training models on datasets. It presents a step-by-step, Python-based code sample that uses the kNN algorithm to manage a model on a dataset.

Chapter One begins with an introduction to datasets and issues that can arise, followed by Chapter Two on outliers and anomaly detection. The next chapter explores ways for handling missing data and invalid data, and Chapter Four demonstrates how to train models with classification algorithms. Chapter 5 introduces visualization toolkits, such as Sweetviz, Skimpy, Matplotlib, and Seaborn, along with some simple Python-based code samples that render charts and graphs. An appendix includes some basics on using awk. Companion files with code, datasets, and figures are available for downloading.

Features:

• Covers extensive topics related to cleaning datasets and working with models
• Includes Python-based code samples and a separate chapter on Matplotlib and Seaborn
• Features companion files with source code, datasets, and figures from the book

This book offers a comprehensive guide to leveraging Python-based data visualization techniques with the innovative capabilities of Google Gemini. Tailored for individuals proficient in Python seeking to enhance their visualization skills, it explores essential libraries like Pandas, Matplotlib, and Seaborn, along with insights into the innovative Gemini platform. With a focus on practicality and efficiency, it delivers a rapid yet thorough exploration of data visualization methodologies, supported by Gemini-generated code samples. Companion files with source code and figures are available for downloading.

FEATURES:

• Covers Python-based data visualization libraries and techniques
• Includes practical examples and Gemini-generated code samples for efficient learning
• Integrates Google Gemini for advanced data visualization capabilities
• Sets up a conducive development environment for a seamless coding experience
• Includes companion files for downloading with source code and figures

As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to basic machine learning algorithms using TensorFlow 2. It is intended to be a fast-paced introduction to various “core” features of TensorFlow, with code samples that cover machine learning and TensorFlow basics. A comprehensive appendix contains some Keras-based code samples and the underpinnings of MLPs, CNNs, RNNs, and LSTMs. The material in the chapters illustrates how to solve a variety of tasks after which you can do further reading to deepen your knowledge. Companion files with all of the code samples are available for downloading from the publisher by emailing proof of purchase to info@merclearning.com.

Features:

• Uses Python for code samples
• Covers TensorFlow 2 APIs and Datasets
• Includes a comprehensive appendix that covers Keras and advanced topics such as NLPs, MLPs, RNNs, LSTMs
• Features the companion files with all of the source code examples and figures (download from the publisher)

• Designed for readers relatively new to working in a bash environment
• Introduces readers to three powerful command line utilities that can be combined to create simple yet powerful shell scripts for performing a multitude of tasks