Generative Ai Using Ai To Promote The Good Life While Avoiding Harm A Practical Guide To Building Applications With Transformers And Diffusion Models
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Generative AI: Using Ai to Promote the Good Life While Avoiding Harm (A Practical Guide to Building Applications with Transformers and Diffusion Models)
Artificial intelligence is about knowing the foundations and best strategies on how to use AI to create appealing text forms, amazing images, and even hypnotic music. From knowing the foundations to honing sophisticated skills, it gives you the tools and knowledge to succeed in the realm of AI-powered production. Discover how to select the appropriate GAI platforms, create successful prompts, adjust your outputs, and create a flawless workflow that best maximizes your creative process. This book will enable you to investigate the countless opportunities of Generative AI and change your creative path regardless of your level of experience as an artist, writer, musician, or just curious novice. Key features: · Clear and concise explanations: Complex topics broken down into easy-to-understand terms. · Hands-on projects: Practical exercises to apply what you've learned. · Real-world examples: Inspiring case studies of generative AI in action. · No coding required: Accessible to everyone, regardless of technical background. This book is perfect for artists, designers, content creators, and curious minds eager to learn about AI art. It’s written in a friendly, accessible style, making it suitable for anyone interested in mastering AI-powered art generation—from hobbyists to professionals looking to expand their creative toolkit. Ready to start your journey Step into the world of AI art and discover how you can create images that captivate, inspire, and communicate like never before.
Deep Learning for Coders with fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Building Maintainable Software, Java Edition
Author: Joost Visser
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-01-28
Have you ever felt frustrated working with someone else’s code? Difficult-to-maintain source code is a big problem in software development today, leading to costly delays and defects. Be part of the solution. With this practical book, you’ll learn 10 easy-to-follow guidelines for delivering Java software that’s easy to maintain and adapt. These guidelines have been derived from analyzing hundreds of real-world systems. Written by consultants from the Software Improvement Group (SIG), this book provides clear and concise explanations, with advice for turning the guidelines into practice. Examples for this edition are written in Java, while our companion C# book provides workable examples in that language. Write short units of code: limit the length of methods and constructors Write simple units of code: limit the number of branch points per method Write code once, rather than risk copying buggy code Keep unit interfaces small by extracting parameters into objects Separate concerns to avoid building large classes Couple architecture components loosely Balance the number and size of top-level components in your code Keep your codebase as small as possible Automate tests for your codebase Write clean code, avoiding "code smells" that indicate deeper problems