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Название: Android Studio Koala Essentials - Kotlin Edition: Developing Android Apps Using Android Studio Koala Feature Drop and Kotlin
Автор: Neil Smyth
Издательство: Payload Media
Год: 2024
Страниц: 845
Язык: английский
Формат: epub
Размер: 45.0 MB

Unlock the Full Potential of Android Development. Are you ready to create powerful, modern Android apps using Kotlin? This comprehensive guide, fully updated for the Android Studio Koala Feature Drop (2024.1.2), is your ultimate companion to mastering Android app development from start to finish. Get Started with Confidence: From setting up your Android development and testing environment to mastering the fundamentals of Kotlin, including data types, control flow, functions, lambdas, and object-oriented programming, we’ll guide you every step of the way. Dive Deep into Advanced Techniques: Learn asynchronous programming with Kotlin coroutines and flow, and gain expertise in Android Architecture Components such as view models, lifecycle management, Room database access, app navigation, live data, and more. Master Essential Android Features: Develop skills in handling touch screens, gesture recognition, audio playback, and recording, and explore advanced functionalities like intents, printing, transitions, and foldable device support. Why This Book? Whether you’re a seasoned developer ready to elevate your Android skills or an experienced programmer eager to explore new horizons, this book offers everything you need to succeed. Packed with practical examples and expert insights, it’s designed to transform your app ideas into reality.

Название: Better APIs : Quality, Stability, Observability
Автор: Mikael Vesavuori
Издательство: Leanpub
Год: 2024-08-24
Страниц: 100
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

Master the art of building high-quality, stable, and observable APIs with this hands-on guide—perfect for developers and architects looking to elevate their API game and ensure their projects are reliable, maintainable, and scalable. This book is a practical guide to improving the quality, stability, and observability of your APIs. Through a combination of theoretical insights and a hands-on example project, you'll learn how to create and maintain production-grade APIs that meet high standards of excellence. Better APIs: Quality, Stability, Observability is designed for developers and architects who want to deepen their understanding of API development and ensure their APIs are reliable, maintainable, and easy to monitor. APIs are the backbone of modern software architecture, and ensuring they are of high quality, stable, and observable is critical to the success of any software project. This book provides you with the tools and knowledge to create APIs that meet these criteria, helping you avoid common pitfalls and ensuring your APIs can scale and evolve over time. Whether you're looking to refine your existing APIs or build new ones from scratch, Better APIs: Quality, Stability, Observability will provide you with the insights and techniques you need to succeed and create APIs that are not only functional but also robust, maintainable, and easy to monitor.

Название: Deep Generative Modeling, 2nd Edition
Автор: Jakub M. Tomczak
Издательство: Springer
Год: 2024
Страниц: 325
Язык: английский
Формат: pdf (true), epub
Размер: 50.2 MB

This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, GANs, Hybrid Models, Score-based Generative Models, Energy-based Models, and Large Language Models. In addition, Generative AI Systems are discussed, demonstrating how deep generative models can be used for neural compression, among others. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics of Machine Learning, Deep Learning, and programming in Python and PyTorch (or other Deep Learning libraries). It should find interest among students and researchers from a variety of backgrounds, including computer science, engineering, Data Science, physics, and bioinformatics who wish to get familiar with deep generative modeling. In order to engage with a reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on the author's GitHub site.

Название: Deep Learning with PyTorch : Step-by-Step A Beginner's Guide
Автор: Daniel Voigt Godoy
Издательство: Leanpub
Год: 2024-07-29: v1.2
Страниц: 1047
Язык: английский
Формат: pdf (true)
Размер: 33.5 MB

If you're looking for a book where you can learn about Deep Learning and PyTorch without having to spend hours deciphering cryptic text and code, and that's easy and enjoyable to read, this is it. This is not a typical book: most tutorials start with some nice and pretty image classification problem to illustrate how to use PyTorch. It may seem cool, but I believe it distracts you from the main goal: how PyTorch works? In this book, I present a structured, incremental, and from first principles approach to learn PyTorch (and get to the pretty image classification problem in due time). PyTorch is the fastest-growing framework for developing Deep Learning models and it has a huge ecosystem. That is, there are many tools and libraries developed on top of PyTorch. It is the preferred framework in academia already and is making its way in the industry. This book aims to get you started with PyTorch while giving you a solid understanding of how it works. In this book, I will guide you through the development of many models in PyTorch, showing you why PyTorch makes it much easier and more intuitive to build models in Python: autograd, dynamic computation graph, model classes and much, much more. I wrote this book for beginners in general—not only PyTorch beginners. Every now and then, I will spend some time explaining some fundamental concepts that I believe are essential to have a proper understanding of what’s going on in the code.

Название: Automate Your Home Using Go: Build a Personal Data Center with Raspberry Pi, Docker, Prometheus, and Grafana
Автор: Ricardo Gerardi, Mike Riley
Издательство: Pragmatic Bookshelf
Год: August 2024 (v.P1.0)
Страниц: 275
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Take control of your home and your data with the power of the Go programming language. Build extraordinary and robust home automation solutions that rival much more expensive, closed commercial alternatives, using the same tools found in high-end enterprise computing environments. Best-selling Pragmatic Bookshelf authors Ricardo Gerardi and Mike Riley show how you can use inexpensive Raspberry Pi hardware and excellent, open source Go-based software tools like Prometheus and Grafana to create your own personal data center. Using the step-by-step examples in the book, build useful home automation projects that you can use as a blueprint for your own custom projects. With just a Raspberry Pi and the Go programming language, build your own personal data center that coordinates and manages your home automation, leveraging the same high-powered software used by large enterprises. The projects in this book are easy to assemble, no soldering or electrical engineering expertise required. Our objective for the book was to avoid as much electrical engineering and wiring as possible. You can complete each project in this book without ever picking up a soldering gun. While it’s commendable to use one for appropriate cases, this book focuses more on software than hardware. We also didn’t want to have hardware components fail as a result of poor soldering or confusing wiring diagrams, so we opted to make the hardware configuration for these projects as simple as possible to avoid any frustration or expensive mistakes. This book is for developers familiar with the Go programming language who want to do more with it than just the usual integration and microservices that Go is typically used for. It is also for home automation tinkerers and electronics hobbyists interested in learning how a language like Go can be more powerful and make software projects easier to build and maintain, especially when compared to other languages used in home automation like Perl and Python.

Название: C++ Brain Teasers: Exercise Your Mind
Автор: Anders Schau Knatten
Издательство: Pragmatic Bookshelf
Год: June 2024 (P1.0)
Страниц: 140
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

C++ is famous for getting all the default behaviors wrong and for sometimes making demons fly out of your nose. Through 25 puzzles, from the useful to the outright weird, we explore some of C++'s most interesting quirks. How does initialization actually work? Do temporaries even exist? As you work through each puzzle, you will peel off some of the layers of complexity of C++, getting a fundamental understanding of how the language works. This will help you write better code and recognize issues more easily while debugging. Each puzzle in the book is a complete, seemingly simple C++ program, but can you figure out the output for each, or will the answers stump you? Most of the programs compile and have deterministic, though sometimes surprising, output. Some might, however, have undefined behavior, where anything can happen, including making demons fly out of your nose! Yet others might have unspecified behavior with output that is not completely deterministic; we just know nothing as bad as nasal demons will happen. After working through the book, you'll have tools and techniques to help you write better and safer code, and a better understanding of the fundamentals of the language, the background for some of C++'s apparent weirdness, and a better feel for identifying bugs and unsafe code in your own programs. The book assumes basic knowledge of C++. If you want to run the programs yourself, you can use either a local C++ compiler or the online compilers.

Название: Machine Learning in Elixir: Learning to Learn with Nx and Axon
Автор: Sean Moriarity
Издательство: Pragmatic Bookshelf
Год: September 2024 (P1.0)
Страниц: 374
Язык: английский
Формат: epub
Размер: 10.1 MB

Stable Diffusion, ChatGPT, Whisper - these are just a few examples of incredible applications powered by developments in Machine Learning. Despite the ubiquity of Machine Learning applications running in production, there are only a few viable language choices for data science and machine learning tasks. Elixir's Nx project seeks to change that. With Nx, you can leverage the power of machine learning in your applications, using the battle-tested Erlang VM in a pragmatic language like Elixir. In this book, you'll learn how to leverage Elixir and the Nx ecosystem to solve real-world problems in computer vision, natural language processing, and more. The Elixir Nx project aims to make Machine Learningpossible without the need to leave Elixir for solutions in other languages. And even if concepts like linear models and logistic regression are new to you, you'll be using them and much more to solve real-world problems in no time. Start with the basics of the Nx programming paradigm - how it differs from the Elixir programming style you're used to and how it enables you to write Machine Learning algorithms. Use your understanding of this paradigm to implement foundational Machine Learning algorithms from scratch. Go deeper and discover the power of Deep Learning with Axon. Unlock the power of Elixir and learn how to build and deploy Machine Learning models and pipelines anywhere. Learn how to analyze, visualize, and explain your data and models.

Название: Deep Learning with JAX (Final Release)
Автор: Grigory Sapunov
Издательство: Manning Publications
Год: 2024
Страниц: 410
Язык: английский
Формат: pdf (true)
Размер: 39.8 MB

Accelerate Deep Learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. The JAX numerical computing library tackles the core performance challenges at the heart of Deep Learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. Deep Learning with JAX is a hands-on guide to using JAX for Deep Learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment. The JAX Python mathematics library is used by many successful Deep Learning organizations, including Google’s groundbreaking DeepMind team. This exciting newcomer already boasts an amazing ecosystem of tools including high-level Deep Learning libraries Flax by Google, Haiku by DeepMind, gradient processing and optimization libraries, libraries for evolutionary computations, Federated Learning, and much more! JAX brings a functional programming mindset to Python Deep Learning, letting you improve your composability and parallelization in a cluster. For intermediate Python programmers who are familiar with Deep Learning.

Название: Generative AI in Action (Final Release)
Автор: Amit Bahree
Издательство: Manning Publications
Год: 2024
Страниц: 466
Язык: английский
Формат: pdf (true)
Размер: 29.3 MB

Generative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action! GenAI has created new opportunities for organizations of all sizes. You can easily use tools like ChatGPT, Bard, and Stable Diffusion to generate text and images for product catalogs, marketing campaigns, technical reporting, and other common tasks. Coding assistants like Copilot are accelerating productivity in software teams. In this insightful book, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution. The book teaches you how to create and use generative models for tasks and use cases. It focuses on this technology’s practical and hands-on aspects and how it works. It does not dive deep into the science, but it references the papers and scientific breakthroughs that have helped develop some of the technology—you can see these at the end of the book. This book is designed to provide a comprehensive understanding of generative AI and its potential within an enterprise context. It explores foundational models, large language models, and related algorithms and architectures, offering readers a thorough grasp of these advanced technologies. Practical insights and examples are provided to help develop and deploy generative AI models, ensuring that readers can apply these concepts in real-world scenarios. Advanced topics such as prompt engineering, retrieval-augmented generation, and model adaptation are discussed in detail, giving readers an in-depth understanding of these cutting-edge techniques. For enterprise architects and senior developers interested in upgrading their architectures with generative AI.

Название: Build a Large Language Model (From Scratch) (Final Release)
Автор: Sebastian Raschka
Издательство: Manning Publications
Год: 2025
Страниц: 370
Язык: английский
Формат: pdf (true)
Размер: 17.3 MB

Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up! 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) 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. 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. 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! 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.