Deep Learning Via Rust

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Deep Learning Via Rust

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Author : Evan Pradipta Hardinatha
language : en
Publisher: RantAI
Release Date : 2024-12-26

Deep Learning Via Rust written by Evan Pradipta Hardinatha and has been published by RantAI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-26 with Computers categories.

"Deep Learning via Rust" or DLVR offers a comprehensive exploration of deep learning concepts and techniques through the lens of the Rust programming language, known for its performance and safety. The book begins by establishing a strong foundation in deep learning principles, mathematical underpinnings, and introduces essential Rust libraries for machine learning. It then delves into a wide array of neural network architectures, including CNNs, RNNs, Transformers, GANs, and emerging models like diffusion and energy-based models, providing both theoretical insights and practical implementations. Advanced topics such as hyperparameter optimization, self-supervised learning, reinforcement learning, and model interpretability are thoroughly examined to enhance model performance and understanding. The later sections focus on building, deploying, and scaling deep learning models in Rust across various applications like computer vision, natural language processing, and time series analysis, while also addressing scalable and distributed training techniques. Finally, the book explores current and emerging trends in the field, including federated learning, quantum machine learning, ethical considerations in AI, and the development of large language models using Rust, positioning readers at the forefront of deep learning research and applications.

Machine Learning Via Rust

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Author : Evan Pradipta Hardinatha
language : en
Publisher: RantAI
Release Date : 2024-10-14

Machine Learning Via Rust written by Evan Pradipta Hardinatha and has been published by RantAI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-14 with Computers categories.

Transform Machine Learning with Rust! 🤖🦀 Introducing MLVR - Machine Learning via Rust—the groundbreaking textbook that seamlessly blends the theoretical rigor of machine learning with the modern, high-performance capabilities of the Rust programming language! 🚀 Whether you're a student embarking on your machine learning journey or a professional looking to elevate your skills, MLVR is your comprehensive guide to mastering machine learning with Rust’s unparalleled strengths in performance, safety, and concurrency. ✨ Why Choose MLVR? 🔍 Comprehensive Coverage: From classical models like linear regression and neural networks to cutting-edge techniques such as AutoML and reinforcement learning, MLVR covers it all. 💡 Modern Integration: Leverage Rust’s unique ownership model and advanced type system to implement machine learning algorithms with unmatched safety and efficiency. 🛠️ Practical Implementation: Benefit from step-by-step coding guides, clear explanations, and real-world applications that bridge the gap between theory and practice. 🤖 Performance & Safety: Harness Rust’s core strengths to build machine learning models that are not only fast but also memory-safe and concurrent. Unlock the Benefits: ✅ High Performance: Optimize machine learning models to run at peak speed using Rust’s low-level control without compromising on safety. ✅ Scalable Solutions: Implement scalable and efficient machine learning systems that can handle large datasets and complex computations. ✅ Robust Deployments: Deploy machine learning models with confidence, knowing that Rust’s strong type system and ownership model prevent common programming errors. What You'll Explore: Foundations of Machine Learning: Understand the essential concepts and algorithms that form the backbone of machine learning. Advanced Techniques: Dive into sophisticated methods like AutoML and reinforcement learning, tailored for Rust’s ecosystem. Real-World Applications: Apply your knowledge to real-world projects, showcasing the practical power of Rust in machine learning. Optimization Strategies: Learn how to fine-tune your models for maximum performance and efficiency using Rust’s capabilities. Perfect For: Students seeking a solid foundation in machine learning with a modern programming language. Professionals aiming to enhance their machine learning expertise and optimize their Rust projects. Developers of all levels who want to implement, optimize, and deploy machine learning models effectively using Rust. Embrace the future of machine learning—transform your skills and projects with MLVR - Machine Learning via Rust’s innovative and comprehensive approach! 📚🌟 Start your journey towards mastering machine learning with Rust today and unlock new possibilities in this rapidly evolving field! 🏆 #MachineLearning #RustProgramming #MLVR #DataScience #AI #TechBooks #LearnRust #DeveloperSkills #SoftwareEngineering

Reinforcement Learning Via Rust

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Author : Evan Pradipta Hardinatha
language : en
Publisher: RantAI
Release Date : 2024-12-25

Reinforcement Learning Via Rust written by Evan Pradipta Hardinatha and has been published by RantAI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-25 with Computers categories.

"RLVR - Reinforcement Learning via Rust" draws its inspiration from Richard S. Sutton and Andrew G. Barto's foundational work, "Reinforcement Learning: An Introduction," and integrates the comprehensive curriculum of Stanford University's renowned [CS234: Reinforcement Learning course](https://web.stanford.edu/class/cs234/), which is celebrated for its in-depth exploration of RL concepts and applications. Our goal is to build upon these classics by presenting a modern approach that leverages Generative AI (GenAI) to balance the theoretical foundations with practical implementations of reinforcement learning using the Rust programming language. We recognize the pivotal role that reinforcement learning plays in developing sophisticated AI/ML systems and believe that mastering these concepts is essential for contributing to the next wave of technological innovation. By promoting Rust for reinforcement learning implementations, we aim to cultivate a vibrant community of developers and researchers who can harness Rust's efficiency, safety, and performance to push the boundaries of AI. Through RLVR, we provide a comprehensive resource that accelerates the development of reinforcement learning, encourages the adoption of Rust, and ultimately contributes to the growth and evolution of the field. By incorporating the structured lectures, practical assignments, and cutting-edge research insights from Stanford's CS234, RLVR ensures that learners gain both theoretical knowledge and hands-on experience, effectively bridging the gap between academic study and real-world application.

Practical Machine Learning With Rust

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Author : Joydeep Bhattacharjee
language : en
Publisher: Apress
Release Date : 2019-12-10

Practical Machine Learning With Rust written by Joydeep Bhattacharjee and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-10 with Mathematics categories.

Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will Learn Write machine learning algorithms in Rust Use Rust libraries for different tasks in machine learning Create concise Rust packages for your machine learning applications Implement NLP and computer vision in Rust Deploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust.

The Rust Programming Language Covers Rust 2018

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Author : Steve Klabnik
language : en
Publisher: No Starch Press
Release Date : 2019-08-12

The Rust Programming Language Covers Rust 2018 written by Steve Klabnik 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 2019-08-12 with Computers categories.

The official book on the Rust programming language, written by the Rust development team at the Mozilla Foundation, fully updated for Rust 2018. The Rust Programming Language is the official book on Rust: an open source systems programming language that helps you write faster, more reliable software. Rust offers control over low-level details (such as memory usage) in combination with high-level ergonomics, eliminating the hassle traditionally associated with low-level languages. The authors of The Rust Programming Language, members of the Rust Core Team, share their knowledge and experience to show you how to take full advantage of Rust's features--from installation to creating robust and scalable programs. You'll begin with basics like creating functions, choosing data types, and binding variables and then move on to more advanced concepts, such as: Ownership and borrowing, lifetimes, and traits Using Rust's memory safety guarantees to build fast, safe programs Testing, error handling, and effective refactoring Generics, smart pointers, multithreading, trait objects, and advanced pattern matching Using Cargo, Rust's built-in package manager, to build, test, and document your code and manage dependencies How best to use Rust's advanced compiler with compiler-led programming techniques You'll find plenty of code examples throughout the book, as well as three chapters dedicated to building complete projects to test your learning: a number guessing game, a Rust implementation of a command line tool, and a multithreaded server. New to this edition: An extended section on Rust macros, an expanded chapter on modules, and appendixes on Rust development tools and editions.

Research Anthology On Machine Learning Techniques Methods And Applications

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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2022-05-13

Research Anthology On Machine Learning Techniques Methods And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-13 with Computers categories.

Machine learning continues to have myriad applications across industries and fields. To ensure this technology is utilized appropriately and to its full potential, organizations must better understand exactly how and where it can be adapted. Further study on the applications of machine learning is required to discover its best practices, challenges, and strategies. The Research Anthology on Machine Learning Techniques, Methods, and Applications provides a thorough consideration of the innovative and emerging research within the area of machine learning. The book discusses how the technology has been used in the past as well as potential ways it can be used in the future to ensure industries continue to develop and grow. Covering a range of topics such as artificial intelligence, deep learning, cybersecurity, and robotics, this major reference work is ideal for computer scientists, managers, researchers, scholars, practitioners, academicians, instructors, and students.

Practical Rust Projects

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Author : Shing Lyu
language : en
Publisher: Apress
Release Date : 2020-02-27

Practical Rust Projects written by Shing Lyu and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-27 with Computers categories.

Go beyond the basics and build complete applications using the Rust programming language. The applications in this book include a high-performance web client, a microcontroller (for a robot, for example), a game, an app that runs on Android, and an application that incorporates AI and machine learning. Each chapter will be organized in the following format: what this kind of application looks like; requirements and user stories of our example program; an introduction to the Rust libraries used; the actual implementation of the example program, including common pitfalls and their solutions; and a brief comparison of libraries for building each application, if there is no clear winner. Practical Rust Projects will open your eyes to the world of practical applications of Rust. After reading the book, you will be able to apply your Rust knowledge to build your own projects. What You Will Learn Write Rust code that runs on microcontrollers Build a 2D game Create Rust-based mobile Android applications Use Rust to build AI and machine learning applications Who This Book Is For Someone with basic Rust knowledge, wishing to learn more about how to apply Rust in a real-world scenario.

Deep Learning With R

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Author : François Chollet
language : en
Publisher: Simon and Schuster
Release Date : 2018-01-22

Deep Learning With R written by François Chollet and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-22 with Computers categories.

Summary Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Continue your journey into the world of deep learning with Deep Learning with R in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/deep-​learning-with-r-in-motion). Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks. About the Book Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image classification and generation Deep learning for text and sequences About the Reader You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed. About the Authors François Chollet is a deep-learning researcher at Google and the author of the Keras library. J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions

Grokking Deep Learning

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Author : Andrew Trask
language : en
Publisher: Manning Publications
Release Date : 2019-01-25

Grokking Deep Learning written by Andrew Trask and has been published by Manning Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-25 with Computers categories.

Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide

Deep Learning And The Game Of Go

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Author : Kevin Ferguson
language : en
Publisher: Simon and Schuster
Release Date : 2019-01-06

Deep Learning And The Game Of Go written by Kevin Ferguson and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-06 with Computers categories.

Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning