Hands-On System Programming with C++: Build performant and concurrent Unix and Linux systems with C++17 Author: Dr. Rian Quinn Pub Date: 2018 ISBN: 978-1789137880 Pages: 552 Language: English Format: PDF A hands-on guide to making system programming with C++ easy C++ is a general-purpose programming language with a bias toward system programming as it provides ready access to hardware-level resources, efficient compilation, and a versatile approach to higher-level abstractions. This book will help you understand the benefits of system programming with C++17. You will gain a firm understanding of various C, C++, and POSIX standards, as well as their respective system types for both C++ and POSIX. After a brief refresher on C++, Resource Acquisition Is Initialization (RAII), and the new C++ Guideline Support Library (GSL), you will learn to program Linux and Unix systems along with process management. As you progress through the chapters, you will become acquainted with C++’s support for IO. You will then study various memory management methods, including a chapter on allocators and how they benefit system programming. You will also explore how to program file input and output and learn about POSIX sockets. This book will help you get to grips with safely setting up a UDP and TCP server/client. Finally, you will be guided through Unix time interfaces, multithreading, and error handling with C++ exceptions. By the end of this book, you will be comfortable with using C++ to program high-quality systems. What you will learn Understand the benefits of using C++ for system programming Program Linux/Unix systems using C++ Discover the advantages of Resource Acquisition Is Initialization (RAII) Program both console and file input and output Uncover the POSIX socket APIs and understand how to program them Explore advanced system programming topics, such as C++ allocators Use POSIX and C++ threads to program concurrent systems Grasp how C++ can be used to create performant sy

Hands-On Concurrency with Rust.Hands-On Concurrency with Rust

Hands-On TypeScript for C# and .NET Core Developers.2018, 很有用

Hands On Machine Learning with Python by John Anderson English | 6 Aug. 2018 | ISBN: 1724731963 | 224 Pages | EPUB | 2.22 MB

Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow

Deep Learning 入门推荐，由浅及深， 深入浅出 Contents： ■Chapter 1: Introduction to Deep Learning ............................................................. 1 ■■Chapter 2: Machine Learning Fundamentals ......................................................... 5 ■■Chapter 3: Feed Forward Neural Networks ......................................................... 15 ■■Chapter 4: Introduction to Theano ....................................................................... 33 ■■Chapter 5: Convolutional Neural Networks ......................................................... 61 ■■Chapter 6: Recurrent Neural Networks ............................................................... 77 ■■Chapter 7: Introduction to Keras ......................................................................... 95 ■■Chapter 8: Stochastic Gradient Descent ............................................................ 111 ■■Chapter 9: Automatic Differentiation ................................................................. 131 ■■Chapter 10: Introduction to GPUs ...................................................................... 147

Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent System 英文高清版pdf 随书代码下载地址： https://github.com/ageron/handson-ml

著名的《Hands-On Machine Learning with Scikit-Learn and TensorFlow》的中文翻译版PDF，由ApacheCN中文社区的机器学习爱好者们共同翻译。一个人可以走的很快，但是一群人却可以走的更远。 这本书可以带领你入门机器学习，并掌握常用机器学习库的编程实现，在ML路上走得更远。

Hands-On Transfer Learning with Python（带书签PDF+代码）,迁移学习Python实战，by Dipanjan Sarkar。

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details Table of Contents Chapter 1: The Machine Learning Landscape Chapter 2: End-to-End Machine Learning Project Chapter 3: Classification Chapter 4: Training Linear Models Chapter 5: Support Vector Machines Chapter 6: Decision Trees Chapter 7: Ensemble Learning and Random Forests Chapter 8: Dimensionality Reduction Chapter 9: Up and Running with TensorFlow Chapter 10: Introduction to Artificial Neural Networks Chapter 11: Training Deep Neural Nets Chapter 12: Distributing TensorFlow Across Devices and S