Which of these five books do you want to be online first? Come and vote for it~

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Which of these five books do you want to be online first? Come and vote for it~ [Copy link]

First of all, I would like to thank the China Machine Press for sponsoring the forum reading activities. The following 5 books are also published by China Machine Press. Which one do you want to be online first? You are welcome to vote for it. We will decide the order of online release based on the final voting results~

Book Description:

Practical AI Big Model (Artificial Intelligence Expert You Yang's masterpiece, recommended by Zhou Hongyi, Yan Shuicheng, and Kai-Fu Lee)
Author: You Yang

"AI Big Models in Action" is a practical manual designed to fill the gap between theory and practice in the field of artificial intelligence (AI), especially AI big models. The book introduces the basics and key technologies of AI big models, such as Transformer, BERT, ALBERT, T5, GPT series, InstructGPT, ChatGPT, GPT 4, PaLM, and visual models, and explains in detail the technical principles, practical applications, and use of high-performance computing (HPC) technologies such as parallel computing and memory optimization. At the same time, "AI Big Models in Action" also provides practical cases and details how to use Colossal AI to train various models. Whether you are an AI beginner or an experienced practitioner, you can learn practical knowledge and skills from this book, so that you can find your own direction in the rapidly developing field of AI.

PyTorch autonomous driving visual perception algorithm practice Liu Stan

"PyTorch Autonomous Driving Visual Perception Algorithm Practice" comprehensively introduces the relevant knowledge of deep learning visual perception in autonomous driving systems, including the basic theories of deep neural networks and deep convolutional neural networks, and deeply explains the four visual perception tasks commonly used in autonomous driving: target detection, semantics, instance segmentation, and monocular depth estimation. "PyTorch Autonomous Driving Visual Perception Algorithm Practice" comprehensively summarizes the important but often overlooked knowledge in autonomous driving engineering practice, including loss balance of multi-task models, Ubuntu operating system, environment configuration tools such as Anaconda and Docker, C++ development environment construction, neural network compression, model export and quantization, TensorRT inference engine and other deployment-related technologies. "PyTorch Autonomous Driving Visual Perception Algorithm Practice" implements each task by PyTorch, and the model deployment code provides C++ implementation, and comes with a medium-sized autonomous driving dataset for examples. All codes are publicly available on the Github open source repository, and many codes can be directly used in production environments, and commercial-friendly code licenses are provided. "PyTorch Autonomous Driving Visual Perception Algorithm Practice" is suitable for students who have basic machine learning knowledge and are interested in working on autonomous driving algorithms. It is also suitable for junior engineers who have just entered the workplace and are at a loss when faced with various unfamiliar technologies. At the same time, this book can also be used as a desk book for mid-level and senior algorithm engineers for easy reference.

Illustrated Introduction - Power Semiconductor Fundamentals and Process Detailed Lecture (Original Book 3rd Edition) Junichi Sato

This book explains the various technical links of the power semiconductor manufacturing process in an easy-to-understand way with illustrations. The book is divided into 11 chapters, namely: the overall picture of power semiconductors, the basic principles of power semiconductors, the principles and functions of various power semiconductors, the uses and markets of power semiconductors, the classification of power semiconductors, silicon wafers used for power semiconductors, the characteristics of power semiconductor manufacturing processes, an introduction to power semiconductor manufacturers, the development of silicon-based power semiconductors, silicon carbide and gallium nitride that challenge the limits of silicon, and the era of carbon reduction pioneered by power semiconductors. This book is suitable for people related to the semiconductor business, people who are preparing to enter the semiconductor field, and professionals and students who are interested in power semiconductors.

Point Cloud Registration from Beginner to Mastery Written by Guo Hao

3D point cloud processing technology is widely used in many fields such as reverse engineering, CAD/CAM, robotics, surveying and remote sensing, machine vision, virtual reality, human-computer interaction, unmanned driving and metaverse. As an important branch of the 3D vision field, point cloud registration has a history of more than 40 years. This book systematically sorts out and summarizes the algorithms and tools that have matured in recent years. The book is divided into two parts. The first part is the hard-core technology chapter (Chapters 1-4), which introduces the concept of point cloud registration, application fields, and the necessary mathematical knowledge of point cloud registration in detail. Finally, the classic algorithms involved in the key steps of the point cloud registration process (such as key point extraction, feature description, etc.) are presented in theory and practice in a multi-dimensional way, so as to prepare the theoretical and technical reserves for readers to have a deep understanding of complex registration algorithms. The second part is the algorithm application chapter (Chapters 5-6), which covers more than a dozen open source rigid and non-rigid registration classic algorithms, and introduces them in detail from the aspects of algorithm principles, theoretical basis, technical implementation, application cases, advantages and disadvantages, etc., and uses the source code implementation analysis of the algorithm to help readers understand the details and calculation process of each algorithm. Finally, through the analysis of algorithm application cases, readers can re-evaluate and understand each algorithm from the theoretical, technical and application levels, helping readers in the industry to quickly implement relevant technical applications, and readers in the academic community to quickly and systematically complete the entry and improvement. The book comes with program source code, high-definition case renderings and result videos, as well as PPT for teaching, striving to enhance readers' reading experience and knowledge content from multiple angles. This book can be used as a reference guide for scientific researchers and company product development engineers, and can also be used as a study manual for senior undergraduates and graduate students in related majors such as computer graphics, robotics, remote sensing measurement, virtual reality, human-computer interaction, CAD/CAM reverse engineering, etc.

AI Compiler Development Guide by Wang Yan

The AI Compiler Development Guide combines dedicated AI accelerators and GPGPU chip architectures to systematically introduce the basic framework and development process of AI compilers, focusing on the implementation methods that need to be considered for these two types of architectures during the development of AI compilers. The book is divided into 7 chapters, covering the implementation analysis and customization methods of open source AI compilers represented by TVM, as well as GPGPU compiler backend-related design methods. While introducing the general principles of AI compilers, the book analyzes the source code of open source compiler projects, allowing readers to have a more intuitive understanding of the AI compiler development process through examples. The AI Compiler Development Guide fills the gap in AI compiler development books. It can be used as a reference book for AI software and hardware designers and developers, and can also be used as a teaching supplement for undergraduate and graduate students in general colleges and universities majoring in intelligent science and technology, computer science and technology, etc.

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