Books

Covering everything from the principles of large language models to creative applications and educational issues, this book is ideal for general readers, teachers, and parents who wish to build a foundation of knowledge about AI.

From search, perception, and learning to reasoning, this book establishes a knowledge framework for understanding artificial intelligence from a general education perspective, making it ideal for Chinese readers who wish to grasp the technical principles and limitations of the technology.

Learn how to develop AI agent applications through hands-on projects focused on the Kouzhi platform’s workflows, knowledge base, prompts, and database.

Integrate tools such as DeepSeek into your daily analysis workflow, focusing on prompt creation, data analysis tasks, and result validation; ideal for operations, business, and data professionals who want to learn by doing.

Focusing on tool invocation, MCP, knowledge and memory, evaluation, and multi-agent collaboration, this course teaches you how to turn Python prototypes into verifiable agent applications. It is suitable for practitioners with a background in programming.

Starting with the diffusion of AI and biotechnology, this book explores technological capabilities, power structures, and governance challenges; it is suitable for Chinese-speaking readers who wish to move beyond a purely tool-oriented perspective.

From data, evaluation, and deployment to continuous monitoring, gain an understanding of the engineering trade-offs involved in implementing machine learning systems. Ideal for engineers and technical leads who want to turn offline models into long-term, maintainable services.

A technical reference book on Chinese large language models, written by a research team at Renmin University of China, is intended for readers with a foundation in deep learning who wish to establish a systematic technical framework.

Move from the fundamentals of deep learning to image, text, and generative AI, and build practical skills using Keras 3 and mainstream frameworks. Suitable for readers with a basic understanding of Python who want to systematically expand their knowledge of machine learning models.

Focusing on models, strategies, and algorithms, this book provides a systematic introduction to supervised, unsupervised, deep, and reinforcement learning. It is ideal for technical readers who are committed to strengthening their mathematical foundations.

This course bridges the gap between understanding AI technology and gaining insights into user needs, model evaluation, and product design. It is ideal for product managers and cross-functional teams responsible for implementing AI solutions.

By examining the history, philosophy, and technological pathways of artificial intelligence, this book helps readers understand the origins of the boom in large language models. It is ideal for readers seeking to develop a comprehensive understanding of the field.

Through Li Feifei’s personal growth and research journey, this book explores the development of computer vision, data, and modern AI, while also discussing the human condition and responsibilities in the context of technological progress.

Aimed at business leaders and technical teams, this session introduces the preparation, development, and implementation processes for large-scale model projects, focusing on business scenarios, solution selection, and budget allocation.

Intended for beginners in application development with a basic understanding of Python, this book focuses on developing an engineering mindset around text generation, question-answering, summarization, and RAG. When reading, be sure to distinguish between stable concepts and interfaces that are subject to change.

This course connects calculus, linear algebra, and probability and statistics to AI problems, making it ideal for strengthening your mathematical foundation while learning algorithms and testing your understanding through Python exercises.

Zhou Zhihua’s introductory textbook on machine learning—covering topics from model evaluation to classic algorithms—is ideal for learners who want to solidify their theoretical foundation and understand the logic behind model selection.