|
|
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems機器學習實用指南 第三版
類型:
簡裝書
作者:
Géron, Aurélien
ISBN13:
9781098125974
出版于:
2022-10-18
|
詳細信息
類型:
簡裝書
語言:
英語(English)
作者:
Géron, Aurélien
頁數:
415 頁
出版于:
2022-10-18
ISBN13:
9781098125974
ISBN10:
1098125975
出版社:
O'Reilly Media
規格:
232 x 194 x 46 mm
重量:
1446 克
商品簡介
This best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
查看Google書籍信息
查看Google書籍信息
書評與摘要
Through a recent series of 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 bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. <p> With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started. <p> <ul> <li>Use Scikit-learn to track an example ML project end to end <li>Explore several models, including support vector machines, decision trees, random forests, and ensemble methods <li>Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection <li>Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers <li>Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning </ul>
