Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 3rd إصدار

 

 

Discover the Power of Deep Learning with Python

Introduction

Have you ever wondered how machines can learn from data? Deep learning is a technology that has revolutionized the field of machine learning. It allows programmers to use simple and efficient tools to create programs that can learn from data. In this bestselling book, you will learn how to use Python frameworks like Scikit-Learn, Keras, and TensorFlow to build intelligent systems.

What You Will Learn

This updated third edition of the book by Aurélien Géron covers a range of techniques, starting from simple linear regression to deep neural networks. The book uses concrete examples, minimal theory, and production-ready Python frameworks to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You will learn how to:

1. Track an Example ML Project End to End

You will use Scikit-learn to track an example machine learning project from start to finish. This will help you understand the entire process of building a machine learning model.

2. Explore Several Models

You will explore several models, including support vector machines, decision trees, random forests, and ensemble methods. This will help you understand the strengths and weaknesses of each model.

3. Exploit Unsupervised Learning Techniques

You will learn how to exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection. This will help you find patterns in data that are not labeled.

4. Dive into Neural Net Architectures

You will dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers. This will help you understand how neural networks work and how they can be used to solve complex problems.

5. Build and Train Neural Nets

You will use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning. This will help you understand how to apply deep learning to real-world problems.

Conclusion

This book is perfect for programmers who want to learn about deep learning but have little to no experience with the technology. The book uses simple language and practical examples to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this book, you will be able to build your own intelligent systems and take your programming skills to the next level.