本书《动手学机器学习》通过使用Scikit-Learn、Keras和TensorFlow等工具，提供了丰富的实践案例与教程，帮助读者掌握现代机器学习技术。
Recent advancements in deep learning have significantly propelled the field of machine learning forward. Now, even those with little technical knowledge can utilize straightforward and effective tools to create programs that learn from data. This practical guide demonstrates how to achieve this through concrete examples, minimal theory, and two production-ready Python frameworks: Scikit-Learn and TensorFlow.
Author Aurélien Géron provides an intuitive understanding of the concepts and tools necessary for building intelligent systems. You will explore a variety of techniques starting with simple linear regression and progressing towards deep neural networks. Each chapter includes exercises to reinforce your learning, requiring only programming experience as a prerequisite.
* Navigate through the machine learning landscape, particularly focusing on neural nets.
* Use Scikit-Learn to follow an example project from start to finish in machine learning.
* Examine several training models including support vector machines, decision trees, random forests, and ensemble methods.
* Utilize TensorFlow to build and train neural networks.
* Delve into various neural network architectures such as convolutional nets, recurrent nets, and deep reinforcement learning.
* Learn techniques for both training and scaling deep neural networks.
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