Hands-On Machine Learning with Scikit-Learn and TensorFlow

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Description

Through a series of contemporary breakthroughs, deep learning has boosted all of the 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 practical book shows you how., By the usage of concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-creator Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You can learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter that can assist you apply what you’ve learned, all you want is programming experience to get started., Explore the machine learning landscape, particularly neural nets Use scikit-learn how to track an example machine-learning project end-to-end Explore several training models, including improve vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring over the top machine learning theory or algorithm details

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