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Book Details
Format: pdf
Size: 31 MB
About this Book:
Through a series of recent 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 practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworksâScikit-Learn and TensorFlowâauthor AurĂ©lien GĂ©ron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. Youâll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what youâve learned, all you need is programming experience to get started.
Explore the machine learning landscape, particularly neural nets
Use Scikit-Learn to track an example machine-learning project end-to-end
Explore several training models, including support 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
For More Premium Graphics,Accounts,Freebies Visit >>> Forum.NulledPremium.com
Book Details
Format: pdf
Size: 31 MB
About this Book:
Through a series of recent 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 practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworksâScikit-Learn and TensorFlowâauthor AurĂ©lien GĂ©ron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. Youâll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what youâve learned, all you need is programming experience to get started.
Explore the machine learning landscape, particularly neural nets
Use Scikit-Learn to track an example machine-learning project end-to-end
Explore several training models, including support 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