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040			_cAKCL
082			_a006.31 _bGER-H3
100			_aGeron, Aurelien
245			_aHands-On Machine Learning with Scikit-Learn, Keras and TensorFlow: _bConcepts, Tools and Techniques to Build Intelligent Systems / _cby Aurelien Geron
250			_a3rd Ed.
260			_aCalifornia: _bO'Reilly Media Inc., _c2023
300			_ap. XXV,834
520			_aThrough 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 frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll 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 you’ve 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
650			_aComputer Science
650			_aArtificial Intelligence
650			_aMachine Learning
650			_aIntelligent Systems
942			_cBK
942			_2ddc
942			_2ddc
942			_2ddc _n0
999			_c40038 _d40038