000			02185nam a2200229Ia 4500
003			NULRC
005			20250520102909.0
008			250520s9999 xx 000 0 und d
020			_a9781491962299
040			_cNULRC
050			_aQ 325.5 .G47 2017
100			_aGéron, Aurélien _eauthor
245		0	_aHands-on machine learning with Scikit-Learn and TensorFlow : _bconcepts, tools, and techniques to build intelligent systems / _cAurélien Géron
260			_aSebastopol, California : _bO'Reilly Media, Incorporated, _cc2017
300			_axx, 547 pages : _billustrations ; _c24 cm.
365			_bUSD33.29
504			_aIncludes index.
505			_aThe fundamentals of machine learning. The machine learning landscape ; End-to-end machine learning project ; Classification ; Training models ; Support vector machines ; Decision trees ; Ensemble learning and random forests ; Dimensionality reduction -- Neural networks and deep learning. Up and running with TensorFlow ; Introduction to artificial neural networks ; Training deep neural nets ; Distributing TensorFlow across devices and servers ; Convolutional neural networks ; Recurrent neural networks ; Autoencoders ; Reinforcement learning -- Exercise solutions -- Machine learning project checklist -- SVM dual problem -- Autodiff -- Other popular ANN architectures.
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.
650			_aMACHINE LEARNING
942			_2lcc _cBK
999			_c18192 _d18192