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020			_a9789352139026
082			_a006.32 _bWEI
100			_aWeidman, Seth _92157
245			_aDeep learning from scratch: building with Python from first principles
260			_bO'Reilly Media _aSebastopol _c2019
300			_axiv, 235 p.
365			_aINR _b925.00
520			_aDescription All Indian Reprints of O'Reilly are printed in Grayscale With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects. This book provides: • Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks • Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework • Working implementations and clear-cut explanations of convolutional and recurrent neural networks • Implementation of these neural network concepts using the popular PyTorch framework
650			_aMachine learning _92343
650			_aPython (Computer program language) _92393
650			_aNeural networks (Computer science) _92344
650			_aArtificial intelligence _91478
942			_2ddc _cBK