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020			_a9781838647292
040			_aCIBESPAM MFL
041			_aeng
082			_a006.31 _bD269
100			_aDawani, Jay
245			_aHands-On Mathematics for deep learning.
260			_aBirmingham, UK _bPackt _c2020
300			_avii, 349 pages _btables, figures;
505			_aSection 1: Essential Mathematics for Deep Learning -1. Linear Algebra -2. Vector Calculus -3. Probability and Statistics -4. Optimization -5. Graph Theory Section 2: Essential Neural Networks -6. Linear Neural Networks -7. Feedforward Neural Networks -8. Regularization -9. Convolutional Neural Networks -10. Recurrent Neural Networks Section 3: Advanced Deep Learning Concepts Simplified -11. Attention Mechanisms -12. Generative Models -13. Transfer and Meta Learning -14. Geometric Deep Learning Other Books You May Enjoy
520			_aMost programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL.
650			_aMathematics
650			_aDeep Learning
650			_aNeural Networks
650			_aComputing
912			_c2023-12-20 _dPaúl Villacreses
913			_aTIC _bCC _dSCSAS
942			_2ddc _cBK
999			_c13024 _d13024