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020			_a9781484265123
024	7		_a10.1007/978-1-4842-6513-0 _2doi
040			_dWaSeSS _cAIMIT LIBRARY
050		4	_aQ334-342
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072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
082	0	4	_a006.32 _21 _bYALO
092			_aEBOOK
100	1		_aYalcin, Orhan Gazi. _931958
245	1	0	_aApplied Neural Networks with TensorFlow 2 : _hAPI oriented deep learning with python / _cby Orhan Gazi Yalcin
250			_a1st ed.
260			_aBerkeley, CA : _bApress : _bImprint: Apress, _c2021.
300			_axix, 295 p. _c23.4 cm.
347			_atext file _bPDF _2rda
505	0		_aChapter 1: Introduction -- Chapter 2: Introduction to Machine Learning -- Chapter 3: Deep Learning and Neutral Networks Overview -- Chapter 4: Complimentary Libraries to TensorFlow 2.x -- Chapter 5: A Guide to TensorFlow 2.0 and Deep Learning Pipeline -- Chapter 6: Feedfoward Neutral Networks -- Chapter 7: Convolutional Neural Networks -- Chapter 8: Recurrent Neural Networks -- Chapter 9: Natural Language Processing -- Chapter 10: Recommender Systems -- Chapter 11: Auto-Encoders -- Chapter 12: Generative Adversarial Networks.
506			_aRequires an SPL library card.
520			_aImplement deep learning applications using TensorFlow while learning the "why" through in-depth conceptual explanations. You'll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy-others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers. You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you'll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs. Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, you'll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively. You will: Compare competing technologies and see why TensorFlow is more popular Generate text, image, or sound with GANs Predict the rating or preference a user will give to an item Sequence data with recurrent neural networks.
538			_aMode of access: World Wide Web.
650		0	_aArtificial intelligence _931959
650		0	_aMachine Learning _931960
650		0	_aDeep Learning and Neural networks _931961
650		0	_aComplementary Libraries to tensor flow _931962
655		7	_aElectronic books. _2local _931963
710	2		_aSpringerLink (Online service) _931964
710	2		_aO'Reilly (Firm) _931965
710	2	0	_aSerials Solutions _931966
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9781484265123
776	0	8	_iPrinted edition: _z9781484265147
856	4	0	_yView this electronic item in O'Reilly Online Learning: Academic/Public Library Edition. _uhttps://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484265130/?ar _zAn e-book available through full-text database.
942			_2ddc _cBK _e1st _k006.32 YALO
999			_c222637 _d222637
999			_b03678066