000			02112nam a2200229Ia 4500
003			NULRC
005			20250520103030.0
008			250520s9999 xx 000 0 und d
020			_a9780137470358
040			_cNULRC
050			_aQ 325.5 .E36 2022
100			_aEkman, Magnus _eauthor
245		0	_aLearning deep learning : _btheory and practice of neural networks, computer vision, natural language processing, and transformers using tensorflow / _cMagnus Ekman
260			_aBoston, Massachusetts : _bAddision-Wesley Publishing Company, _cc2022
300			_aliii, 688 pages : _bcolor illustrations ; _c24 cm
365			_bUSD56
504			_aIncludes index.
505			_aLearning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this text can be used for students with prior programming experience but with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Ekman shows how to use them to build advanced architectures, including the Transformer.
520			_a1. The Rosenblatt Perceptron -- 2. Gradient-Based Learning -- 3. Sigmoid neurons and backpropagation -- 4. Fully Connected networks applied to multiclass classification -- 5. Towards DL: Frameworks and networks Tweaks -- 6. Fully Connected Networks Applied to Regression -- 7. Convolutional Neural Networks Applied to Image Classification -- 8. Deeper CNNs and Pretrained Models -- 9. Predicting time sequences with recurrent neural networks -- 10. Long Short-term Memory -- 11. Text Autocompletion with LSTM and Beam Search -- 12. Neural Language Models and Word Embeddings -- 13. Word Embeddings from word2vec and GloVe -- 14. Sequence to sequence networks and natural Language Translation -- 15. Attention and the Transformer -- 16. One-to-many network for image captioning -- 17. Medley of Additional topics -- 18. Summary and Next steps.
650			_aMACHINE LEARNING
942			_2lcc _cBK
999			_c21844 _d21844