000			02837nam a22002897a 4500
001			21994071
003			IIITD
005			20240504150404.0
008			210414s2021 mau 000 0 eng
010			_a 2021937264
020			_a9789356063976
040			_aDLC _beng _erda _cDLC
042			_apcc
082			_a006.31 _bEKM-L
100	1		_aEkman, Magnus
245	1	0	_aLearning deep learning : _btheory and practice of neural networks, computer vision, natural language processing, and transformers using tensorflow _cby Magnus Ekman.
260			_aNew Delhi : _bPearson, _c©2023
263			_a2108
300			_alv, 554 p. : _bill. ; _c23 cm.
505			_tChapter 1. The Rosenblatt Perceptron _tChapter 2. Gradient-Based Learning _tChapter 3. Sigmoid Neurons and Backpropagation _tChapter 4. Fully Connected Networks Applied to Multiclass Classification _tChapter 5. Toward DL: Frameworks and Network Tweaks _tChapter 6. Fully Connected Networks Applied to Regression _tChapter 7. Convolutional Neural Networks Applied to Image Classification _tChapter 8. Deeper CNNs and Pretrained Models _tChapter 9. Predicting Time Sequences with Recurrent Neural Networks _tChapter 10. Long Short-Term Memory _tChapter 11. Text Autocompletion with LSTM and Beam Search _tChapter 12. Neural Language Models and Word Embeddings _tChapter 13. Word Embeddings from word2vec and GloVe _tChapter 14. Sequence-to-Sequence Networks and Natural Language Translation _tChapter 15. Attention and the Transformer _tChapter 16. One-to-Many Network for Image Captioning _tChapter 17. Medley of Additional Topics _tChapter 18. Summary and Next Steps
520			_a"Deep learning is at the heart of many of today's most exciting advances in machine learning and artificial intelligence. Pioneering applications at companies like Tesla, Google, and Facebook are now being followed by massive investments in fields ranging from finance to healthcare. Now, there's a complete guide to deep learning with TensorFlow, the #1 Python library for building these breakthrough applications. Magnus Ekman illuminates both the underlying concepts and the hands-on programming techniques you'll need, even if you have no machine learning experience. Throughout, you'll find concise, well-annotated code examples using TensorFlow and the Keras API; for comparison and easy migration between frameworks, complementary examples in PyTorch are provided online. Ekman also explains enough of the mathematics to help newcomers grasp how deep learning actually works. The guide concludes by previewing emerging trends in deep learning, and exploring the challenging ethical issues surrounding its use"--
650			_aDeep Learning
650			_aNeural Networks
906			_a0 _bibc _corignew _d2 _eepcn _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c172598 _d172598