Learning deep learning : theory and practice of neural networks, computer vision, natural language processing, and transformers using tensorflow
Ekman, Magnus
Learning deep learning : theory and practice of neural networks, computer vision, natural language processing, and transformers using tensorflow by Magnus Ekman. - New Delhi : Pearson, ©2023 - lv, 554 p. : ill. ; 23 cm.
Chapter 1. The Rosenblatt Perceptron Chapter 2. Gradient-Based Learning Chapter 3. Sigmoid Neurons and Backpropagation Chapter 4. Fully Connected Networks Applied to Multiclass Classification Chapter 5. Toward DL: Frameworks and Network Tweaks Chapter 6. Fully Connected Networks Applied to Regression Chapter 7. Convolutional Neural Networks Applied to Image Classification Chapter 8. Deeper CNNs and Pretrained Models Chapter 9. Predicting Time Sequences with Recurrent Neural Networks Chapter 10. Long Short-Term Memory Chapter 11. Text Autocompletion with LSTM and Beam Search Chapter 12. Neural Language Models and Word Embeddings Chapter 13. Word Embeddings from word2vec and GloVe Chapter 14. Sequence-to-Sequence Networks and Natural Language Translation Chapter 15. Attention and the Transformer Chapter 16. One-to-Many Network for Image Captioning Chapter 17. Medley of Additional Topics Chapter 18. Summary and Next Steps
"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"--
9789356063976
2021937264
Deep Learning
Neural Networks
006.31 / EKM-L
Learning deep learning : theory and practice of neural networks, computer vision, natural language processing, and transformers using tensorflow by Magnus Ekman. - New Delhi : Pearson, ©2023 - lv, 554 p. : ill. ; 23 cm.
Chapter 1. The Rosenblatt Perceptron Chapter 2. Gradient-Based Learning Chapter 3. Sigmoid Neurons and Backpropagation Chapter 4. Fully Connected Networks Applied to Multiclass Classification Chapter 5. Toward DL: Frameworks and Network Tweaks Chapter 6. Fully Connected Networks Applied to Regression Chapter 7. Convolutional Neural Networks Applied to Image Classification Chapter 8. Deeper CNNs and Pretrained Models Chapter 9. Predicting Time Sequences with Recurrent Neural Networks Chapter 10. Long Short-Term Memory Chapter 11. Text Autocompletion with LSTM and Beam Search Chapter 12. Neural Language Models and Word Embeddings Chapter 13. Word Embeddings from word2vec and GloVe Chapter 14. Sequence-to-Sequence Networks and Natural Language Translation Chapter 15. Attention and the Transformer Chapter 16. One-to-Many Network for Image Captioning Chapter 17. Medley of Additional Topics Chapter 18. Summary and Next Steps
"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"--
9789356063976
2021937264
Deep Learning
Neural Networks
006.31 / EKM-L