Bhasin H. - Hands-on Deep Learning [2024, PDF/EPUB, ENG]
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Hands-on Deep Learning
Год издания: 2024 Автор: Bhasin H. Издательство: Apress ISBN: 979-8-8688-1035-0 Язык: Английский Формат: PDF/EPUB Качество: Издательский макет или текст (eBook) Интерактивное оглавление: Да Количество страниц: 373 Описание: This book discusses Deep Learning, from its fundamental principles to its practical applications, with hands-on exercises and coding. It focuses on Deep Learning techniques and shows how to apply them across a wide range of practical scenarios. The book begins with an introduction to the core concepts of Deep Learning. It delves into topics such as transfer learning, multi-task learning, and end-to-end learning, providing insights into various Deep Learning models and their real-world applications. Next, it covers neural networks, progressing from single-layer perceptrons to multi-layer perceptrons, and solving the complexities of backpropagation and gradient descent. It explains optimizing model performance through effective techniques, addressing key considerations such as hyperparameters, bias, variance, and data division. It also covers convolutional neural networks (CNNs) through two comprehensive chapters, covering the architecture, components, and significance of kernels implementing well-known CNN models such as AlexNet and LeNet. It concludes with exploring autoencoders and generative models such as Hopfield Networks and Boltzmann Machines, applying these techniques to a diverse set of practical applications. These applications include image classification, object detection, sentiment analysis, COVID-19 detection, and ChatGPT. The Deep Learning methods extract the appropriate features and select the most important ones without explicitly stating which one to use. Moreover, Deep Learning generally results in better performance provided that a sufficient amount of data is given as input to the model. They use state-of-the-art optimization methods and make appropriate use of the hardware. Formally, Deep Learning may be defined as follows: Deep-learning methods are representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level. By the end of this book, you will have gained a thorough understanding of Deep Learning, from its fundamental principles to its innovative applications, enabling you to apply this knowledge to solve a wide range of real-world problems. What You Will Learn: - What are deep neural networks? - What is transfer learning, multi-task learning, and end-to-end learning? - What are hyperparameters, bias, variance, and data division? - What are CNN and RNN? ОглавлениеAbout the Author xiiiAbout the Technical Reviewers xv Acknowledgments xix Chapter 1: Revisiting Machine Learning 1 Chapter 2: Introduction to Deep Learning 43 Chapter 3: Neural Networks 59 Chapter 4: Training Deep Networks 111 Chapter 5: Hyperparameter Tuning 133 Chapter 6: Convolutional Neural Networks: I 157 Chapter 7: Convolutional Neural Network: II 185 Chapter 8: Transfer Learning 207 Chapter 9: Recurrent Neural Network 225 Chapter 10: Gated Recurrent Unit and Long Short-Term Memory 257 Chapter 11: Autoencoders 287 Chapter 12: Introduction to Generative Models 307 Appendix A: Classifying The Simpsons Characters 323 Appendix B: Face Detection 331 Appendix C: Sentiment Classification Revisited 335 Appendix D: Predicting Next Word 343 Appendix E: COVID Classification 347 Appendix F: Alzheimer’s Classification 351 Appendix G: Music Genre Classification Using MFCC and Convolutional Neural Network 355 Index 359
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