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.

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?

  Who This Book Is For

  Machine learning engineers, data scientists, AI practitioners, software developers, and engineers interested in deep learning

  Provides explanations on sequence models and their analysis using various datasets Covers generative models and transformers, demonstrating their applications Provides hands-on projects that guide you in understanding the processes of model creation, fine-tuning, and testing

  Autorentext

  Harsh Bhasin is a researcher and practitioner. He has completed his PhD in Diagnosis and Conversion Prediction of Mild Cognitive Impairment Using Machine Learning from Jawaharlal Nehru University, New Delhi. He worked as a Deep Learning consultant for various firms and taught at various Universities, including Jamia Hamdard, and DTU. He is currently associated with Bennett University. Harsh has authored 11 books, including Programming in C# and Algorithms. He has authored more than 40 papers that have been published in international conferences and renowned journals, including Alzheimer's and Dementia, Soft Computing, Springer, BMC Medical Informatics & Decision Making, AI & Society, etc. He is the reviewer of a few renowned journals and has been the editor of a few special issues. He has been a recipient of Visvesvaraya Fellowship, Ministry of Electronics and Information Technology. His areas of expertise include Deep Learning, Algorithms and Medical Imaging. Apart from his professional endeavours, he is deeply interested in Hindi Poetry: the progressive era and Hindustani Classical Music: percussion instruments.

  Inhalt

Chapter 1: Revisiting Machine Learning.- Chapter 2: Introduction to Deep Learning.- Chapter 3: Neural Networks.- Chapter 4: Training Deep Networks.- Chapter 5: Hyperparameter Tuning.- Chapter 6: Convolutional Neural Networks: Part 1.- Chapter 7: Convolutional Neural Networks : Part 2.- Chapter 8: Transfer Learning.- Chapter 9: Recurrent Neural Networks.- Chapter 10: LSTM and GRU.- Chapter 11: Autoencoders.- Chapter 12: Introduction to Generative Models.- Appendices A-G.