Dipanjan Sarkar, Nitin Panwar, Raghav Bali, Tamoghna Ghosh

Hands-On Transfer Learning with Python (eBook, ePUB)

Implement advanced deep learning and neural network models using TensorFlow and Keras

• Format: ePub

Produktbeschreibung

Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.

The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples.

The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).

By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.

Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 438
• Erscheinungstermin: 31. August 2018
• Englisch
• ISBN-13: 9781788839051
• Artikelnr.: 54095479

Autorenporträt

Dipanjan (DJ) Sarkar is a Data Scientist at Intel, leveraging data science, machine learning, and deep learning to build large-scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He has been an analytics practitioner for several years now, specializing in machine learning, NLP, statistical methods, and deep learning. He is passionate about education and also acts as a Data Science Mentor at various organizations like Springboard, helping people learn data science. He is also a key contributor and editor for Towards Data Science, a leading online journal on AI and Data Science. He has also authored several books on R, Python, machine learning, NLP, and deep learning.