Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow

NVIDIA's Full-Color Guide to Deep Learning is a comprehensive guide to deep learning, ideal for developers, data scientists, analysts, and others with no prior machine learning or statistics experience. It covers core concepts, hands-on programming techniques, and applications in computer vision and natural language processing. The book includes code examples using TensorFlow with Keras and PyTorch, and an introduction to neural architecture search.

Format: Paperback / softback
Length: 752 pages
Publication date: 04 October 2021
Publisher: Pearson Education (US)

NVIDIA's comprehensive guide to deep learning, "Deep Learning: All You Need to Get Started and Get Results," aims to democratize AI knowledge and resources for a historic revolution. Written by Magnus Ekman, a Bren Professor at Caltech and Director of ML Research at NVIDIA, the book employs a practical approach, encouraging readers to consider applying deep learning techniques in their work.

Dr. Anima Anandkumar, Bren Professor at Caltech and Director of ML Research at NVIDIA, praises Ekman's learning technique, stating that it has proven pivotal to success. The author's straightforward approach fosters a sense of wonder and exploration, allowing readers to envision the potential advancements of deep learning.

Dr. Craig Clawson, Director of NVIDIA Deep Learning Institute, also contributes a foreword, highlighting deep learning's significance in today's machine learning and artificial intelligence landscape. "Learning Deep Learning" serves as a comprehensive guide to deep learning, catering to developers, data scientists, analysts, and individuals with or without prior machine learning or statistics experience.

The book begins by introducing the fundamental building blocks of deep neural networks, including artificial neurons, fully connected, convolutional, and recurrent layers. Ekman then demonstrates how these concepts are applied to build advanced architectures, such as the Transformer, for tasks like computer vision and natural language processing (NLP). He showcases examples of modern networks like Mask R-CNN, GPT, and BERT, which have revolutionized various AI domains. Ekman also delves into the development of natural language translators and image description systems, showcasing the practical applications of deep learning.

Throughout the book, Ekman provides concise and well-annotated code examples using TensorFlow with Keras, making the concepts accessible to a wide range of readers. The book's clear explanations, accompanied by visualizations and practical examples, ensure a solid understanding of deep learning principles and their implementation.

In conclusion, "Deep Learning: All You Need to Get Started and Get Results" by Magnus Ekman is a timely and valuable resource for anyone interested in diving into the world of deep learning. With its comprehensive coverage, practical approach, and accessible language, the book empowers individuals to participate in the historic revolution of AI and unlock its vast potential. Whether you are a developer, data scientist, analyst, or simply curious about the future of AI, this guide will provide you with the knowledge and skills needed to succeed in this rapidly evolving field.

Weight: 1134g
Dimension: 189 x 230 x 32 (mm)
ISBN-13: 9780137470358