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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 All You Need to Get Started and Get Results
"To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals."
-- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA
"Ekman uses a learning technique that in our experience has proven pivotal to success―asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us."
-- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute
Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience.
After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images.
Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others.
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
"To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals."
-- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA
"Ekman uses a learning technique that in our experience has proven pivotal to success―asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us."
-- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute
Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience.
After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images.
Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others.
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
752 pages, Paperback
Published August 17, 2021
48 people are currently reading
100 people want to read
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Displaying 1 - 2 of 2 reviews
September 19, 2024
The quote "Should we even be doing this?" made me laugh.
Otherwise, this book frightened me.
TBH, it is honestly some more of what I was really into when I was this little eight-year old obsessing over her little Tamagotchi.
I recognised a great deal of what was in the book from when I began to look into its subject when I was into Digimon. (That series ended three years ago according to my last Google search.)
So that is what this book made me think of, really, since in the anime, some of the kids were in, maybe, a computer lab setting.
That is what I thought of this book!
Otherwise, this book frightened me.
TBH, it is honestly some more of what I was really into when I was this little eight-year old obsessing over her little Tamagotchi.
I recognised a great deal of what was in the book from when I began to look into its subject when I was into Digimon. (That series ended three years ago according to my last Google search.)
So that is what this book made me think of, really, since in the anime, some of the kids were in, maybe, a computer lab setting.
That is what I thought of this book!
April 29, 2022
A great book about deep learning. It describes the toolset and many hot topics in deep learning. Another great resource for deep learning online is d2l.ai but I would say this book is a personal pick from my perspective.
Displaying 1 - 2 of 2 reviews