NVIDIA's Full-Color Guide to Deep Learning: 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. Explore and master core concepts: perceptrons,
gradient-based learning, sigmoid neurons, and back propagation See
how DL frameworks make it easier to develop more complicated and
useful neural networks Discover how convolutional neural networks
(CNNs) revolutionize image classification and analysis Apply
recurrent neural networks (RNNs) and long short-term memory (LSTM)
to text and other variable-length sequences Master NLP with
sequence-to-sequence networks and the Transformer architecture
Build applications for natural language translation and image
captioning 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.
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