Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Learning Deep Learning: Theory and Practice of Neural
Networks, Computer Vision, Natural Language Processing, and Transformers
Using TensorFlow (sách keo gáy bìa mềm)
Thể loại:Computers - Computer Science
Năm:2021
In lần thứ:1
Nhà xuát bản:Addison-Wesley Professional
Ngôn ngữ:english
Trang:747
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.
