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NVIDIA’s Full-Shade Information to Deep Studying: All You Have to Get Began and Get Outcomes
“To allow everybody to be a part of this historic revolution requires the democratization of AI data and sources. This ebook is well timed and related in direction of conducting these lofty objectives.”
— From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Analysis, NVIDIA
“Ekman makes use of a studying method that in our expertise has confirmed pivotal to success―asking the reader to consider utilizing DL methods in apply. His easy strategy is refreshing, and he permits the reader to dream, only a bit, about the place DL might but take us.”
— From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Studying Institute
Deep studying (DL) is a key element of immediately’s thrilling advances in machine studying and synthetic intelligence. Studying Deep Studying is an entire information to DL. Illuminating each the core ideas and the hands-on programming methods wanted to succeed, this ebook is right for builders, information scientists, analysts, and others–including these with no prior machine studying or statistics expertise.
After introducing the important constructing blocks of deep neural networks, akin to synthetic neurons and absolutely related, convolutional, and recurrent layers, Magnus Ekman reveals learn how to use them to construct superior architectures, together with the Transformer. He describes how these ideas are used to construct fashionable networks for pc imaginative and prescient and pure language processing (NLP), together with Masks R-CNN, GPT, and BERT. And he explains how a pure language translator and a system producing pure language descriptions of photographs.
All through, Ekman gives concise, well-annotated code examples utilizing TensorFlow with Keras. Corresponding PyTorch examples are offered on-line, and the ebook thereby covers the 2 dominating Python libraries for DL utilized in trade and academia. He concludes with an introduction to neural structure search (NAS), exploring necessary moral points and offering sources for additional studying.
Discover and grasp core ideas: perceptrons, gradient-based studying, sigmoid neurons, and again propagationSee how DL frameworks make it simpler to develop extra sophisticated and helpful neural networksDiscover how convolutional neural networks (CNNs) revolutionize picture classification and analysisApply recurrent neural networks (RNNs) and lengthy short-term reminiscence (LSTM) to textual content and different variable-length sequencesMaster NLP with sequence-to-sequence networks and the Transformer architectureBuild functions for pure language translation and picture captioning
NVIDIA’s invention of the GPU sparked the PC gaming market. The corporate’s pioneering work in accelerated computing–a supercharged type of computing on the intersection of pc graphics, high-performance computing, and AI–is reshaping trillion-dollar industries, akin to transportation, healthcare, and manufacturing, and fueling the expansion of many others.
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From the Writer
You Will Be taught About
Perceptron and different synthetic neurons
They’re the basic constructing blocks of deep neural networks which have enabled the DL revolution.
Totally related feedforward networks and convolutional networks
You’ll apply these networks to unravel sensible issues, akin to predicting housing costs primarily based on a lot of variables or figuring out to which class a picture belongs.
Methods to signify phrases from a pure language utilizing an encoding
Encoding captures a number of the semantics of the encoded phrases. Encodings are used along with a recurrent neural community to create a neural-based pure language translator. This translator can routinely translate easy sentences from English to French or different comparable languages.
Constructing an image-captioning community
Networks that mixes picture and language processing. This community takes a picture as an enter and routinely generates a pure language description of the picture.
Writer : Addison-Wesley Skilled
Publication date : Aug. 17 2021
Version : 1st
Language : English
Print size : 752 pages
ISBN-10 : 0137470355
ISBN-13 : 978-0137470358
Merchandise weight : 975 g
Dimensions : 18.54 x 2.79 x 22.86 cm
Finest Sellers Rank: #217,534 in Books (See Prime 100 in Books) #14 in Workplace Certifications #15 in Fuzzy Logic Algorithms #22 in Laptop Networks
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