HomeTALK AI TVLearning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural...

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


Price: $74.99 - $54.25
(as of Sep 15, 2024 21:44:48 UTC – Details)


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 propagationSee how DL frameworks make it easier to develop more complicated and useful neural networksDiscover how convolutional neural networks (CNNs) revolutionize image classification and analysisApply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequencesMaster NLP with sequence-to-sequence networks and the Transformer architectureBuild 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.


From the Publisher

You Will Learn About

Artificial NeuronArtificial Neuron

Dataset NetworkDataset Network

Network TranslatorNetwork Translator

Image Caption NetworkImage Caption Network

Perceptron and other artificial neurons

They are the fundamental building blocks of deep neural networks that have enabled the DL revolution.

Fully connected feedforward networks and convolutional networks

You will apply these networks to solve practical problems, such as predicting housing prices based on a large number of variables or identifying to which category an image belongs.

Ways to represent words from a natural language using an encoding

Encoding captures some of the semantics of the encoded words. Encodings are used together with a recurrent neural network to create a neural-based natural language translator. This translator can automatically translate simple sentences from English to French or other similar languages.

Building an image-captioning network

Networks that combines image and language processing. This network takes an image as an input and automatically generates a natural language description of the image.

Publisher ‏ : ‎ Addison-Wesley Professional; 1st edition (August 17, 2021)
Language ‏ : ‎ English
Paperback ‏ : ‎ 752 pages
ISBN-10 ‏ : ‎ 0137470355
ISBN-13 ‏ : ‎ 978-0137470358
Item Weight ‏ : ‎ 2.15 pounds
Dimensions ‏ : ‎ 7.3 x 1.1 x 9 inches

LEAVE A REPLY

Please enter your comment!
Please enter your name here

Must Read

🔥*NEW* IDEOGRAM 2.0 JUST CHANGED THE GAME!🔥

Ideogram 2.0 https://bit.ly/ideogramPA (Get 100 Extra Prompts by signing up via my link) 100 Ideogram PROMPTS ... source

How to use Ideogram Ai: for Beginners

How to use Ideogram Ai: for Beginners Unleash your creativity with Ideogram, the FREE AI art generator! Whether you're a ... source

Something good is coming from OpenAi… #openai #strawberry #chatgpt

Whispers are spreading... something amazing is just around the corner! Let's see... #openai #strawberry #chatgpt Resources: ... source