Learning Deep Learning (Paperback)
Idioma: inglés
Editorial: Pearson Education (US), Boston, 2021
- Tapa blanda
- Nuevo
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Vendedor de IberLibro desde 12 de octubre de 2005
Condición: Nuevo
EUR 76,70
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Paperback. 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. Samples Preview sample pages from Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow > 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) revolutionise 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 Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …
N° de ref. del artículo 9780137470358
- Título
- Learning Deep Learning (Paperback)
- Autor
- Magnus Ekman
- Editorial
- Pearson Education (US), Boston
- Año de publicación
- 2021
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 0137470355
- ISBN 13
- 9780137470358
NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results
-- 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.
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
As the Deep Learning (DL) field exploded the past few years, fueled by NVIDIA's GPU technology and CUDA, Dr. Ekman found himself in the middle of a company expanding beyond computer graphics into becoming a deep learning (DL) powerhouse. As a part of that journey, he challenged himself to stay up-to-date with the most recent developments in the field. He considers himself to be an educator, and in the process of writing Learning Deep Learning ( LDL), he partnered with the NVIDIA Deep Learning Institute (DLI), which offers hands-on training in AI, accelerated computing, and accelerated data science. He is thrilled about DLI's plans to add LDL to its existing portfolio of self-paced online courses, live instructor-led workshops, educator programs, and teaching kits.
“Acerca de” puede pertenecer a otra edición de este título.
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