Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Nlp, and Transformers Using Tensorflow (Paperback)

Magnus Ekman




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.

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- 來自卡爾科技學院Bren教授和NVIDIA機器學習研究總監Anima Anandkumar博士的前言

- 來自NVIDIA深度學習研究所所長Craig Clawson博士的前言


在介紹了深度神經網絡的基本構建模塊(如人工神經元和全連接、卷積和循環層)之後,Magnus Ekman展示了如何使用它們構建先進的架構,包括Transformer。他描述了這些概念如何用於構建計算機視覺和自然語言處理(NLP)的現代網絡,包括Mask R-CNN、GPT和BERT。他還解釋了自然語言翻譯和生成圖像自然語言描述的系統。

在整個過程中,Ekman使用了簡潔、註釋良好的TensorFlow代碼示例。相應的PyTorch示例可在線上獲得,因此本書涵蓋了工業和學術界中兩個主要的Python DL庫。他最後介紹了神經架構搜索(NAS),探討了重要的道德問題並提供了進一步學習的資源。

- 探索並掌握核心概念:感知器、基於梯度的學習、S型神經元和反向傳播
- 看看DL框架如何使開發更複雜、更有用的神經網絡變得更容易
- 發現卷積神經網絡(CNN)如何革新圖像分類和分析
- 將循環神經網絡(RNN)和長短期記憶(LSTM)應用於文本和其他可變長度序列
- 通過序列到序列網絡和Transformer架構掌握NLP
- 構建自然語言翻譯和圖像標題生成的應用


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Magnus Ekman, Ph.D., is a director of architecture at NVIDIA Corporation. His doctorate is in computer engineering, and he is the inventor of multiple patents. He was first exposed to artificial neural networks in the late nineties in his native country, Sweden. After some dabbling in evolutionary computation, he ended up focusing on computer architecture and relocated to Silicon Valley, where he lives with his wife Jennifer, children Sebastian and Sofia, and dog Babette. He previously worked with processor design and R&D at Sun Microsystems and Samsung Research America, and has been involved in starting two companies, one of which (Skout) was later acquired by The Meet Group, Inc. In his current role at NVIDIA, he leads an engineering team working on CPU performance and power efficiency for system on chips targeting the autonomous vehicle market.

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.


Magnus Ekman博士是NVIDIA Corporation的架構總監。他的博士學位是在計算機工程領域,並且是多項專利的發明人。他在90年代末在他的祖國瑞典首次接觸到人工神經網絡。在一些進化計算的嘗試之後,他專注於計算機架構並遷居到矽谷,他與妻子Jennifer、兒子Sebastian和Sofia以及狗Babette一起生活。他曾在Sun Microsystems和Samsung Research America從事處理器設計和研發工作,並參與創辦了兩家公司,其中一家(Skout)後來被The Meet Group, Inc.收購。在他目前在NVIDIA的職位上,他領導一個工程團隊,致力於針對自動駕駛市場的系統芯片的CPU性能和功耗效率。