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

Magnus Ekman

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

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商品描述

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.

商品描述(中文翻譯)

NVIDIA全彩深度學習指南：一切你需要入門並獲得成果的指南

使每個人都能參與這場歷史性的革命，需要人工智慧知識和資源的民主化。這本書對實現這些崇高目標具有及時性和相關性。
- 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

Ekman使用了一種在我們的經驗中被證明對成功至關重要的學習技巧-要求讀者思考如何在實踐中應用深度學習技術。他直接的方法令人耳目一新，並允許讀者稍微幻想一下深度學習可能帶給我們的未來。
- 來自NVIDIA深度學習研究所所長Craig Clawson博士的前言

深度學習（DL）是當今機器學習和人工智慧領域中令人興奮的進展的關鍵組成部分。《學習深度學習》是一本完整的DL指南，既闡明了核心概念，又介紹了成功所需的實踐編程技巧。本書非常適合開發人員、數據科學家、分析師和其他人士，包括那些沒有機器學習或統計學經驗的人。

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

NVIDIA通過GPU的發明引發了個人電腦遊戲市場的繁榮。該公司在加速計算方面的開創性工作（一種在計算機圖形、高性能計算和人工智慧交叉領域的超級計算形式）正在重塑交通、醫療保健和製造等價值數萬億美元的行業，並推動其他行業的增長。

- 註冊您的書籍以便方便地獲取下載、更新和/或更正。詳情請參閱書中內容。

作者簡介

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.

作者簡介(中文翻譯)

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。

隨著深度學習（DL）領域在過去幾年中的爆發，受到NVIDIA的GPU技術和CUDA的推動，Ekman博士發現自己處於一家公司的中心，該公司正在從電腦圖形擴展到成為深度學習（DL）強大的力量。作為這個旅程的一部分，他挑戰自己保持與該領域最新發展的同步。他自認為是一位教育者，在撰寫《學習深度學習》（LDL）的過程中，他與NVIDIA深度學習研究所（DLI）合作，該研究所提供AI、加速計算和加速數據科學的實踐培訓。他對DLI計劃將《學習深度學習》（LDL）添加到其現有的自學在線課程、現場指導工作坊、教育者計劃和教學套件中感到興奮。

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