Hands-On One-shot Learning with Python

Jadon, Shruti, Garg, Ankush

  • 出版商: Packt Publishing
  • 出版日期: 2020-04-10
  • 售價: $1,770
  • 貴賓價: 9.5$1,682
  • 語言: 英文
  • 頁數: 156
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1838825460
  • ISBN-13: 9781838825461
  • 相關分類: Python程式語言
  • 海外代購書籍(需單獨結帳)

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商品描述

Key Features

  • Learn how you can speed up the deep learning process with one-shot learning
  • Use Python and PyTorch to build state-of-the-art one-shot learning models
  • Explore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learning

Book Description

One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this book, you'll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples.

Hands-On One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. The book begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you've got to grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you'll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence.

By the end of this book, you'll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models.

What you will learn

  • Get to grips with the fundamental concepts of one- and few-shot learning
  • Work with different deep learning architectures for one-shot learning
  • Understand when to use one-shot and transfer learning, respectively
  • Study the Bayesian network approach for one-shot learning
  • Implement one-shot learning approaches based on metrics, models, and optimization in PyTorch
  • Discover different optimization algorithms that help to improve accuracy even with smaller volumes of data
  • Explore various one-shot learning architectures based on classification and regression

Who this book is for

If you're an AI researcher or a machine learning or deep learning expert looking to explore one-shot learning, this book is for you. It will help you get started with implementing various one-shot techniques to train models faster. Some Python programming experience is necessary to understand the concepts covered in this book.

商品描述(中文翻譯)

主要特點


  • 學習如何使用單次學習來加速深度學習過程

  • 使用Python和PyTorch構建最先進的單次學習模型

  • 探索Siamese網絡、記憶增強神經網絡、模型不可知元學習和區分性k-shot學習等架構

書籍描述

單次學習一直是科學家們研究的熱門領域,他們試圖開發出模仿人類學習的認知機器。通過本書,您將通過實際示例探索單次學習的關鍵方法,例如基於度量、基於模型和基於優化的技術。

《使用Python進行實踐單次學習》將引導您探索和設計可以從一個或僅僅幾個訓練樣本中獲取關於對象的信息的深度學習模型。本書首先概述了深度學習和單次學習,然後介紹了您可以使用的不同方法,例如深度學習架構和概率模型。一旦您掌握了核心原則,您將探索使用PyTorch 1.x在Omniglot和MiniImageNet等數據集上進行單次學習的真實世界示例和實現。最後,您將探索基於生成建模的方法,並了解構建展示人類級智能的系統的關鍵考慮因素。

通過閱讀本書,您將熟悉不同的單次和少量樣本學習方法,並能夠使用它們來構建自己的深度學習模型。

您將學到什麼


  • 瞭解單次和少量樣本學習的基本概念

  • 使用不同的深度學習架構進行單次學習

  • 了解何時分別使用單次學習和轉移學習

  • 研究基於貝葉斯網絡的單次學習方法

  • 在PyTorch中實現基於度量、模型和優化的單次學習方法

  • 探索各種基於分類和回歸的單次學習架構

本書適合對象

如果您是一位AI研究人員、機器學習或深度學習專家,並希望探索單次學習,那麼本書適合您。它將幫助您開始實施各種單次技術以更快地訓練模型。需要一些Python編程經驗才能理解本書中涵蓋的概念。

作者簡介

Shruti Jadon is currently working as a Machine Learning Software Engineer at Juniper Networks, Sunnyvale and visiting Researcher at Rhode Island Hospital (Brown University). She has obtained her master's degree in Computer Science from University of Massachusetts, Amherst. Her research interests include deep learning architectures, computer vision, and convex optimization. In the past, she has worked at Autodesk, Quantiphi, SAP Labs, and Snapdeal.

Ankush Garg is currently working as a Software Engineer in the auto-translation team at Google, Mountain View. He has obtained his master's degree in Computer Science from the University of Massachusetts, Amherst and Bachelor's at NSIT, Delhi. His research interests include language modeling, model compression, and optimization. In the past, he has worked as a Software Engineer at Amazon, India.

作者簡介(中文翻譯)

Shruti Jadon目前在Juniper Networks的機器學習軟體工程師,同時也是Rhode Island Hospital(布朗大學)的訪問研究員。她在麻省大學阿默斯特分校獲得了計算機科學碩士學位。她的研究興趣包括深度學習架構、計算機視覺和凸優化。過去,她曾在Autodesk、Quantiphi、SAP Labs和Snapdeal工作。

Ankush Garg目前在Google的自動翻譯團隊擔任軟體工程師,位於Mountain View。他在麻省大學阿默斯特分校獲得了計算機科學碩士學位,並在NSIT(德里)獲得了學士學位。他的研究興趣包括語言建模、模型壓縮和優化。過去,他曾在亞馬遜(印度)擔任軟體工程師。

目錄大綱

  1. Introduction to One-shot Learning
  2. Metrics-Based Methods
  3. Models-Based Methods
  4. Optimization-Based Methods
  5. Generative Modeling-Based Methods
  6. Conclusion and Other Approaches

目錄大綱(中文翻譯)


  1. 單次學習介紹

  2. 基於度量的方法

  3. 基於模型的方法

  4. 基於優化的方法

  5. 基於生成模型的方法

  6. 結論和其他方法