Hands-On Meta Learning with Python: Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow (Paperback)
Sudharsan Ravichandiran
- 出版商: Packt Publishing
- 出版日期: 2018-12-28
- 售價: $1,380
- 貴賓價: 9.5 折 $1,311
- 語言: 英文
- 頁數: 226
- 裝訂: Paperback
- ISBN: 1789534208
- ISBN-13: 9781789534207
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相關分類:
Python、程式語言、DeepLearning、TensorFlow
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相關翻譯:
Python 元學習 : 通用人工智能的實現 (Hands-On Meta Learning with Python: Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow) (簡中版)
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相關主題
商品描述
Explore a diverse set of meta-learning algorithms and techniques to enable human-like cognition for your machine learning models using various Python frameworks
Key Features
- Understand the foundations of meta learning algorithms
- Explore practical examples to explore various one-shot learning algorithms with its applications in TensorFlow
- Master state of the art meta learning algorithms like MAML, reptile, meta SGD
Book Description
Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster.
Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning.
By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.
What you will learn
- Understand the basics of meta learning methods, algorithms, and types
- Build voice and face recognition models using a siamese network
- Learn the prototypical network along with its variants
- Build relation networks and matching networks from scratch
- Implement MAML and Reptile algorithms from scratch in Python
- Work through imitation learning and adversarial meta learning
- Explore task agnostic meta learning and deep meta learning
Who this book is for
Hands-On Meta Learning with Python is for machine learning enthusiasts, AI researchers, and data scientists who want to explore meta learning as an advanced approach for training machine learning models. Working knowledge of machine learning concepts and Python programming is necessary.
Table of Contents
- Introduction to Meta Learning
- Face and Audio Recognition using Siamese Network
- Prototypical Network and its variants
- Building Matching and Relation Network using Tensorflow
- Memory Augmented Networks
- MAML and its variants
- Meta-SGD and Reptile ALgorithm
- Gradient Agreement as an Optimization Objective
- Recent Advancements and Next Steps
商品描述(中文翻譯)
探索多樣的元學習演算法和技術,以使用各種Python框架為您的機器學習模型實現類似人類認知的能力。
主要特點:
- 瞭解元學習演算法的基礎知識
- 通過實際示例探索各種一次性學習演算法及其在TensorFlow中的應用
- 掌握MAML、reptile、meta SGD等最先進的元學習演算法
書籍描述:
元學習是機器學習中一個令人興奮的研究趨勢,它使模型能夠理解學習過程。與其他機器學習範式不同,元學習可以更快地從小數據集中學習。
《使用Python進行實踐元學習》首先解釋了元學習的基礎知識,幫助您理解學習如何學習的概念。您將通過在TensorFlow和Keras中實現它們來深入研究各種一次性學習演算法,如siamese、prototypical、relation和memory-augmented networks。隨著您閱讀本書的過程,您將深入研究MAML、Reptile和CAML等最先進的元學習演算法。然後,您將探索如何使用Meta-SGD快速學習,並發現如何使用CACTUs進行無監督學習的元學習。在最後幾章中,您將研究元學習的最新趨勢,如對抗性元學習、任務不可知元學習和元模仿學習。
通過閱讀本書,您將熟悉最先進的元學習演算法,並能夠為您的機器學習模型實現類似人類認知的能力。
您將學到什麼:
- 瞭解元學習方法、演算法和類型的基礎知識
- 使用siamese網絡構建語音和人臉識別模型
- 學習原型網絡及其變體
- 從頭開始構建匹配和關聯網絡
- 使用Python從頭實現MAML和Reptile演算法
- 進行模仿學習和對抗性元學習
- 探索任務不可知元學習和深度元學習
本書適合對機器學習熱衷、人工智能研究人員和數據科學家,他們希望探索元學習作為訓練機器學習模型的高級方法。需要具備機器學習概念和Python編程的工作知識。
目錄:
1. 元學習介紹
2. 使用Siamese網絡進行人臉和音頻識別
3. 原型網絡及其變體
4. 使用TensorFlow構建匹配和關聯網絡
5. 增強記憶網絡
6. MAML及其變體
7. Meta-SGD和Reptile演算法
8. 梯度一致性作為優化目標
9. 最新進展和下一步