Hands-On Meta Leaing with Python

by: Sudharsan Ravichandiran (Author)

Publisher: Packt Publishing

Publication Date: 2018/12/28

Language: English

Print Length: 226 pages

ISBN-10: 1789534208

ISBN-13: 9781789534207

Book Description

Explore a diverse set of meta-leaing algorithms and techniques to enable human-like cognition for your machine leaing models using various Python frameworksKey FeaturesUnderstand the foundations of meta leaing algorithms Explore practical examples to explore various one-shot leaing algorithms with its applications in TensorFlow Master state of the art meta leaing algorithms like MAML, reptile, meta SGD Book DescriptionMeta leaing is an exciting research trend in machine leaing, which enables a model to understand the leaing process. Unlike other ML paradigms, with meta leaing you can lea from small datasets faster. Hands-On Meta Leaing with Python starts by explaining the fundamentals of meta leaing and helps you understand the concept of leaing to lea. You will delve into various one-shot leaing 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 leaing algorithms such as MAML, Reptile, and CAML. You will then explore how to lea quickly with Meta-SGD and discover how you can perform unsupervised leaing using meta leaing with CACTUs. In the concluding chapters, you will work through recent trends in meta leaing such as adversarial meta leaing, task agnostic meta leaing, and meta imitation leaing. By the end of this book, you will be familiar with state-of-the-art meta leaing algorithms and able to enable human-like cognition for your machine leaing models. What you will leaUnderstand the basics of meta leaing methods, algorithms, and types Build voice and face recognition models using a siamese network Lea 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 leaing and adversarial meta leaing Explore task agnostic meta leaing and deep meta leaing Who this book is forHands-On Meta Leaing with Python is for machine leaing enthusiasts, AI researchers, and data scientists who want to explore meta leaing as an advanced approach for training machine leaing models. Working knowledge of machine leaing concepts and Python programming is necessary.Table of ContentsIntroduction to Meta LeaingFace and Audio Recognition using Siamese NetworkPrototypical Network and its variantsBuilding Matching and Relation Network using TensorflowMemory Augmented NetworksMAML and its variantsMeta-SGD and Reptile ALgorithmGradient Agreement as an Optimization ObjectiveRecent Advancements and Next Steps

About the Author

Explore a diverse set of meta-leaing algorithms and techniques to enable human-like cognition for your machine leaing models using various Python frameworksKey FeaturesUnderstand the foundations of meta leaing algorithms Explore practical examples to explore various one-shot leaing algorithms with its applications in TensorFlow Master state of the art meta leaing algorithms like MAML, reptile, meta SGD Book DescriptionMeta leaing is an exciting research trend in machine leaing, which enables a model to understand the leaing process. Unlike other ML paradigms, with meta leaing you can lea from small datasets faster. Hands-On Meta Leaing with Python starts by explaining the fundamentals of meta leaing and helps you understand the concept of leaing to lea. You will delve into various one-shot leaing 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 leaing algorithms such as MAML, Reptile, and CAML. You will then explore how to lea quickly with Meta-SGD and discover how you can perform unsupervised leaing using meta leaing with CACTUs. In the concluding chapters, you will work through recent trends in meta leaing such as adversarial meta leaing, task agnostic meta leaing, and meta imitation leaing. By the end of this book, you will be familiar with state-of-the-art meta leaing algorithms and able to enable human-like cognition for your machine leaing models. What you will leaUnderstand the basics of meta leaing methods, algorithms, and types Build voice and face recognition models using a siamese network Lea 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 leaing and adversarial meta leaing Explore task agnostic meta leaing and deep meta leaing Who this book is forHands-On Meta Leaing with Python is for machine leaing enthusiasts, AI researchers, and data scientists who want to explore meta leaing as an advanced approach for training machine leaing models. Working knowledge of machine leaing concepts and Python programming is necessary.Table of ContentsIntroduction to Meta LeaingFace and Audio Recognition using Siamese NetworkPrototypical Network and its variantsBuilding Matching and Relation Network using TensorflowMemory Augmented NetworksMAML and its variantsMeta-SGD and Reptile ALgorithmGradient Agreement as an Optimization ObjectiveRecent Advancements and Next Steps