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Hands-On Meta Learning with Python
Metalearningisanexcitingresearchtrendinmachinelearning,whichenablesamodeltounderstandthelearningprocess.UnlikeotherMLparadigms,withmetalearningyoucanlearnfromsmalldatasetsfaster.Hands-OnMetaLearningwithPythonstartsbyexplainingthefundamentalsofmetalearningandhelpsyouunderstandtheconceptoflearningtolearn.Youwilldelveintovariousone-shotlearningalgorithms,likesiamese,prototypical,relationandmemory-augmentednetworksbyimplementingtheminTensorFlowandKeras.Asyoumakeyourwaythroughthebook,youwilldiveintostate-of-the-artmetalearningalgorithmssuchasMAML,Reptile,andCAML.YouwillthenexplorehowtolearnquicklywithMeta-SGDanddiscoverhowyoucanperformunsupervisedlearningusingmetalearningwithCACTUs.Intheconcludingchapters,youwillworkthroughrecenttrendsinmetalearningsuchasadversarialmetalearning,taskagnosticmetalearning,andmetaimitationlearning.Bytheendofthisbook,youwillbefamiliarwithstate-of-the-artmetalearningalgorithmsandabletoenablehuman-likecognitionforyourmachinelearningmodels.
目錄(176章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Hands-On Meta Learning with Python
- Dedication
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the author
- About the reviewers
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Conventions used
- Get in touch
- Reviews
- Introduction to Meta Learning
- Meta learning
- Meta learning and few-shot
- Types of meta learning
- Learning the metric space
- Learning the initializations
- Learning the optimizer
- Learning to learn gradient descent by gradient descent
- Optimization as a model for few-shot learning
- Summary
- Questions
- Further reading
- Face and Audio Recognition Using Siamese Networks
- What are siamese networks?
- Architecture of siamese networks
- Applications of siamese networks
- Face recognition using siamese networks
- Building an audio recognition model using siamese networks
- Summary
- Questions
- Further readings
- Prototypical Networks and Their Variants
- Prototypical networks
- Algorithm
- Performing classification using prototypical networks
- Gaussian prototypical network
- Algorithm
- Semi-prototypical networks
- Summary
- Questions
- Further reading
- Relation and Matching Networks Using TensorFlow
- Relation networks
- Relation networks in one-shot learning
- Relation networks in few-shot learning
- Relation networks in zero-shot learning
- Loss function
- Building relation networks using TensorFlow
- Matching networks
- Embedding functions
- The support set embedding function (g)
- The query set embedding function (f)
- The architecture of matching networks
- Matching networks in TensorFlow
- Summary
- Questions
- Further reading
- Memory-Augmented Neural Networks
- NTM
- Reading and writing in NTM
- Read operation
- Write operation
- Erase operation
- Add operation
- Addressing mechanisms
- Content-based addressing
- Location-based addressing
- Interpolation
- Convolution shift
- Sharpening
- Copy tasks using NTM
- Memory-augmented neural networks (MANN)
- Read and write operations
- Read operation
- Write operation
- Summary
- Questions
- Further reading
- MAML and Its Variants
- MAML
- MAML algorithm
- MAML in supervised learning
- Building MAML from scratch
- Generate data points
- Single layer neural network
- Training using MAML
- MAML in reinforcement learning
- Adversarial meta learning
- FGSM
- ADML
- Building ADML from scratch
- Generating data points
- FGSM
- Single layer neural network
- Adversarial meta learning
- CAML
- CAML algorithm
- Summary
- Questions
- Further reading
- Meta-SGD and Reptile
- Meta-SGD
- Meta-SGD for supervised learning
- Building Meta-SGD from scratch
- Generating data points
- Single layer neural network
- Meta-SGD
- Meta-SGD for reinforcement learning
- Reptile
- The Reptile algorithm
- Sine wave regression using Reptile
- Generating data points
- Two-layered neural network
- Reptile
- Summary
- Questions
- Further readings
- Gradient Agreement as an Optimization Objective
- Gradient agreement as an optimization
- Weight calculation
- Algorithm
- Building gradient agreement algorithm with MAML
- Generating data points
- Single layer neural network
- Gradient agreement in MAML
- Summary
- Questions
- Further reading
- Recent Advancements and Next Steps
- Task agnostic meta learning (TAML)
- Entropy maximization/reduction
- Algorithm
- Inequality minimization
- Inequality measures
- Gini coefficient
- Theil index
- Variance of algorithms
- Algorithm
- Meta imitation learning
- MIL algorithm
- CACTUs
- Task generation using CACTUs
- Learning to learn in concept space
- Key components
- Concept generator
- Concept discriminator
- Meta learner
- Loss function
- Concept discrimination loss
- Meta learning loss
- Algorithm
- Summary
- Questions
- Further reading
- Assessments
- Chapter 1: Introduction to Meta Learning
- Chapter 2: Face and Audio Recognition Using Siamese Networks
- Chapter 3: Prototypical Networks and Their Variants
- Chapter 4: Relation and Matching Networks Using TensorFlow
- Chapter 5: Memory-Augmented Neural Networks
- Chapter 6: MAML and Its Variants
- Chapter 7: Meta-SGD and Reptile Algorithms
- Chapter 8: Gradient Agreement as an Optimization Objective
- Chapter 9: Recent Advancements and Next Steps
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-07-02 14:29:49
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