[center]Hands-On One-shot Learning with Python | 207 | Shruti Jadon | 2020 | Packt Publishing | [/align]

Get to grips with building powerful deep learning models using PyTorch and scikit-learn

   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.

   Table of Contents

✔         Introduction to One-shot Learning
✔         Metrics-Based Methods
✔         Models-Based Methods
✔         Optimization-Based Methods
✔         Generative Modeling-Based Methods
✔         Conclusion and Other Approaches     

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

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