Data
Reader small image

You're reading from  Hands-On Meta Learning with Python

Product typeBook
Published inDec 2018
Reading LevelIntermediate
PublisherPackt
ISBN-139781789534207
Edition1st Edition
Languages
Python
Tools
TensorFlowOpenAI Gym
Concepts
Reinforcement Learning
Right arrow
Author (1)
Sudharsan Ravichandiran
Sudharsan Ravichandiran
author image
Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.
Read more about Sudharsan Ravichandiran

Right arrow

Summary

In this chapter, we started off with prototypical networks, and we saw how a prototypical network computes the class prototype using the embedding function and predicts the class label of the query set by comparing the Euclidean distance between the class prototype and query set embeddings. Following this, we experimented with a prototypical network by performing classification on an omniglot dataset. Then, we learned about the Gaussian prototypical network, which, along with the embeddings, also uses the covariance matrix to compute the class prototype. Following this, we explored semi-prototypical networks, which are used to handle semi-supervised classes. In the next chapter, we will learn about relation and matching networks.

lock icon
The rest of the page is locked
Previous Page
Chapter 8 of 17, Page 5
Next Page
You have been reading a chapter from
Hands-On Meta Learning with Python
Published in: Dec 2018Publisher: PacktISBN-13: 9781789534207
© 2018 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.
Read more about Sudharsan Ravichandiran

Other recommended products

Related to this chapter

Hands-On Deep Learning Algorithms with Python

Hands-On Deep Learning Algorithms with Python

This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement them using popular Python-based deep learning libraries such as TensorFlow.

BookJul 2019512 pages
Hands-On One-shot Learning with Python

Hands-On One-shot Learning with Python

This book is a step by step guide to one-shot learning using Python-based libraries. It is designed to help you understand and design models that can learn information about your data from one, or only a few, training examples. You will also learn to apply these techniques with real-world examples and datasets for classification and regression.

BookApr 2020156 pages
Deep Reinforcement Learning with Python

Deep Reinforcement Learning with Python

Deep Reinforcement Learning with Python - Second Edition will help you learn reinforcement learning algorithms, techniques and architectures – including deep reinforcement learning – from scratch. This new edition is an extensive update of the original, reflecting the state-of-the-art latest thinking in reinforcement learning.

BookSep 2020760 pages
Hands-On Mathematics for Deep Learning

Hands-On Mathematics for Deep Learning

The main aim of this book is to make the advanced mathematical background accessible to someone with a programming background. This book will equip the readers with not only deep learning architectures but the mathematics behind them. With this book, you will understand the relevant mathematics that goes behind building deep learning models.

BookJun 2020364 pages
Advanced Deep Learning with Python

Advanced Deep Learning with Python

This book is an expert-level guide to master the neural network variants using the Python ecosystem. You will gain the skills to build smarter, faster, and efficient deep learning systems with practical examples. By the end of this book, you will be up to date with the latest advances and current researches in the deep learning domain.

BookDec 2019468 pages
Hands-On Reinforcement Learning with Python

Hands-On Reinforcement Learning with Python

Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.

BookJun 2018318 pages
Hands-On Deep Learning Architectures with Python

Hands-On Deep Learning Architectures with Python

This book explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations to help you understand the concepts and ideas required to build efficient artificial intelligence systems, this book will help you construct deep models using popular frameworks and datasets.

BookApr 2019316 pages
Neural Network Programming with Tensorflow

Neural Network Programming with Tensorflow

If you’re aware of the buzz surrounding the terms such as machine learning, artificial intelligence or deep learning, you might know what neural networks are. TensorFlow is a popular framework which can be used to implement efficient neural networks and deep learning models. This book will show you how to leverage the power of TensorFlow to train efficient neural networks. You will start with understanding the fundamentals and basic math for neural networks and why TensorFlow is a popular choice of tool for programming neural networks. During the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how it maps to the appropriate TensorFlow constructs.

BookNov 2017274 pages
TensorFlow 1.x Deep Learning Cookbook

TensorFlow 1.x Deep Learning Cookbook

Deep Neural Networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning, and implement different deep learning networks with easy to follow independent recipes.

BookDec 2017536 pages

Personalised recommendations for you

Based on your interests and search pattern

Et al.

Et al.

Ever wonder why speech recognition systems don't understand the Scottish accent, or what would happen if an astronaut only ate mac 'n' cheese, or other spurious reflections you'd have at a bar? We did, then collated those deliberations into absurd research articles with fake figures and methodologies inspired by even more fictionally absurd studies.

BookAug 2023230 pages5
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages4
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages5
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages1
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages5
Mastering Tableau 2023

Mastering Tableau 2023

This book is a comprehensive resource to mastering your Tableau skills and becoming a BI expert. As you progress, you will learn how to build advanced dashboards and improve your storytelling to derive key business insight, as well as make you well-versed with advanced functionalities of Tableau in the business intelligence domain.

BookAug 2023684 pages
Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

BookSep 2023258 pages5
Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

BookSep 2023258 pages2
Data Engineering with AWS

Data Engineering with AWS

Embark on a journey to master data engineering pipelines on AWS! Our book offers a hands-on experience of AWS services for ingesting, transforming, and consuming data. Whether you're an absolute beginner or someone with basic data engineering experience, this guide is an indispensable resource.

BookOct 2023636 pages5
Modern Data Architecture on AWS

Modern Data Architecture on AWS

Every organization wants an agile, performant, and cost-effective data platform that meets all their current and future business needs. Purpose-built AWS analytics services and their features play a big part in building such a modern data platform. This book brings to you all the design and architectural patterns that’ll help you achieve this goal.

BookAug 2023420 pages5
Practical Guide to Applied Conformal Prediction in Python

Practical Guide to Applied Conformal Prediction in Python

Discover the power of Conformal Prediction with the "Practical Guide to Applied Conformal Prediction in Python." Master the latest techniques to quantify uncertainty in machine learning and computer vision models, and seamlessly apply them to your industry applications.

BookDec 2023240 pages
TinyML Cookbook

TinyML Cookbook

With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano microcontrollers.

BookNov 2023664 pages