You're reading from Hands-On Neural Networks with Keras

Product typeBook

Published inMar 2019

Reading LevelIntermediate

PublisherPackt

ISBN-139781789536089

Edition1st Edition

Languages

Python

Tools

Keras

Concepts

Neural Networks

Author (1)

Niloy Purkait

Sampling from the latent space

To further elaborate, suppose we had to draw some samples (y) from a probability distribution of variables from a latent space, with a mean of (μ) and a variance of (σ2):

Sampling operation: y ̴ N(μ , σ2)

Since we use a sampling process to draw from this distribution, each individual sample may change every time the process is queried. We can't exactly differentiate the generated sample (y) with respect to the distribution parameters (μ and σ2), since we are dealing with a sampling operation, and not a function. So, how exactly can we backpropagate our model's errors? Well, one solution could be to redefine the sampling process, such as performing a transformation on a random variable (z), to get to our generated output (y), like so:

Sampling equation: y = μ + σz

This is a crucial...

The rest of the page is locked

You have been reading a chapter from

Hands-On Neural Networks with Keras

Published in: Mar 2019Publisher: PacktISBN-13: 9781789536089

Author (1)

Niloy Purkait

Niloy Purkait is a technology and strategy consultant by profession. He currently resides in the Netherlands, where he offers his consulting services to local and international companies alike. He specializes in integrated solutions involving artificial intelligence, and takes pride in navigating his clients through dynamic and disruptive business environments. He has a masters in Strategic Management from Tilburg University, and a full specialization in data science from Michigan University. He has advanced industry grade certifications from IBM, in subjects like signal processing, cloud computing, machine and deep learning. He is also perusing advanced academic degrees in several related fields, and is a self-proclaimed lifelong learner.
Read more about Niloy Purkait

Other recommended products

Related to this chapter

Generative Adversarial Networks Projects

In this book, we will use different complexities of datasets in order to build end-to-end projects. With every chapter, the level of complexity and operations will become advanced. It consists of 8 full-fledged projects covering approaches such as 3D-GAN, Age-cGAN, DCGAN, SRGAN, StackGAN, and CycleGAN with real-world use cases.

BookJan 2019316 pages

TensorFlow Reinforcement Learning Quick Start Guide

This book is an essential guide for anyone interested in Reinforcement Learning. The book provides an actionable reference for Reinforcement Learning algorithms and their applications using TensorFlow and Python. It will help readers leverage the power of algorithms such as Deep Q-Network (DQN), Deep Deterministic Policy Gradients (DDPG), and Proximal Policy Optimization (PPO) to solve challenging control and decision-making problems.

BookMar 2019184 pages

Reinforcement Learning with TensorFlow

Reinforcement learning allows you to develop intelligent, self-learning systems. This book shows you how to put the concepts of Reinforcement Learning to train efficient models.You will use popular reinforcement learning algorithms to implement use-cases in image processing and NLP, by combining the power of TensorFlow and OpenAI Gym.

BookApr 2018334 pages

Neural Networks with Keras Cookbook

This book presents solutions to the majority of the challenges you will face while training neural networks to solve deep learning problems. It covers the trending deep learning architectures used in industry and tackles a variety of use cases in computer vision, text processing, audio analysis, recommender systems, and game bots

BookFeb 2019568 pages

Advanced Deep Learning with R

This book will help readers to apply deep learning algorithms in R using advanced examples. You will cover variants of neural network models such as ANN, CNN, RNN, LSTM, and more using expert techniques. Readers will make use of popular deep learning libraries such as Keras-R, Tensorflow-R, and more to implement AI models.

BookDec 2019352 pages

Deep Learning with R Cookbook

This book will help you get through the problems that you face during the execution of different tasks and understand hacks in deep learning. With unique recipes, you will implement various deep learning architectures using R 3.5.x. You will cover complex algorithms to perform tasks such as reinforcement learning, GANs, advanced neural networks and more.

BookFeb 2020328 pages

Python Deep Learning Cookbook

Deep Learning is a rapidly evolving field of Machine Learning science which gives machines the ability to learn from information. This book contains detailed recipes to tackle with the common and not so common problems while dealing with deep learning algorithms and models in Python. You will benefit from this book by finding technical solutions to the issues presented, along with a detailed explanation of the solutions, and a discussion on corresponding pros and cons of implementing the proposed solution using Theano, Tensorflow, MXNet, and Keras. You'll come across recipes on data pre-processing, network models and topologies, supervised and unsupervised learning presented in a “solution to problem” fashion.

BookOct 2017330 pages

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 Q-Learning with Python

Q-learning is the reinforcement learning approach behind Deep-Q-Learning and is a values-based learning algorithm in RL. This book will help you get comfortable with developing the effective agents for Q learning and also make you learn to effectively develop and deploy Deep Q networks for complex AI applications.

BookApr 2019212 pages

Deep Learning with Keras

Keras is a high-level neural network library written in Python that runs on top of either Theano or TensorFlow. With this book, you’ll learn the basics of Keras in a highly practical way and understand how this minimal, highly modular framework runs on both CPU and GPU, allowing you to put your ideas into action in the shortest possible time.

BookApr 2017318 pages

Deep Learning for Natural Language Processing

Starting with the basics, this book teaches you how to choose from the various text pre-processing techniques and select the best model from the several neural network architectures for NLP issues.

BookJun 2019372 pages

Neural Network Projects with Python

This book contains practical implementations of several deep learning projects in multiple domains, including in regression-based tasks such as taxi fare prediction in New York City, image classification of cats and dogs using a convolutional neural network, implementing a facial recognition security system using Siamese Neural Networks, and more.

BookFeb 2019308 pages

Personalised recommendations for you

Based on your interests and search pattern

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

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

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

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

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

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

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

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

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

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

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

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

You're reading from Hands-On Neural Networks with Keras

Unlock this book and the full library FREE for 7 days

Author (1)

Generative Adversarial Networks Projects

TensorFlow Reinforcement Learning Quick Start Guide

Reinforcement Learning with TensorFlow

Neural Networks with Keras Cookbook

Advanced Deep Learning with R

Deep Learning with R Cookbook

Python Deep Learning Cookbook

Hands-On Deep Learning Algorithms with Python

Hands-On Q-Learning with Python

Deep Learning with Keras

Deep Learning for Natural Language Processing

Starting with the basics, this book teaches you how to choose from the various text pre-processing techniques and select the best model from the several neural network architectures for NLP issues.

Neural Network Projects with Python

Et al.

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.

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.

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.

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.

Mastering Tableau 2023

Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

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.

Modern Data Architecture on AWS

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.

TinyML Cookbook