Productionizing AI by 2023
Author:2023
Language: eng
Format: epub
Chapter 5 Neural Networks aNd deep learNiNg
Figure 5-10. LSTM Architecture
Other Types of Neural Networks
Convolutional and Recurrent Neural Networks are used as deep learning architectures to
train on Supervised Learning problems, that is, where we have prelabeled data (such as
classified images or end-of-day stock prices for forecasting). This is the focus of our first
hands-on exercise at the end of this section.
Before we go to that, we will take a quick look at other architectures which are
generally applied to solving Unsupervised Deep Learning problems. These include
Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), and Deep
Boltzmann Machines (DBMs) as well as Autoencoders, Variational Autoencoders,
and Generative Adversarial Networks. Autoencoders are addressed in our second
hands-on lab in this section.
Restricted Boltzmann Machines (RBMs)
RBMs are generative, stochastic two-layered artificial neural networks which learn a
probability distribution over a set of inputs. There are only two types of neurons, hidden
(h in diagram) and visible (v in diagram), all of which are connected to each other. There
are no output nodes.
Whereas Boltzmann Machines have connections between input nodes, Restricted
Boltzmann Machines are a special class with restricted connections between the visible
and the hidden units. This allows for more efficient training using gradient-based
contrastive divergence algorithms.
149
Download
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.
Algorithms of the Intelligent Web by Haralambos Marmanis;Dmitry Babenko(8310)
Test-Driven Development with Java by Alan Mellor(6831)
Data Augmentation with Python by Duc Haba(6749)
Principles of Data Fabric by Sonia Mezzetta(6489)
Learn Blender Simulations the Right Way by Stephen Pearson(6398)
Microservices with Spring Boot 3 and Spring Cloud by Magnus Larsson(6262)
Hadoop in Practice by Alex Holmes(5966)
Jquery UI in Action : Master the concepts Of Jquery UI: A Step By Step Approach by ANMOL GOYAL(5816)
RPA Solution Architect's Handbook by Sachin Sahgal(5663)
Big Data Analysis with Python by Ivan Marin(5411)
The Infinite Retina by Robert Scoble Irena Cronin(5354)
Life 3.0: Being Human in the Age of Artificial Intelligence by Tegmark Max(5162)
Pretrain Vision and Large Language Models in Python by Emily Webber(4381)
Infrastructure as Code for Beginners by Russ McKendrick(4148)
Functional Programming in JavaScript by Mantyla Dan(4044)
The Age of Surveillance Capitalism by Shoshana Zuboff(3964)
WordPress Plugin Development Cookbook by Yannick Lefebvre(3863)
Embracing Microservices Design by Ovais Mehboob Ahmed Khan Nabil Siddiqui and Timothy Oleson(3662)
Applied Machine Learning for Healthcare and Life Sciences Using AWS by Ujjwal Ratan(3640)
