Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Hinton's Capsule network


Geoffrey Hinton, the father of deep learning, created a huge stir in the space of deep learning by introducing a new network. This network was called the Capsule Network (CapsNet). An algorithm to train this network was also brought forth, which is called dynamic routingbetween capsules. For the first time, Hinton spoke about it in 2011 in the paper called transforming autoencoder. In 2017 November, a full paper was published by Hinton and his team regarding the Capsule network.

 

The Capsule Network and convolutional neural networks

The convolutional neural network (CNN) has been one of the most important milestones in the area of deep learning. It has got everyone excited and has been the cornerstone for new research, too. But, as they say, Nothing is perfect in this world. Nor is our beloved CNN.

Can you recall how CNNs work? The most important job of a CNN is to execute convolution. What this means is that once you pass an image through CNN, the features, such as...