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

Bias initialization


It is a common practice to initialize the bias by zero as the symmetrical breaking of neurons is taken care of by the random weights' initialization.

Hyperparameters

Hyperparameters are one of the building blocks of the deep learning network. It is an element that determines the optimal architecture of the network (for example, number of layers) and also a factor that is responsible for ensuring how the network will be trained.

The following are the various hyperparameters of the deep learning network:

  • Learning rate: This is responsible for determining the pace at which the network is trained. A slow learning rate ensures a smooth convergence, whereas a fast learning rate may not have smooth convergence.
  • Epoch: The number of epochs is the number of times the whole training data is consumed by the network while training. 
  • Number of hidden layers: This determines the structure of the model, which helps in achieving the optimal capacity of the model.
  • Number of nodes (neurons):...