What Is Deep Playground

This section provides a quick introduction of Deep Playground, which is an open source interactive tool for learning classical neural networks.

What Is Deep Playground? Deep Playground is an open source interactive tool for learning classical neural networks. It was created by Daniel Smilkov and Shan Carter and is available on GitHub at https://github.com/tensorflow/playground.

A Web based version of Deep Playground is also available at https://playground.tensorflow.org. You can play with it with any Web browser without installing anything on your computer.

The picture below shows you a screenshot of Deep Playground.

Deep Playground Screenshot
Deep Playground - Screenshot

Explanations of some interactive controls are given below:

Play control group - It provides 3 functions: Resets the model with weight matrices randomly initialized again; Stops/resumes the epoch loop; And runs 1 epoch only.

DATA - Allows you to select a specific pattern to generate the input dataset, which is a collections of (x1, x2, y). x1 and x2 is the input vector with values in the range of -6 and 6. y is the output has a value -1 (displayed in orange) or 1 (displayed blue).

REGENERATE - Regenerates the input dataset of the same pattern.

FEATURES - Allows you to reduce or expand the input vector. Additional input components like x1*x2 will improve the accuracy of the model.

HIDDEN LAYERS - Allows you to add or remove hidden neural network layers. It also allows you to control the number of nodes in each hidden layer.

OUTPUT - Shows the accuracy of the model for the training subset and the test subset. It also shows the prediction of any other input using the background color.

Table of Contents

 About This Book

Deep Playground for Classical Neural Networks

What Is Deep Playground

 Simple Model in Playground

 Impact of Extra Input Features

 Impact of Additional Hidden Layers and Neurons

 Complex Model in Playground

 Impact of Training Set Size

 Impact of Neural Network Configuration

 Impact of Learning Rate

 Impact of Activation Functions

 Building Neural Networks with Python

 Simple Example of Neural Networks

 TensorFlow - Machine Learning Platform

 PyTorch - Machine Learning Platform

 CNN (Convolutional Neural Network)

 RNN (Recurrent Neural Network)

 GNN (Graph Neural Network)

 References

 Full Version in PDF/EPUB