**Neural Network Tutorials - Herong's Tutorial Examples** - 1.20, by Dr. Herong Yang

Deep Playground for Classical Neural Networks

This chapter provides introductions and tutorials on Deep Playground. Topics include introduction to Deep Playground; solving the classification problems without and with hidden layers; impact comparison of training set, network configuration, learning rate, and activation function.

Impact of Extra Input Features

Impact of Additional Hidden Layers and Neurons

Takeaways:

- Deep Playground is a nice open source interactive tool for learning classical neural networks.
- Deep Playground runs in a Web browser without any additional plugins or programs.
- Deep Playground comes with some 2 dimensional classification and regression problems.
- Deep Playground supports upto 6 hidden layers and 8 neurons per layer.
- Deep Playground supports a selection of 4 activation functions: ReLU(), Tanh(), Sigmoid(), and Linear().

Table of Contents

►Deep Playground for Classical Neural Networks

Building Neural Networks with Python

Simple Example of Neural Networks

TensorFlow - Machine Learning Platform

PyTorch - Machine Learning Platform

CNN (Convolutional Neural Network)