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.
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
- 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
About This Book
►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)
RNN (Recurrent Neural Network)
GNN (Graph Neural Network)
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