zeta1999 / interactive_tools

Interactive Tools for Machine Learning, Deep Learning and Math

Interactive Tools for ML, DL and Math

Content

• CNN Explainer
• Play with GANs in the Browser
• ConvNet Playground
• Distill: Exploring Neural Networks with Activation Atlases
• A visual introduction to Machine Learning
• Interactive Deep Learning Playground
• Initializing neural networks
• Embedding Projector
• Seeing Theory: Probability and Stats

CNN Explainer

An interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs). It runs a pre-tained CNN in the browser and lets you explore the layers and operations.

• Live Demo | Video | Code | Paper

Play with GANs in the Browser

Explore Generative Adversarial Networks directly in the browser with GAN Lab. There are many cool features that support interactive experimentation.

• Interactive hyperparameter adjustment
• User-defined data distribution
• Slow-motion mode
• Manual step-by-step execution

ConvNet Playground

ConvNet Playground is an interactive visualization tool for exploring Convolutional Neural Networks applied to the task of semantic image search.

Distill: Exploring Neural Networks with Activation Atlases

Feature inversion to visualize millions of activations from an image classification network leads to an explorable activation atlas of features the network has learned. This can reveal how the network typically represents some concepts.

A visual introduction to Machine Learning

Available in many different languages.

Interactive Deep Learning Playground

New to Deep Learning? Tinker with a Neural Network in your browser.

Initializing neural networks

Initialization can have a significant impact on convergence in training deep neural networks. Simple initialization schemes can accelerate training, but they require care to avoid common pitfalls. In this post, deeplearning.ai folks explain how to initialize neural network parameters effectively.

Embedding Projector

It's increaingly important to understand how data is being interpreted by machine learning models. To translate the things we understand naturally (e.g. words, sounds, or videos) to a form that the algorithms can process, we often use embeddings, a mathematical vector representation that captures different facets (dimensions) of the data. In this interactive, you can explore multiple different algorithms (PCA, t-SNE, UMAP) for exploring these embeddings in your browser.

Math

Seeing Theory: Probability and Stats

A visual introduction to probability and statistics.