- 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
- OpenAI Microscope
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.
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 is an interactive visualization tool for exploring Convolutional Neural Networks applied to the task of semantic image search.
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.
Available in many different languages.
New to Deep Learning? Tinker with a Neural Network in your browser.
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.
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.
The OpenAI Microscope is a collection of visualizations of every significant layer and neuron of eight important vision models.
This is a collection of pages demonstrating the use of the interact command in Sage. It should be easy to just scroll through and copy/paste examples into Sage notebooks.
Examples include Algebra, Bioinformatics, Calculus, Cryptography, Differential Equations, Drawing Graphics, Dynamical Systems, Fractals, Games and Diversions, Geometry, Graph Theory, Linear Algebra, Loop Quantum Gravity, Number Theory, Statistics/Probability, Topology, Web Applications.
by Simon Ward-Jones. A visual đź‘€ tour of probability distributions.
- Bernoulli Distribution
- Binomial Distribution
- Normal Distribution
- Beta Distribution
- LogNormal Distribution
by Simon Ward-Jones. Explaining the basics of bayesian inference with the example of flipping a coin.
A visual introduction to probability and statistics.
