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.
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.