This is not a website. Their are 4 different articles available on this website in which author is explaining as well as allowing others to use his visualization tool to learn about these algorithms/theories by changing parameters of their own choice.

**distill.pub article**and then use infinite curiosity’s interactive visualization to experiment.

Learn Anything is an open source website that shows you the right way to learn anything whether it is machine learning, data science, app development, web development, etc, with appropriate resources. You can contribute your suggestions, explore connections and curate learning paths.

The Language Interpretability Tool (LIT) is for researchers and practitioners looking to understand NLP model behavior through a visual, interactive, and extensible tool. You can use LIT to ask and answer questions like: What kind of examples does my model perform poorly on? Why did my model make this prediction? Can it attribute it to adversarial behavior, or undesirable priors from the training set? Does my model behave consistently if I change things like textual style, verb tense, or pronoun gender? and more.

Artificial Intelligence and JavaScript lovers, take a look at a Neural Networks right in your browser! This website known as “Deep playground”, is an interactive visualization of neural networks, written in TypeScript using d3.js. You can play with the different settings and see a visual representation of the output.

Metacademy is built around an interconnected web of concepts, each one annotated with a short description, a set of learning goals, a (very rough) time estimate, and pointers to learning resources. The concepts are arranged in a prerequisite graph, which is used to generate a learning plan for a concept. Here are the learning plan and graph for deep belief nets.

If you’re not sure what you want to learn, check out some of our roadmaps, which highlight the important concepts in a subject area and how they relate to each other. The graph is organized around learning goals, so that you know which aspects of each concept are important to learn. Metacademy keeps track of what you’ve already learned, so that you know where to pick up

OpenAI Microscope is a collection of visualizations of every significant layer and neuron of several common “model organisms” which are often studied in interpretability. Microscope makes it easier to analyze the features that form inside these neural networks, and we hope it will help the research community as we move towards understanding these complicated systems.

One of the best website to learn probability and statistics for machine learning using visuals and great explanation. Seeing Theory will take you through a lot of the concepts you need in machine learning.

Word2vec is an algorithm that transforms words into vectors, so that words with similar meaning end up laying close to each other. Moreover, it allows us to use vector arithmetic’s to work with analogies, for example the famous king – man + woman = queen. king – man + woman is queen; but Why and How? Get the clear understanding as well as create your own word2vec visualization from the above attached link.

Do you love reading research papers? Or do you find reading papers intimidating? Or are you looking for annotated research papers that are much easier to understand? Papers pro is all you need. The author Aakash Nain (Senior Data Scientist @H2O.ai) and other github contributors highlights the important part of papers and create as well as shares a summary of most popular deep learning, computer vision and machine learning research papers.

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

This website offers an incredibly good visualization! Next time whenever I’ll explain machine learning to somebody who’s smart but not a computer scientist or statistician, this is where I’ll start. On their website “R2D3”, the authors are trying to illustrate 7 dimensional data. There are 67 2-dimensional comparisons, scatterplots, 67/2 if you eliminate ones made redundant by symmetry, 6*7/2+7 if you add 1-dimensional histograms for “self comparisons”.

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 interactive explanation, deeplearning.ai folks explain how to initialize neural network parameters effectively. After learning you can also practice in the article itself.

It’s increasingly 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.

Here You’ll find brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more. The another top source where you can learn about machine learning terms is google developers website. By combining both website you will have a core knowledge of basics as well as different machine learning algorithms, neural networks, maths for machine learning, TensorFlow related terms, etc.

Learn, Create and Turn any pencil sketch into real image as shown in the above image using pix2pix model and tensorflow. The author has also provided research paper, its implementation, tool access and other details regarding this project on his website.

Learn Linear Algebra in a Visual Way. After using linear algebra for 20 years times three persons, they were ready to write a linear algebra book that they think will make it substantially easier to learn and to teach linear algebra. we believe that an interactive illustration can say even more, and that is why we have decided to build our linear algebra book around such illustrations. We believe that these figures make it easier and faster to digest and to learn linear algebra.

ML Demos is an open-source visualization tool for machine learning algorithms created to help studying and understanding how several algorithms function and how their parameters affect and modify the results in problems of classification, regression, clustering, dimensionality reduction, dynamical systems and reward maximization.

This is a really an interesting project. It’s a collection of utilities for constructing rule-based models, as opposed to statistical models (i.e. ML). You can learn about various machine learning models in detailed from this website

This is an tool for exploring how Word Embeddings relate to each other through a “calculator” inspired interface. It uses the GloVe 6B pretrained vectors, and is intended as an educational tool only.

Matrix factorization implemented in pure JavaScript. Also for sparse data and logistic regression. Turn city-month temperature maps, movie data, exam results, etc into vectors (with an interactive matrix factorization/PCA).

This is a toy implementation of a visual search engine using Apache MXNet Gluon and deployed on AWS Fargate using MXNet Model Server. Code available here. Try to upload an image and it will search for products with similar visual features among roughly 1M items from the 2013 Amazon catalog!

D3 Graph Theory is a project aimed at anyone who wants to learn graph theory. It provides quick and interactive introduction to the subject. The visuals used in the project makes it an effective learning tool.

ConvNet Playground is an interactive visualization tool for exploring Convolutional Neural Networks applied to the task of semantic image search. Check out more from the above attached link.

Activation Atlases describes a new technique aimed at helping to answer the question of what image classification neural networks “see” when provided an image. Activation atlases provide a new way to peer into convolutional vision networks, giving a global, hierarchical, and human-interpretable overview of concepts within the hidden layers of a network. Learn, Explore and Use Activation Atlases from the above attached link.

A key challenge in developing and deploying responsible Machine Learning (ML) systems is understanding their performance across a wide range of inputs. The What-If tool lets you visually probe the behavior of trained machine learning models, with minimal coding.

How do you make sure a model works equally well for different groups of people? It turns out that in many situations, this is harder than you might think. In this guide, Google will illustrate how this happens by creating a (fake) medical model to screen these people for a disease.

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.

A visual tour of Bernoulli Distribution, Binomial Distribution, Normal Distribution, Beta Distribution and Lognormal Distribution. Apart from this, You can also learn about other machine learning algorithms including Random Forests, Gradient Boosted Decision Trees, Bayesian Inference, etc with examples and code.

ML4A is a collection of tools and educational resources which apply techniques from machine learning to arts and creativity. Learn Fundamentals and implement it to various machine learning projects available on the website.

AI Experiments is a showcase for simple experiments that make it easier for anyone to start exploring machine learning, through pictures, drawings, language, music, and more.

Do you have any more visualization tools and interactive resources related to machine learning, neural networks, deep learning, artificial intelligence, etc, then please share with us on any of our social media. We’ll surely add it in this list. And if you really find this article helpful, help us by sharing this post in different subreddits, Hacker News, LinkedIn groups, Facebook groups, twitter, Quora space, etc. According to us, this tools and guides are one of the best resources to learn machine learning for free in a practical manner. What’s your thought? Share it wherever you find this post.