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A Neural Network Playground is a browser-based educational visualization created by Daniel Smilkov and Shan Carter for experimenting with a real neural network. Users adjust controls to explore how a network learns from data. The project is open sourced on GitHub under the Apache License so its features can be tailored to a topic or lesson.
Pix2pix implementation in keras. The repository provides code to download the facades dataset, install requirements, and start training, and it references the original Image-to-Image Translation with Conditional Adversarial Networks paper.
NN Visual is an interactive web-based neural network visualizer. It lets users configure hidden layers, select datasets such as auto_mpg, iris, xor, or mnist, and train a network while observing forward and backpropagation step by step. The tool displays training loss, mean absolute error, weight distributions, and node activation brightness. Written explanations accompany the visualizations to teach concepts like gradient descent, weights, biases, and activation functions without requiring code.
pgmpy is a Python toolkit for causal and probabilistic reasoning using graphical models. It implements data structures for models such as DAGs, PDAGs, MAGs, PAGs, Bayesian Networks, Dynamic Bayesian Networks and Structural Equation Models, with algorithms for tasks including causal discovery, causal identification, inference, model validation, parameter estimation and simulations. Algorithms follow a unified composable API and are scikit-learn compatible when possible. It is available on PyPI and conda.
Hadoop Illuminated's Chapter 16 page lists pointers to publicly available big data sets, covering generic repositories, geo data, web data and government data. It links to sources such as Amazon AWS data sets, InfoChimps, the Comprehensive Knowledge Archive Network, OpenStreetMap, Wikipedia data, Google N-gram data, Freebase, data.gov and World Bank data.
GeoLite databases and web services from MaxMind provide free geolocation and limited network data for IP addresses. The data is less accurate than MaxMind's paid GeoIP products. A free GeoLite account is required to download databases or query the web services, which use the same integration methods and documentation as GeoIP with minor modifications.
An open source tool for running arbitrary queries against public data from the Stack Exchange network.
Analyzes structured data and helps produce queries, insights or visualizations.
Pastalog is a realtime visualization server for neural network training performance. It works with Lasagne, Keras, Tensorflow, Torch, Theano and other frameworks. Data can be logged through a Python API, a Torch wrapper, or POST requests, and logged metrics update in real time at localhost.
Minimalist neural network library optimized for sparse data and single machine environments.
Zetane Viewer is a free tool for visualizing machine learning and artificial neural network models, including architecture and internal data such as feature maps, weights, biases and layer output tensors. The viewer supports .ONNX and .ZTN files and is available for Windows, Linux and Mac. It can also be launched from existing scripts or notebooks via the Zetane Python API.
A GitHub repository for generating retro pixel game characters with generative adversarial networks. It includes the TinyHero dataset of 64x64 pixel characters generated from the Universal LPC spritesheet, with 3648 images. Notebooks cover a DCGAN based on the PyTorch tutorial, a conditional DCGAN and a deep convolutional autoencoder. Art is dual licensed under GPL 3.0 and CC-BY-SA 3.0.
Codecademy's Data Scientist: Machine Learning Specialist Career Path is a beginner-level course covering Python 3, SQL, pandas, scikit-learn, Matplotlib, and TensorFlow. The path is listed as taking 95 hours on average and includes 59 projects with no prerequisites, and is described as teaching analysis of data, drawing predictions with machine learning, and building neural networks.
DeepAlpha is an AI-powered cryptocurrency trading bot that uses a deep neural network combining BiLSTM and attention mechanisms, trained on GPU. It supports 25 coins with a reported 84.9% accuracy rate, integrates with Bybit, Binance, OKX, and Gate.io exchanges, and is available as a free cloud or self-hosted service.
TabGAN is a software project hosted on GitHub under the Diyago/Tabular-data-generation repository. It focuses on the application of generative adversarial networks (GANs) to tabular data generation, reviewing and analyzing recent research and practical implementations in this field.
Project that automatically maps relationship networks by parsing public data using LLMs and visualizes it as an interactive graph.
Official PyTorch implementation of the CVPR 2019 paper on deep high-resolution representation learning for human pose estimation. The network maintains high-resolution representations through the whole process and connects multi-resolution subnetworks in parallel with repeated multi-scale fusions. Results are reported on the COCO keypoint detection dataset and the MPII Human Pose dataset.
Personal page of Hakan Kardes, a principal scientist whose listed research interests include big data, record linkage, data mining, graph theory, social networks and Internet measurements. The page lists publications on topics such as connected component computation in MapReduce, address parsing and organization entity resolution.
Orange is an open-source machine learning and data visualization toolbox. It offers visual programming in which widgets are placed on a canvas and connected to load datasets and analyze data. It supports interactive visualization methods including box plots, scatter plots, decision trees, hierarchical clustering, heatmaps, MDS, t-SNE and linear projections. Extensions add text mining, network analysis and other specialized capabilities.
Peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyft.
An AWS program offering students and educators no-cost access to cloud computing skills training developed by AWS experts, plus career pathways for students. It provides a library of free, self-paced online training and hands-on labs covering storage, compute, networking, databases and cloud operations. Learners as young as 13 can register with an email address, and learners 18 and older can access a job board.
emel is a simple and functional machine learning library built for the Erlang ecosystem and implemented in Gleam. It turns data into functions and supports installing through rebar.config, mix.exs or gleam add. Implemented algorithms include linear regression, k nearest neighbors, decision tree, naive Bayes, k means, perceptron, logistic regression and neural network, along with mathematics, algebra, geometry and statistics.
This repository provides a hyperparameter optimization implementation for machine learning algorithms, accompanying a paper on hyperparameter optimization theory and practice. It includes sample code for regression and classification problems using the Boston-Housing and MNIST datasets, covering algorithms such as random forest, SVM, KNN and artificial neural networks.
A beginner-friendly guide on using Keras to implement a simple neural network in Python. It walks through MNIST handwritten digit classification, covering setup, data preparation, building the model, compiling, training, testing and using the model. It is intended for complete beginners to Keras who have a basic background knowledge of neural networks.
