Projects sharing features with Swne

TensorBoard is a visualization toolkit for tracking and analyzing machine learning model training progress and performance using TensorFlow event logs. It provides a monitoring dashboard for plotting scalar metrics, tensor distributions, and training curves, and includes specialized tools for visualizing neural network computational graphs and projecting high-dimensional embeddings. The project enables side-by-side comparison of multiple training runs to analyze the impact of hyperparameters on model outcomes. It also features a high-dimensional embedding projector and a graph visualizer for

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

Orange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The