Debugging, monitoring and visualization for Python Machine Learning and Data Science

Entity Framework Core Power Tools - reverse engineering, migrations and model visualization in Visual Studio & CLI

moDel Agnostic Language for Exploration and eXplanation

📍 Interactive Studio for Explanatory Model Analysis

A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.

Explanatory Model Analysis. Explore, Explain and Examine Predictive Models

Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN

Visualize correlations between variables

Automated Shorthand Recognition using Optimized DNNs

Triplot: Instance- and data-level explanations for the groups of correlated features.

Interactive 3D visualization tool for deep learning architectures. Supports PyTorch, ONNX, Keras, and TensorFlow models with real time exploration and analysis.

This project integrates MobileViTv3 into YOLOv8 for UAV-based object detection, achieving higher accuracy and smoother training than the original yolov8n.pt. Designed with lightweight efficiency, it is well-suited for deployment on edge devices such as drones. A Streamlit app is provided for intuitive model visualization and real-time inference.

This repo helps to track model Weights, Biases and Gradients during training with loss tracking and gives detailed insight for Classification-Model Evaluation

PyTorch model architecture diagram generator: neural network diagram, SVG, PNG, TikZ, LaTeX

LiteCNN: Intuitive Python library for creating, training and visualizing convolutional neural networks. Features simplified CNN layer definition, automated training workflows, model visualization, and seamless Keras-to-ONNX conversion. Includes 15 pre-configured popular models for immediate use.

Portable ML bridges and visual neural-network topology across PyTorch, Keras, TensorFlow, and serialized models.

"A machine learning project to detect fake product reviews using Opinion Mining. It analyzes review text, extracts features, and trains models to classify reviews as genuine or deceptive. The focus is on accuracy and precision to ensure online content authenticity."

Local-first ML experiments with complete visual explanations from data and layers to gradients, health, and replay.

Graphical User Interface to debug ROS systems

Inspectable PyTorch training with visual layer shapes, activations, sparsity, gradients, and topology.