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.

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

Display outputs of each layer in CNN models

ReactJS dashboard to visualize the model results of ShipCohortStudy

This repository contains credit card prediction project that I made using Streamlit and Python programming language.

This repository provides a collection of code and implementations for various chaos theory models. It aims to facilitate the understanding and exploration of chaos theory concepts and inspire further research and experimentation in this field.

ML Model Visualization

Powerful Python tool for visualizing and interacting with pre-trained Masked Language Models (MLMs) like BERT. Features include self-attention visualization, masked token prediction, model fine-tuning, embedding analysis with PCA/t-SNE, and SHAP-based model interpretability.

Yellowbrick wraps the scikit-learn and matplotlib to create publication-ready figures and interactive data explorations. It is a diagnostic visualization platform for machine learning that allows us to steer the model selection process by helping to evaluate the performance, stability, and predictive value of our models and further assist in dia…

This program demonstrates the use of a decision tree classifier to recommend music genres based on user demographics. Visualizes the decision-making process of the model using Graph viz. The accuracy score provides a quantitative measure of how effectively the model predicts user preferences.

Code to visualize how different layers view the input when the output is changed. Also visualize the salient features as seen by the input image