Popular GitHub Repositories for Model Visualization

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

Automated Shorthand Recognition using Optimized DNNs

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

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.

Display outputs of each layer in CNN 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."

ML Model Visualization

This will utilize neural network and machine learning models to paper trade on the stock market.

This project analyzes traffic accident data to identify patterns and predict crash severity using machine learning models. Various classification algorithms, including Random Forest, Logistic Regression, Decision Tree, and K-Nearest Neighbors (KNN), were trained to classify accident types.

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

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.

? Visualize feature models interactively ⚡ Validate configurations in real-time ? Translate constraints from natural language (AI-powered) ? Product-focused insights for devs & PMs