moDel Agnostic Language for Exploration and eXplanation

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

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.

Librería Python para generar reportes de evaluación (clasificación, regresión, forecasting) con métricas y gráficos listos en Markdown, JSON y pronto HTML.

Display outputs of each layer in CNN models

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.

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

Easy-to-use UI based tool that visualizes the internal layers and activations of any Pytorch network that takes image as input , built using PyQt

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

Multiple Linear Regression implemented in Python using Gradient Descent to predict diabetes progression (Y) from 10 physiological features. Features data normalization, cost function analysis, and model visualization. 📉📊🧠