Streamlit component for TensorBoard, TensorFlow's visualization toolkit

The streamlit based alternative to tensorboard.

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🔁 ChurnPredictor-ANN-Streamlit is a customer churn prediction app using an 🧠 Artificial Neural Network trained on the Churn_Modelling dataset. It features a sleek Streamlit UI, 🔍 GridSearchCV for hyperparameter tuning, and 📊 TensorBoard for training visualization. A great tool to explore deep learning in customer retention analytics!

The project predicts bank customer churn using an Artificial Neural Network (ANN). It includes data preprocessing, model training with TensorFlow and Keras, and deployment via a Streamlit app. The model's performance is visualized using TensorBoard, showcasing effective machine learning techniques for customer retention.

ANN, DNN, PCA Analysis, TensorBoard, Keras, Tensorflow, Streamlit

Building Fraud detection pipeline utilizing streamlit, FastAPI, Polars, Tensorboard

Visualize embedding vocab(or any embedding space really) using tensorboard or streamlit, in order to get a good sense of clustering performance.

Deep learning project for image classification using transfer learning with PyTorch. Features ResNet50/EfficientNet models, Streamlit web interface, ONNX export, and comprehensive training pipeline with TensorBoard logging.

A Streamlit-based interactive app to visualize multiple TensorBoard runs grouped by user-defined tags extracted via regex. Supports aggregation (mean, median), individual run plotting, and interactive Plotly charts.

Customer churn prediction using a Keras-based ANN with preprocessing, feature scaling, and hyperparameter tuning via GridSearchCV. Includes model evaluation with early stopping and TensorBoard, and a Streamlit app for deployment.

End-to-end MNIST digit classification using CNN with data augmentation, hyperparameter tuning, model checkpoints, and TensorBoard logging. Includes a Streamlit app for real-time handwritten digit prediction from uploaded images. Built with TensorFlow and Keras.

ANN-powered customer churn prediction system using TensorFlow. Predicts whether bank customers will leave with high accuracy. Features deep learning model, Streamlit web app, and comprehensive TensorBoard monitoring. Built with Python, NumPy, Pandas & Scikit-learn for production-ready ML.

We built an end-to-end Iris classifier using Keras. Steps: data loading, scaling, one-hot encoding → model design & training → TensorBoard visualization → Bayesian hyperparameter tuning → final evaluation (Confusion/ROC/PR) → Streamlit app for live prediction with saved model and preprocess artifacts.

This project uses an Artificial Neural Network (ANN) to predict if bank customers will leave. It includes steps like preparing the data, training the model with TensorFlow and Keras, and deploying it with a Streamlit app. The model’s performance is shown using TensorBoard, highlighting effective ways to help keep customers

Pneumo.ai is a Streamlit web app for detecting pneumonia from X-rays, leveraging CNN variants like AlexNet, VGG, and ResNet18, with ResNet18 leading with an accuracy of 0.89. It employs early stopping callbacks for efficient model training, visualizes training using TensorBoard, and optimizes inference time with pruning and quantization techniques.