Fully batched seq2seq example based on practical-pytorch, and more extra features.

Exploring Reinforcement Learning Solutions to the Vehicle Routing Problem. PPO, A2C, DQN, SAC

A simple program on how you can use tensor-board for visualization and how you can freeze your model graph and later use if for testing

Deep-Attention Text Classifier in Tensorflow

Data visualization using Matplotlib, pandas, seaborn and tensorboard

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

Predicted biomass pyrolysis yields using multilayer perceptron models (ANNs) and RFs.

Classifies the cifar-10 database by using a vgg16 network. Training, predicting and showing learned filters are included.

Embedding Visualization in TensorBoard

Supervised binary classification of skin lesions from dermascopic images using an ensemble of diverse CNN architectures (EfficientNet-B4, ResNet-50).

📊 📈 📉 👀 Graph Visualization using Tensorboard

TF Reader is an offline alternative to TensorBoard that offers a more thorough division of Scalars and custom plots, with a tkinter GUI and matplotlib. It scans training folders, identifies sessions by model/reward tags, size, and supports curve smoothing via slider, but currently only plots Scalars.

A Python and PyTorch–based project that rethinks the traditional Vision Transformer (ViT) architecture by integrating EfficientNet for enhanced feature extraction. The hybrid design addresses key limitations of standard ViTs, improving face recognition accuracy while achieving better computational efficiency.

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.

Consists of variety of Autoencoders implementation for various applications such as denoising image, reverse image search, segmantic hair segmentation.

Trained nearly 700 official data provided by Ministry of Health Malaysia to forecast Covid-19 cases.