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).

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.

📊 📈 📉 👀 Graph Visualization using Tensorboard

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.

Example of how to use TensorBoard in TensorFlow

Training and fine-tuning pipeline for a custom GPT-style language model built exclusively for Amharic. Pretrained on a 12+ GB corpus and adapted on curated datasets, with support for SentencePiece tokenization, LoRA fine-tuning, and efficient inference tools.

In this practical session, we will explore federated learning using the open-source framework Fed-BioMed by setting up and running a small federated learning experiment. Throughout the tutorial, we will experiment with some of the key parameters of the framework, gaining hands-on experience with how they affect the training process.