Shehbaz Tariq's repositories
AIPND_Image_classifier_project
Developing an AI application
Deep-Learning-with-PyTorch-Lightning
Getting Started with PyTorch Lightning, Published by Packt
deepJSCC-feedback
Joint Source-Channel Coding of Images With Feedback
drawio-libs
Collection of images, libraries, shapes to use with the https://drawl.io application
Efficient-CapsNet
Official TensorFlow code for the paper "Efficient-CapsNet: Capsule Network with Self-Attention Routing".
harmonic-oscillator-pinn
Code accompanying my blog post: So, what is a physics-informed neural network?
ibm-quantum-challenge-fall-22
For IBM Quantum Fall Challenge 2022
ImageClassifier-PreTrained
KPITB Innova Scholarship 2022 - AI Programming with Python
LaTeX-graphics-with-TikZ
LaTeX graphics with TikZ, by Packt Publishing
latex_templates
A collection of useful LaTeX templates.
PsCo
Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning (ICLR 2023)
pytorch-Deep-Learning
Deep Learning (with PyTorch)
pytorch-deep-learning-1
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
pytorchTutorial
PyTorch Tutorials from YouTube channel
qiskit-cert-workbook
Qiskit Certification exam preparation notebook
qiskit-certified-exam-workbook
This is the workbook I created while I was studying for the Qiskit Associate Developer exam. I hope this becomes useful to others as it was for me :)
QSSL
Code for Quantum Self-Supervised Learning
Quantum-Multi-Agent-Reinforcement-Learning
Quantum Multi-agent Reinforcement Learning (QMARL)
quantum-neural-network
For building quantum neural networks in Qiskit and integrating with PyTorch
segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Semantic-Communication-Systems
pytorch implementation of "Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data"
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.