Inspired by @DAIR.AI's fantastic collection of Machine Learning notebooks, this repository contains the TensorFlow implementation of their code, which is currently implemented using PyTorch.
These notebooks contain code examples for all sorts of machine learning tasks and applications. They are meant to be minimal and easily reusable and extendable, thus are encouraged to use them for educational and research purposes.
| Name | Description | Notebook |
|---|---|---|
| Introduction to Computational Graphs | A basic tutorial to learn about computational graphs |
|
| TensorFlow Hello World! | Build a simple neural network and train it |
|
| A Gentle Introduction to TensorFlow | A detailed explanation introducing TensorFlow concepts |
|
| Name | Description | Notebook |
|---|---|---|
| Image Similarity using Siamese Networks | An implementation of a Siamese Network for calculating Image Similarity scores using contrastive loss |
|
| Image Classification using CNNs (AlexNet) | An implementation of the well-known AlexNet Network (2012) for classifying hot dog images |
|
| Object Detection using CNNs (YOLOv3) | An implementation of the well-known YOLO single-stage detector (2015) for Object Detection |
|
🐞 If you find any bugs or have any questions regarding these notebooks, please open an issue. I'll address it as soon as I can.
🐦 Reach out on Twitter if you have any questions.
🔗 Please cite the following if you use the code examples in your research:
@misc{zabala2022ml,
author = {Zabala, Francisco},
title = {ML Notebooks (TensorFlow)},
journal = {GitHub},
year = {2022},
url = {https://github.com/datasith/ML-Notebooks-TensorFlow},
}