curiousily/Getting-Things-Done-with-Pytorch
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.
| repo name | curiousily/Getting-Things-Done-with-Pytorch |
| repo link | https://github.com/curiousily/Getting-Things-Done-with-Pytorch |
| homepage | https://www.curiousily.com/ |
| language | Jupyter Notebook |
| size (curr.) | 30116 kB |
| stars (curr.) | 176 |
| created | 2020-01-31 |
| license | Apache License 2.0 |
Get SH*T Done with PyTorch
Learn how to solve real-world problems with Deep Learning models (NLP, Computer Vision, and Time Series). Go from prototyping to deployment with PyTorch and Python!
Read the book here
📖 Read for FREE
The whole book can be read using the links below. Each part contains a notebook that you can find in this repository.
- Getting Started with PyTorch
- Build Your First Neural Network
- Transfer Learning for Image Classification using Torchvision
- Face Detection on Custom Dataset with Detectron2
- Time Series Forecasting with LSTMs for Daily Coronavirus Cases
- Time Series Anomaly Detection using LSTM Autoencoders
- Create Dataset for Sentiment Analysis by Scraping Google Play App Reviews
- Sentiment Analysis with BERT and Transformers by Hugging Face
- Deploy BERT for Sentiment Analysis as REST API using FastAPI
Consider buying the book if you want to support my work. Thanks for stopping by! 🤗
