Getting Things Done With Pytorch Save
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, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.
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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 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
- Object Detection on Custom Dataset with YOLO (v5)
Consider buying the book if you want to support my work. Thanks for stopping by! 🤗
Open Source Agenda is not affiliated with "Getting Things Done With Pytorch" Project. README Source: curiousily/Getting-Things-Done-with-Pytorch
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