ml-mipt
pytorch-implementations
| ml-mipt | pytorch-implementations | |
|---|---|---|
| 18 | 1 | |
| 8 | 23 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | almost 3 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | - |
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ml-mipt
pytorch-implementations
-
Paper Implementations using PyTorch
I have created a git repository that contains a collection of pytorch notebooks implementing different deep learning papers. These notebooks were created by me during my learning process and hopefully would help others play around with the concepts related to the papers. Here is the link: https://github.com/jaygala24/pytorch-implementations
What are some alternatives?
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
autogluon - Fast and Accurate ML in 3 Lines of Code
MachineLearningWithPython - Get started with Machine Learning with Python - An introduction with Python programming examples
Deep-Learning-Computer-Vision - My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.
mlops-course - Learn how to design, develop, deploy and iterate on production-grade ML applications.
conformal-prediction - Lightweight, useful implementation of conformal prediction on real data.
ml-course - Open Machine Learning course
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
paper-implementations - Attempts to implement various deep learning, computer vision papers.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
deepcourse - Learn the Deep Learning for Computer Vision in three steps: theory from base to SotA, code in PyTorch, and space-repetition with Anki