Machine Learning Studies

1. Stanford CS231n Winter 2016 : Convolutional Neural Networks for Visual Recognition - Done

2. Stanford CS20 2018 : Tensorflow for Deep Learning Research - Continue

3. Stanford CS229 2017 : Machine Learning

4. Oxford CS deepnlp 2017 : https://github.com/oxford-cs-deepnlp-2017/lectures

5. NOTRE DAME Statistical Computing 2017 : https://www.zabaras.com/statisticalcomputing

6. Read http://efavdb.com/gaussian-processes/ - Done

7. Check http://blog.dlib.net/2017/12/a-global-optimization-algorithm-worth.html - Done

8. Read https://ratsgo.github.io/blog/categories/

9. Implement Awesome-GANs : https://github.com/kozistr/Awesome-GANs/ - Done, Continue

10. Implement Object Detection with Yolo v2 & Faster R-CNN for embedded devices - Pending

11. Find about Real-Time Image Captioning for embedded devices - Done

12. Kaggle : Plant Seedlings Classification - Done

13. Write a kernel, Kaggle : Plant Seedlings Classification - Done

14. Find about WaveNet, sound classification(?), generation - Done, Continue

15. https://github.com/jtoy/awesome-tensorflow

16. Sound Classification & Pre-Processing... - Continue

17. Check Yolo v3 - Done