cs231n-spring2017

1. Lecture 1 _ Introduction to Convolutional Neural Networks for Visual Recognition-vT1JzLTH4G4.mp4145.61MB
2. Lecture 10 _ Recurrent Neural Networks-6niqTuYFZLQ.mp4149.69MB
3. Lecture 11 _ Detection and Segmentation-nDPWywWRIRo.mp4163.26MB
4. Lecture 12 _ Visualizing and Understanding-6wcs6szJWMY.mp4178.9MB
5. Lecture 13 _ Generative Models-5WoItGTWV54.mp4161.23MB
6. Lecture 14 _ Deep Reinforcement Learning-lvoHnicueoE.mp4128.18MB
7. Lecture 15 _ Efficient Methods and Hardware for Deep Learning-eZdOkDtYMoo.mp4184.46MB
8. Lecture 16 _ Adversarial Examples and Adversarial Training-CIfsB_EYsVI.mp4152.91MB
9. Lecture 2 _ Image Classification-OoUX-nOEjG0.mp4128.52MB
10. Lecture 3 _ Loss Functions and Optimization-h7iBpEHGVNc.mp4161.58MB
11. Lecture 4 _ Introduction to Neural Networks-d14TUNcbn1k.mp4139.96MB
12. Lecture 5 _ Convolutional Neural Networks-bNb2fEVKeEo.mp4150.12MB
13. Lecture 6 _ Training Neural Networks I-wEoyxE0GP2M.mp4169.28MB
14. Lecture 7 _ Training Neural Networks II-_JB0AO7QxSA.mp4153.71MB
15. Lecture 8 _ Deep Learning Software-6SlgtELqOWc.mp4167.52MB
16. Lecture 9 _ CNN Architectures-DAOcjicFr1Y.mp4168.62MB