1_Intro.pdf
May 5, 2020
4.2 MB
2_Gradient Descent.pdf
May 5, 2020
2.7 MB
3_Momentum and Acceleration.pdf
May 5, 2020
1.8 MB
4_Constraint Optimization.pdf
May 5, 2020
1.6 MB
5_Mirror Descent.pdf
May 5, 2020
834 KB
6_Stochastic Gradient Descent.pdf
May 5, 2020
1.3 MB
7_Stochastic Gradient Descent 2.pdf
May 5, 2020
2.1 MB
8_Distributed Optimization.pdf
May 5, 2020
1.2 MB
10_Proximal Algorithm.pdf
May 5, 2020
608 KB
11_Duality and MinMax Optimization.pdf
May 5, 2020
3.6 MB
12_Foundations of Convex Optimization.pdf
May 5, 2020
193 KB
13_Hessian Matrix and Preconditioned Gradient Descent.pdf
May 5, 2020
730 KB
14_Self-concordant function and interior point method.pdf
May 5, 2020
1.2 MB
15_Adaptive Algorithms.pdf
May 5, 2020
682 KB
16_Ellipsoid Algorithm.pdf
May 5, 2020
500 KB
17_Introduction to non-convex optimization.pdf
May 5, 2020
3.2 MB
18_Bayesian Optimization.pdf
May 5, 2020
1.3 MB
19_Simulated Annealing and Evolutionary algorithms.pdf
May 5, 2020
2.4 MB
20_Over-parameterization.pdf
May 5, 2020
646 KB
21_Over-parameterization in deep learning.pdf
May 5, 2020
2.2 MB
22_Optimization algorithms and generalizations.pdf
May 5, 2020
10.3 MB
23_Adversarial Examples and Adversarial Training.pdf
May 5, 2020
5 MB
24_Understanding Adversarial examples and adversarial training in deep learning.pdf
May 5, 2020
19.8 MB
25_Regularizations in deep learning.pdf
May 5, 2020
2.5 MB
26_Minmax optimization and Generative Adversarial Networks.pdf
May 5, 2020
5.6 MB
27_Introduction to online optimization.pdf
May 5, 2020
382 KB
28_Introduction to reinforcement learning.pdf
May 5, 2020
3.1 MB