Nanodegree Program - Become a Deep Reinforcement Learning Expert

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Part 01-Module 01-Lesson 01_Welcome to Deep Reinforcement Learning

01. Welcome To DRLND-i1-l0n1ntes.mp4

02:07

01. Welcome to the Deep Reinforcement Learning Nanodegree.html

02. RL In The Real World-IGlAyGbOTHo.mp4

01:16

02. RL in the Real World.html

03. Overview of the ND Program.html

04. Projects You Will Build.html

04. Unity Machine Learning Agents-jC12h4UAxqs.mp4

02:34

05. Play Tennis!.html

06. Deadline Policy.html

07. Udacity Support.html

08. Community Guidelines.html

index.html

banana.zip
bipedal-walker.zip
dumb-soccer.zip
game-example.zip
lunar-lander.zip
mjg2mzcyma.zip
mountain-car-cts.zip
output.zip
reacher.zip
screen-shot-2018-06-12-at-5.07.10-pm.zip
screen-shot-2018-07-06-at-11.33.53-am.zip
soccer.zip

Part 01-Module 01-Lesson 02_Get Help from Peers and Mentors

01. What It Takes.html

02. Reviews.html

03. Knowledge.html

04. Student Hub.html

index.html

image4.zip
image8.zip
screen-shot-2018-11-07-at-9.55.40-pm.zip
screen-shot-2018-11-07-at-9.59.16-pm.zip
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screen-shot-2018-11-09-at-6.28.07-pm.zip

Part 01-Module 01-Lesson 03_Get Help with Your Account

01. FAQ.html

02. Support.html

index.html

screen-shot-2018-11-09-at-7.38.47-pm.zip
screen-shot-2018-11-09-at-7.48.22-pm.zip
screen-shot-2018-11-09-at-7.49.34-pm.zip
screen-shot-2018-11-09-at-7.49.50-pm.zip

Part 01-Module 01-Lesson 04_Learning Plan

01. Learning Plan.html

02. Reference Guide.html

03. OpenAI Gym.html

04. GitHub Repository.html

05. AWS Credits.html

06. Student Resources.html

index.html

42135602-b0335606-7d12-11e8-8689-dd1cf9fa11a9.zip
download.zip
grokking-deeprl.zip
image4.zip
openaigym-gif.zip
paper-notes.zip
udacitylogo.zip
unknown.zip

Part 01-Module 01-Lesson 05_Introduction to RL

01. Introduction.html

01. Introduction-6jSFl5kxIBs.mp4

01:32

02. Applications.html

02. Applications-CV6B84mKRNM.mp4

02:16

03. The Setting.html

03. The Setting-nh8Gwdu19nc.mp4

04:58

04. Resources.html

04. Resources-_YPqfAnCqtk.mp4

01:22

index.html

Part 01-Module 01-Lesson 06_The RL Framework The Problem

01. Introduction.html

01. Introduction-X_9l_ZqXXBA.mp4

00:31

02. The Setting, Revisited.html

02. The Setting, Revisited-V6Q1uF8a6kA.mp4

04:07

03. Episodic vs. Continuing Tasks.html

03. Episodic vs. Continuing Tasks-E1I-BPanSM8.mp4

01:59

04. Quiz Test Your Intuition.html

05. Quiz Episodic or Continuing.html

06. The Reward Hypothesis.html

06. The Reward Hypothesis-uAqNwgZ49JE.mp4

02:20

07. Goals and Rewards, Part 1.html

07. Goals and Rewards, Part 1-XPnj3Ya3EuM.mp4

02:16

08. Goals and Rewards, Part 2.html

08. Goals and Rewards, Part 2-pVIFc72VYH8.mp4

03:55

09. Quiz Goals and Rewards.html

10. Cumulative Reward.html

10. Cumulative Reward-ysriH65lV9o.mp4

03:51

11. Discounted Return.html

11. Discounted Return-opXGNPwwn7g.mp4

05:09

12. Quiz Pole-Balancing.html

13. MDPs, Part 1.html

13. MDPs, Part 1-NBWbluSbxPg.mp4

02:46

14. MDPs, Part 2.html

14. MDPs, Part 2-CUTtQvxKkNw.mp4

05:08

15. Quiz One-Step Dynamics, Part 1.html

16. Quiz One-Step Dynamics, Part 2.html

17. MDPs, Part 3.html

17. MDPs, Part 3-UlXHFbla3QI.mp4

02:08

18. Finite MDPs.html

19. Summary.html

index.html

1omsg2-mkguagky1c64uflw.zip
article-2278590-1792e332000005dc-394-634x615.zip
backgammonboard.svg.zip
chess-game.zip
go.zip
index.zip
maze.zip
pup.zip
screen-shot-2017-09-20-at-12.02.06-pm.zip
screen-shot-2017-09-21-at-3.08.03-pm.zip
screen-shot-2017-09-21-at-3.25.10-pm.zip
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screen-shot-2017-09-21-at-12.20.30-pm.zip
screen-shot-2017-09-21-at-12.20.50-pm.zip

Part 01-Module 01-Lesson 07_The RL Framework The Solution

01. Introduction.html

01. Introduction-9Wyf5Zsska8.mp4

00:53

02. Policies.html

02. Policies-hc3LrvaC13U.mp4

03:24

03. Quiz Interpret the Policy.html

04. Gridworld Example.html

04. Gridworld Example-XeHBmPFqTsE.mp4

01:38

05. State-Value Functions.html

05. State-Value Functions-llakAjwox_8.mp4

03:16

06. Bellman Equations.html

06. Bellman Equations-UgIaDMvSdUo.mp4

02:38

07. Quiz State-Value Functions.html

08. Optimality.html

08. Optimality-j231aRV74QM.mp4

03:17

09. Action-Value Functions.html

09. Action-Value Functions-KJLaRfOOPGA.mp4

03:22

10. Quiz Action-Value Functions.html

11. Optimal Policies.html

11. Optimal Policies-2rguYpVyCto.mp4

02:45

12. Quiz Optimal Policies.html

13. Summary.html

index.html

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screen-shot-2017-09-21-at-12.20.30-pm.zip
screen-shot-2017-09-24-at-4.28.04-pm.zip
screen-shot-2017-09-25-at-5.51.40-pm.zip
screen-shot-2017-09-25-at-6.02.37-pm.zip
screen-shot-2017-09-25-at-9.18.00-pm.zip
screen-shot-2017-09-25-at-11.35.38-am.zip

Part 01-Module 01-Lesson 08_Monte Carlo Methods

01. L601 Intro RENDER V2-3H5x0lstvmo.mp4

00:56

01. Review.html

02. Gridworld Example.html

02. L602 Gridworld Example RENDER V2-2-Lwibg_IfmrA.mp4

02:18

03. L603 Monte Carlo Methods RENDER V3-2-titaMCRl224.mp4

04:56

03. Monte Carlo Methods.html

04. L604 MC Prediction Part 1RENDER V2-6ts9gdIS6vg.mp4

03:09

04. MC Prediction - Part 1.html

05. L605 MC Prediction Part 2 RENDER V3-jR49ZyKuJ98.mp4

01:20

05. MC Prediction - Part 2.html

06. L606 MC Prediction Part 3 RENDERv1 V4-9LP6uXdmWxQ.mp4

01:30

06. MC Prediction - Part 3.html

07. OpenAI Gym BlackJackEnv.html

08. Workspace - Introduction.html

09. Coding Exercise.html

09. MC Prediction - Solution Walkthrough-Pwiqk7Pncgc.mp4

07:18

10. Workspace.html

11. Greedy Policies.html

11. L611 Greedy Policies RENDER V4-DH6c-aODMLU.mp4

02:01

12. Epsilon-Greedy Policies.html

12. L612 Epsilon Greedy Policies RENDER V4-PxJMtlR06MY.mp4

03:35

13. MC Control.html

14. Exploration vs. Exploitation.html

15. Incremental Mean.html

15. L615 Incremental Mean RENDER V4-h-8MB7V1LiE.mp4

02:29

16. Constant-alpha.html

16. L617 Constant Alpha Edits RENDER V1-LetHoOtNdJc.mp4

00:51

16. MC Control Constant-alpha-QFV1nI9Zpoo.mp4

02:32

17. Coding Exercise.html

17. M1 L6 S2 V1-6E_3NJcoxmU.mp4

06:44

18. Workspace.html

19. Summary.html

index.html

2-card-21.zip
exploration-vs.-exploitation.zip
jupyter.zip
latex-image-1-copy.zip
screen-shot-2017-09-20-at-12.02.06-pm.zip
screen-shot-2017-09-25-at-11.35.38-am.zip
screen-shot-2017-10-04-at-4.58.58-pm.zip
screen-shot-2017-10-04-at-5.01.26-pm.zip
screen-shot-2017-10-05-at-3.55.40-pm.zip
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screen-shot-2018-05-04-at-3.14.47-pm.zip
screen-shot-2018-05-05-at-1.20.10-pm.zip
screen-shot-2018-05-10-at-6.10.16-pm.zip

Part 01-Module 01-Lesson 09_Temporal-Difference Methods

01. Introduction.html

01. Introduction-yXErXQulI_o.mp4

02:04

02. Review MC Control Methods.html

03. L602 Gridworld Example RENDER V2-2-Lwibg_IfmrA.mp4

02:18

03. Quiz MC Control Methods.html

03. Quiz MC Control Methods-ZwIg6LDMyuo.mp4

01:52

04. TD Control Sarsa.html

04. TD Control Sarsa Part 1-HYV0SP9wm7g.mp4

02:24

04. TD Control Sarsa Part 2-U_CV-UC9G2c.mp4

01:40

05. Quiz Sarsa.html

06. TD Control Q-Learning.html

06. TD Control Sarsamax-4DxoYuR7aZ4.mp4

03:14

07. Quiz Q-Learning.html

08. TD Control Expected Sarsa.html

08. TD Control Expected Sarsa-kEKupCyU0P0.mp4

00:50

09. Quiz Expected Sarsa.html

10. TD Control Theory and Practice.html

11. OpenAI Gym CliffWalkingEnv.html

12. Workspace - Introduction.html

13. Coding Exercise.html

14. Workspace.html

15. Analyzing Performance.html

16. Quiz Check Your Understanding.html

17. Summary.html

index.html

environment.zip
episode.zip
exploration-vs.-exploitation.zip
jupyter.zip
matengai-of-kuniga-coast-in-oki-island-shimane-pref600.zip
qtable2.zip
screen-shot-2017-10-17-at-11.02.44-am.zip
screen-shot-2017-12-17-at-12.49.34-pm.zip
screen-shot-2018-03-07-at-2.33.19-pm.zip
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screen-shot-2018-03-07-at-3.16.47-pm.zip
screen-shot-2018-03-07-at-3.53.08-pm.zip
screen-shot-2018-05-01-at-11.10.05-pm.zip
screen-shot-2018-05-04-at-6.14.28-pm.zip
screen-shot-2018-05-04-at-6.14.42-pm.zip
screen-shot-2018-05-04-at-6.14.56-pm.zip
screen-shot-2018-05-10-at-6.10.16-pm.zip
screen-shot-2018-05-24-at-11.45.52-am.zip

Part 01-Module 01-Lesson 10_Solve OpenAI Gym's Taxi-v2 Task

01. Introduction.html

02. Instructions.html

03. Mini Project.html

index.html

new-tab.zip
open-agent-monitor-main.zip
open-terminal.zip
run-main.zip
screen-shot-2018-04-14-at-3.13.15-pm.zip

Part 01-Module 01-Lesson 11_RL in Continuous Spaces

01. Introducing Arpan.html

02. Introduction-GPjK124RU5g.mp4

04:40

02. Lesson Overview.html

03. Discrete vs. Continuous Spaces.html

03. Discrete vs. Continuous Spaces-uHstLeRzaE8.mp4

04:40

04. Quiz Space Representations.html

05. Discretization.html

05. Discretization-j2eZyUpy--E.mp4

03:06

06. Exercise Discretization.html

07. Workspace Discretization.html

08. Tile Coding.html

08. Tile Coding-BRs7AnTZ_8k.mp4

02:37

09. Exercise Tile Coding.html

10. Workspace Tile Coding.html

11. Coarse Coding.html

11. Coarse Coding-Uu1J5KLAfTU.mp4

02:12

12. Function Approximation.html

12. Function Approximation-UTGWVY6jEdg.mp4

02:56

13. Linear Function Approximation.html

13. Linear Function Approximation-OJ5wrB7o-pI.mp4

04:57

14. Kernel Functions.html

14. Kernel Functions-RdkPVYyVOvU.mp4

02:03

15. Non-Linear Function Approximation.html

15. Non-Linear Function Approximation-rITnmpD2mN8.mp4

01:08

16. Summary.html

16. Summary-MTEBk43oByU.mp4

01:07

index.html

arpan-headshot.zip
jupyter.zip
poker-hand-3-of-a-kind.zip
qtable.zip
screen-shot-2018-05-02-at-12.24.35-am.zip
screen-shot-2018-05-03-at-11.30.05-pm.zip

Part 01-Module 01-Lesson 12_What's Next

01. Arpan Rollercoaster-Rf6cCYRqV58.mp4

01:08

01. Congratulations!.html

02. What can you do now.html

index.html

dqn.zip

Part 02-Module 01-Lesson 01_Study Plan

01. Study Plan.html

02. Deep RL for Robotics.html

02. Deep RL in Robotics-IjG_IWJdb1w.mp4

02:02

index.html

output.zip
udacitylogo.zip

Part 02-Module 01-Lesson 02_Deep Q-Networks

01. DQN Overview-WgiAvr7COR0.mp4

01:14

01. From RL to Deep RL.html

01. From RL to Deep RL-7HLJ0uaR1F0.mp4

01:54

02. Deep Q-Networks.html

02. Deep Q-Networks-GgtR_d1OB-M.mp4

04:18

03. Experience Replay.html

03. Experience Replay-wX_-SZG-YMQ.mp4

06:53

04. Fixed Q-Targets.html

04. Fixed Q-Targets-SWpyiEezfp4.mp4

04:03

05. Deep Q-Learning Algorithm.html

05. Deep Q-Learning Algorithm-MqTXoCxQ_eY.mp4

03:08

06. Coding Exercise.html

07. Workspace.html

08. Deep Q-Learning Improvements.html

09. 10 Double DQN V2-PGCEMLujiGI.mp4

02:56

09. Double DQN.html

10. 10 Prioritized Experience Replay V1-cN8z-7Ze9L8.mp4

04:34

10. Prioritized Experience Replay.html

11. 10 Dueling DQN V2-zZeHbPs39Ls.mp4

01:19

11. Dueling DQN.html

12. Rainbow.html

13. Summary.html

13. Summary-x6JggcDTcys.mp4

00:49

index.html

dqn.zip
dueling-q-network.zip
jupyter.zip
rainbow-1445337690d8q.zip
screen-shot-2018-06-30-at-6.40.09-pm.zip
screen-shot-2018-06-30-at-7.03.40-pm.zip
sonic.zip

Part 02-Module 01-Lesson 03_Deep RL for Robotics

01. 01 Introduction RENDER V2-dfeawuScC7k.mp4

01:16

01. Introduction.html

02. 02 Welcome!-1oElWzRt-lU.mp4

01:17

02. 03 Transitioning-BvDvxw8e0CY.mp4

01:18

02. C++ for Robotics.html

03. 03 CC API HSSC HS RENDER V3-a9-HdpCaYW4.mp4

00:54

03. CC++ API.html

04. Catch Sample.html

05. Udacity Workspace.html

06. Fruit Sample.html

07. Rover Sample.html

08. Arm Sample.html

09. 09 Jetson TX2 Edits V1-M26z7vTti_g.mp4

01:14

09. Jetson Overview-i56qM6NNW9A.mp4

02:44

09. Jetson TX2.html

10. 10 Summary HS V3-cb1FGgZIitc.mp4

00:37

10. Summary.html

index.html

armenv.zip
fruit.zip
nv-rl-stack-diagram.zip
output.zip
rl-gazebo-fall.zip

Part 02-Module 01-Lesson 04_Navigation

01. Unity ML-Agents.html

02. The Environment - Introduction.html

03. The Environment - Play.html

04. Getting Started-ltz2GhFv04A.mp4

00:23

04. The Environment - Explore.html

05. Project Instructions.html

06. Benchmark Implementation.html

07. Not sure where to start.html

08. Collaborate!.html

09. Workspace.html

10. (Optional) Challenge Learning from Pixels.html

Project Description - Navigation.html

Project Rubric - Navigation.html

index.html

849-1234251683jkyt.zip
2018-02-27-16-05-37.zip
banana.zip
bananas-save.zip
idea-2579308-640.zip
screen-shot-2018-06-08-at-1.04.47-pm.zip
unity-wide.zip

Part 02-Module 02-Lesson 01_Opportunities in Deep Reinforcement Learning

01. Opportunities in DRL.html

02. Career Services-cuKecPpZ7PM.mp4

02:11

02. Meet the Careers Team.html

03. Access Your Career Portal.html

04. Your Udacity Professional Profile.html

index.html

get-hired-with-the-udacity-career-portal.zip
screen-shot-2017-10-27-at-1.49.58-pm.zip
screen-shot-2018-07-27-at-1.24.38-pm.zip
udacitylogo-copy.zip

Part 02-Module 02-Lesson 02_Optimize Your GitHub Profile

01. Prove Your Skills With GitHub.html

02. Introduction.html

02. Introduction-Vnj2VNQROtI.mp4

00:54

03. GitHub profile important items.html

03. GitHub profile important items-prvPVTjVkwQ.mp4

01:35

04. Good GitHub repository.html

04. Good GitHub repository-qBi8Q1EJdfQ.mp4

01:10

05. Interview with Art - Part 1.html

05. Interview with Art - Part 1-ClLYamtaO-Q.mp4

02:11

06. Identify fixes for example bad profile.html

06. Identify fixes for example bad profile-AF07y1oAim0.mp4

00:12

06. Identify fixes for example bad profile-ncFtwW5urHk.mp4

00:51

07. Quick Fixes #1.html

07. Quick Fixes-Lb9e2KemR6I.mp4

01:11

08. Quick Fixes #2.html

08. Quick Fixes #2-It6AEuSDQw0.mp4

00:13

09. Writing READMEs with Walter.html

09. Writing READMEs with Walter-DQEfT2Zq5_o.mp4

00:40

10. Interview with Art - Part 2.html

10. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4

01:19

11. Commit messages best practices.html

12. Reflect on your commit messages.html

12. Reflect on your commit messages-_0AHmKkfjTo.mp4

00:16

13. Participating in open source projects.html

13. Participating in open source projects-OxL-gMTizUA.mp4

00:15

14. Interview with Art - Part 3.html

14. Interview with Art - Part 3-M6PKr3S1rPg.mp4

02:30

15. Participating in open source projects 2.html

15. Participating in open source projects 2-elZCLxVvJrY.mp4

00:54

16. Starring interesting repositories.html

16. Starring interesting repositories-U3FUxkm1MxI.mp4

00:13

16. Starring interesting repositories-ZwMY5rAAd7Q.mp4

00:19

17. Next Steps.html

Project Description - Optimize Your GitHub Profile.html

Project Rubric - Optimize Your GitHub Profile.html

index.html

6485174133.zip
6499079068.zip
6509638772.zip
6551597473.zip
mat-leonard-circle.zip

Part 03-Module 01-Lesson 01_Study Plan

01. Study Plan.html

index.html

udacitylogo.zip

Part 03-Module 01-Lesson 02_Introduction to Policy-Based Methods

01. M3 L2 C01 V2-mMnhi8yzwKk.mp4

02:38

01. Policy-Based Methods.html

02. M3 L2 C02 V1-v8tGjlc2aG4.mp4

01:38

02. Policy Function Approximation.html

03. More on the Policy.html

04. Hill Climbing.html

04. M3 L2 C04 V3-5E86a0OyVyI.mp4

03:48

05. Hill Climbing Pseudocode.html

05. M3 L2 C05 V1-0XzzqIXyax0.mp4

01:10

06. Beyond Hill Climbing.html

06. M2L3 04 V1-QicxmyE5vTo.mp4

03:53

07. M3 L2 C07 V3-2poDljPvY58.mp4

01:36

07. More Black-Box Optimization.html

08. Coding Exercise.html

09. Workspace.html

10. OpenAI Request for Research.html

11. M2L3 02 V2-ToS8vXGdODE.mp4

06:37

11. Why Policy-Based Methods.html

12. Summary.html

index.html

cartpole.zip
jupyter.zip
screen-shot-2018-06-26-at-11.53.35-am.zip
screen-shot-2018-06-28-at-6.46.54-pm.zip
screen-shot-2018-07-01-at-10.54.05-am.zip
screen-shot-2018-07-01-at-11.19.22-am.zip
screen-shot-2018-07-01-at-11.28.57-am.zip

Part 03-Module 01-Lesson 03_Policy Gradient Methods

01. M3L3 C01 V3-ZEhQRASU5O4.mp4

03:34

01. What are Policy Gradient Methods.html

02. M3L3 C02 V6-zoOgRoaxGiU.mp4

03:07

02. The Big Picture.html

03. Connections to Supervised Learning.html

03. M3L3 C03 V2-dJz_p4FKE-g.mp4

02:59

04. M3L3 C04 V2-St9ftvMQ_ks.mp4

02:23

04. Problem Setup.html

05. M3L3 C05 V2-o6CI2q3IXEs.mp4

06:24

05. REINFORCE.html

06. (Optional) Derivation.html

07. Coding Exercise.html

08. Workspace.html

09. What's Next.html

10. Summary.html

index.html

350px-normal-distribution-pdf.zip
grad-descent.zip
jupyter.zip
screen-shot-2018-07-17-at-4.44.10-pm.zip
screen-shot-2018-07-27-at-2.25.43-pm.zip

Part 03-Module 01-Lesson 04_Proximal Policy Optimization

01. Instructor Introduction.html

01. Instructor Introduction-sokSgNtGj9Y.mp4

00:45

02. Lesson Preview.html

02. Training an agent to play atari-pong!-w27mvWFBnvQ.mp4

01:40

03. Beyond REINFORCE.html

04. Noise Reduction.html

04. Noise Reduction-GCGqT2knFJ0.mp4

02:20

05. Credit Assignment.html

05. Credit Assignment-tfZw1moB25Y.mp4

01:26

06. Policy Gradient Quiz.html

07. pong with REINFORCE walkthrough-eKIjPrQWIgg.mp4

11:13

07. pong with REINFORCE (code walkthrough).html

08. pong with REINFORCE (workspace).html

09. Importance Sampling.html

09. Importance Sampling-cYPvWriOPIk.mp4

04:23

10. PPO Part 1 The Surrogate Function-Y-boYZlpO7g.mp4

02:28

10. PPO part 1- The Surrogate Function.html

11. PPO Part 2 Clipping Policy Updates-NRzjGGX6c34.mp4

04:07

11. PPO part 2- Clipping Policy Updates.html

12. PPO summary.html

12. TLPPO Summary V1-qRAUAAWA_kc.mp4

00:58

13. Pong with PPO walkthrough-XhfhR7Z01S0.mp4

05:10

13. pong with PPO (code walkthrough).html

14. pong with PPO (workspace).html

index.html

clipped-surrogate.zip
clipped-surrogate-explained.zip
policy-reward-cliff.zip

Part 03-Module 01-Lesson 05_Actor-Critic Methods

01. Introduction.html

01. M3L501 Introduction HS 1 V1-_OHo1pEaJcQ.mp4

01:43

02. M3 L5 02 Motivation V1-dpFPlDtdxyQ.mp4

02:42

02. Motivation.html

03. Bias and Variance.html

03. M3 L5 03 Bias And Variance V2-_vnkkwm46uU.mp4

02:04

04. M3 L5 04 Two Ways For Estimating Expected Returns V3-2W6yIBDvfsQ.mp4

04:08

04. Two Ways for Estimating Expected Returns.html

05. Baselines and Critics.html

05. M3 L5 05 Baselines And Critics V1-wqmqoiUuQHI.mp4

02:28

06. M3 L5 06 Policybased Valuebased And ActorCritic V1-iyin896PNEc.mp4

03:09

06. Policy-based, Value-Based, and Actor-Critic.html

07. A Basic Actor-Critic Agent.html

07. M3 L5 07 A Basic ActorCritic Agent V2-KdHQ24hBKho.mp4

01:58

08. A3C Asynchronous Advantage Actor-Critic, N-step.html

08. M3 L5 08 A3C Asynchronous Advantage ActorCritic V2-twNXFplIAP8.mp4

02:54

09. A3C Asynchronous Advantage Actor-Critic, Parallel Training.html

09. M3 L5 09 A3C Asynchronous Advantage ActorCritic Parallel Training V2-kKRbAKhjACo.mp4

01:56

10. A3C Asynchronous Advantage Actor-Critic, Off- vs On-policy.html

10. M3 L5 10 A3C Asynchronous Advantage ActorCritic Offpolicy Vs Onpolicy V1-AZiy5R0DESU.mp4

04:45

11. A2C Advantage Actor-Critic.html

11. M3 L5 11 A2C Advantage ActorCritic V2-fIWe3xA97DA.mp4

01:52

12. A2C Code Walk-through.html

12. A2c Export V1-LiUBJje2N0c.mp4

14:07

13. GAE Generalized Advantage Estimation.html

13. M3 L5 13 GAE Generalized Advantage Estimation V2-oLFocWp0dt0.mp4

03:42

14. DDPG Deep Deterministic Policy Gradient, Continuous Actions.html

14. M3 L5 14 DDPG Deep Deterministic Policy Gradient Continuous Actionspace V1-0NVOPIyrr98.mp4

03:40

15. DDPG Deep Deterministic Policy Gradient, Soft Updates.html

15. M3 L5 15 DDPG Deep Deterministic Policy Gradient Soft Updates V1-RT-HDnAVe9o.mp4

02:12

16. DDPG Code Walk-through.html

16. DDPG Export V1-08V9r3NgFSE.mp4

11:37

17. M3L517 Summary HS 1 V1-rRuiMhijw_s.mp4

01:28

17. Summary.html

index.html

Part 03-Module 01-Lesson 06_Deep RL for Finance (Optional)

01. Introduction.html

01. M3L601 Introduction HS V1-Nn1PblFSnP8.mp4

00:41

02. High Frequency Trading.html

02. M3L602 High Frequency Trading HFT RENDER V2-oM1zZdZ-8fE.mp4

02:23

03. Challenges of Supervised Learning.html

03. M3L603 Challenges Of Supervised Learning RENDER V1-_hAPnbDtteM.mp4

02:50

04. Advantages of RL for Trading.html

04. M3L04 Advantages Of Reinforcemnt Learning For Trading RENDER V1-rqHL4BZocI8.mp4

02:33

05. M3L606 Optimization SC PT1 V1-6NiRtFyA2DU.mp4

00:59

05. Optimal Liquidation Problem - Part 1 - Introduction.html

06. M3L607 Optimization SC PT2 V1-JzL66ZbTC9U.mp4

01:53

06. Optimal Liquidation Problem - Part 2 - Market Impact.html

07. M3L608 Optimization SC PT3 V1-3pN77gMg788.mp4

02:15

07. Optimal Liquidation Problem - Part 3 - Price Model.html

08. M3L609 Optimization SC PT4 V2-N2LP-wg1jEI.mp4

05:08

08. Optimal Liquidation Problem - Part 4 - Expected Shortfall.html

09. Almgren and Chriss Model.html

09. M3L610 Almgren And Chriss Model SC V1-rokcEQ4LXbU.mp4

04:45

10. M3L611 Trading Lists SC V1-cGT-ADpHR74.mp4

05:38

10. Trading Lists.html

11. M3L612 The Efficient Frontier V1-EwM7Ksbs-ds.mp4

04:46

11. The Efficient Frontier.html

12. DRL for Optimal Execution of Portfolio Transactions.html

index.html

Part 03-Module 01-Lesson 07_Continuous Control

01. Unity ML-Agents.html

02. The Environment - Introduction.html

03. The Environment - Real World.html

04. The Environment - Explore.html

04. Untitled-i2gVvXgOMnc.mp4

00:32

05. Project Instructions.html

06. Benchmark Implementation.html

07. Not sure where to start.html

08. General Advice.html

09. Collaborate!.html

10. Workspace.html

11. (Optional) Challenge Crawl.html

Project Description - Continuous Control.html

Project Rubric - Continuous Control.html

index.html

849-1234251683jkyt.zip
2018-02-27-16-05-37.zip
crawler.zip
idea-2579308-640.zip
image8.zip
output.zip
pic3.zip
reacher.zip
screen-shot-2018-05-02-at-4.56.45-pm.zip
screen-shot-2018-05-03-at-9.10.50-am.zip
screen-shot-2018-07-11-at-11.19.56-am.zip
udacitylogo.zip
unity-wide.zip
unknown.zip

Part 03-Module 02-Lesson 01_Strengthen Your Online Presence Using LinkedIn

01. Get Opportunities with LinkedIn.html

01. Why Network-exjEm9Paszk.mp4

02:00

02. Meet Chris-0ccflD9x5WU.mp4

03:00

02. Use Your Story to Stand Out.html

03. Elevator Pitch-S-nAHPrkQrQ.mp4

02:04

03. Why Use an Elevator Pitch.html

04. Create Your Elevator Pitch.html

04. Elevator Pitch-0QtgTG49E9I.mp4

01:08

04. Pitching to a Recruiter-LxAdWaA-qTQ.mp4

00:50

05. Use Your Elevator Pitch on LinkedIn.html

06. Create Your Profile With SEO In Mind.html

07. Profile Essentials.html

08. Work Experiences Accomplishments.html

09. Build and Strengthen Your Network.html

10. Reaching Out on LinkedIn.html

11. Boost Your Visibility.html

12. Up Next.html

Project Description - Improve Your LinkedIn Profile.html

Project Rubric - Improve Your LinkedIn Profile.html

index.html

profile-pics.zip
redacted-linkedinresults.zip
screen-shot-2018-02-23-at-5.00.25-pm.zip
screen-shot-2018-02-23-at-5.11.40-pm.zip
screen-shot-2018-09-21-at-11.36.43-am.zip
screen-shot-2018-09-21-at-12.02.03-pm.zip
speaking.zip

unnamed-project-desc-0.zip
unnamed-project-desc-1.zip

Part 04-Module 01-Lesson 01_Study Plan

01. Study Plan.html

index.html

udacitylogo.zip

Part 04-Module 01-Lesson 02_Introduction to Multi-Agent RL

01. Introducing Chhavi.html

01. M4 L2 C01 Introducing Chhavi HS V1-imuw8tOMed4.mp4

00:18

02. Introduction to Multi-Agent Systems.html

02. M4 L2 C02 Introduction To Multi Agent Systems V1-ra-w63kzq6I.mp4

01:13

03. M4 L2 C03 Motivation For Multi Agent Systems V1-i_s22qgQYL4.mp4

01:22

03. Motivation for Multi-Agent Systems.html

04. Applications of Multi-Agent Systems.html

04. M4 L2 C04 Applications Of Multi Agent Systems V2-fw0G_gSDm6Q.mp4

01:02

05. Benefits of Multi-Agent Systems.html

05. M4 L2 C05 Benefits Of Multi Agent Systems V2-NXDv9cEZTaw.mp4

01:23

06. M4 L2 C06 Markov Games 2 V1-Y9qq4Jqnwls.mp4

02:22

06. Markov Games.html

07. Markov Games.html

08. Approaches to MARL.html

08. M4 L2 C07 Approaches To MARL V1-uKV9AJykin0.mp4

01:54

09. Cooperation, Competition, Mixed Environments.html

09. M4 L2 C08 Cooperation Competition Mixed Environments A V1-vx6PIH5_oFg.mp4

01:24

10. M4 L2 C09 Paper Description Part I HSAEG V1-nRKrQamUISs.mp4

00:35

10. Research Topics.html

11. M4 L2 C10a Paper Description Part II V1-Ks9-TeCg3Fs.mp4

00:58

11. Paper Description, Part 1.html

12. M4 L2 C10b Paper Description Part II V2-4hFAhtLJR5U.mp4

01:21

12. Paper Description, Part 2.html

13. M4 L2 C11 Summary HS V1-yGPHGYHqjq8.mp4

01:08

13. Summary.html

14. Lab Instructions.html

15. MADDPG - Lab.html

index.html

Part 04-Module 01-Lesson 03_Case Study AlphaZero

01. AlphaZero Preview.html

01. Alpha Zero Preview-Zzc1XJ1aJ-4.mp4

01:51

02. Zero-Sum Game.html

02. Zero-Sum Game-uPw1dHVqdXQ.mp4

03:55

03. Monte Carlo Tree Search 1 - Random Sampling.html

03. Monte Carlo Tree Search 1 - Random Sampling-wn2B3j_Qz6E.mp4

04:58

04. Monte Carlo Tree Search 2 - Expansion and Back-propagation.html

04. Monte Carlo Tree Search 2 - Expansion and Back-propagation-H34Wtk1iNDY.mp4

05:04

05. AlphaZero 1 Guided Tree Search.html

05. AlphaZero 1 Guided Tree Search-LinuRy47xbw.mp4

06:11

06. AlphaZero 2 Self-Play Training.html

06. Alpha Zero 2 Self-Play Training-wl1qfPXqRuQ.mp4

04:10

07. Alphazero python classes walkthrough-hKnBQvtJ_zQ.mp4

12:13

07. TicTacToe using AlphaZero - notebook walkthrough-uUFuBscf98I.mp4

17:14

07. TicTacToe using AlphaZero - walkthrough.html

08. TicTacToe using AlphaZero - Workspace.html

09. Advanced TicTacToe using AlphaZero.html

09. Alphazero advanced tictactoe walkthrough-MOIk_BbCjRw.mp4

07:10

index.html

Part 04-Module 01-Lesson 04_Collaboration and Competition

01. Unity ML-Agents.html

02. The Environment - Introduction.html

03. The Environment - Explore.html

03. Untitled-kxDvrkg8ep0.mp4

00:23

04. Project Instructions.html

05. Benchmark Implementation.html

06. Collaborate!.html

07. Workspace.html

08. (Optional) Challenge Play Soccer.html

Project Description - Collaboration and Competition.html

Project Rubric - Collaboration and Competition.html

index.html

849-1234251683jkyt.zip
2018-02-27-16-05-37.zip
screen-shot-2018-08-16-at-4.37.07-pm.zip
soccer.zip
tennis.zip
unity-wide.zip
we-can-do-it-poster-1393770492mjo.zip

Part 05-Module 01-Lesson 01_Dynamic Programming

01. Introduction.html

01. Introduction-ek2PD9RDrWw.mp4

00:42

02. OpenAI Gym FrozenLakeEnv.html

03. Your Workspace.html

04. Another Gridworld Example.html

04. Another Gridworld Example-n9SbomnLb-U.mp4

01:23

05. An Iterative Method, Part 1.html

05. An Iterative Method-AX-hG3KvwzY.mp4

05:51

06. An Iterative Method, Part 2.html

07. Quiz An Iterative Method.html

08. Iterative Policy Evaluation.html

08. Iterative Policy Evaluation-eDXIL_oOJHI.mp4

04:50

09. Implementation.html

10. Mini Project DP (Parts 0 and 1).html

11. Action Values.html

12. Implementation.html

13. Mini Project DP (Part 2).html

14. Policy Improvement.html

14. Policy Improvement-4_adUEK0IHg.mp4

05:59

15. Implementation.html

16. Mini Project DP (Part 3).html

17. Policy Iteration.html

17. Policy Iteration-gqv7o1kBDc0.mp4

01:29

18. Implementation.html

19. Mini Project DP (Part 4).html

20. Truncated Policy Iteration.html

20. Truncated Policy Iteration-a-RvCxlPMho.mp4

02:28

21. Implementation.html

22. Mini Project DP (Part 5).html

23. Value Iteration.html

23. Value Iteration-XNeQn8N36y8.mp4

02:55

24. Implementation.html

25. Mini Project DP (Part 6).html

26. Check Your Understanding.html

27. Summary.html

index.html

actionvalue.zip
est-action.zip
frozen-lake-6.zip
improve.zip
iteration.zip
policy-eval.zip
screen-shot-2017-09-26-at-2.18.38-pm.zip
screen-shot-2017-09-26-at-4.22.09-pm.zip
screen-shot-2017-09-26-at-11.03.16-pm.zip
screen-shot-2017-10-02-at-10.41.44-am.zip
screen-shot-2017-12-17-at-9.41.03-am.zip
statevalue.zip
truncated-eval.zip
truncated-iter.zip
value-iteration.zip

Part 06-Module 01-Lesson 01_Neural Networks

01. Introducing Luis.html

02. Why Neural Networks.html

02. Why Neural Networks-zAkzOZntK6Y.mp4

00:46

03. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4

02:09

03. Combinando modelos-Boy3zHVrWB4.mp4

03:47

03. Layers-pg99FkXYK0M.mp4

02:08

03. Multiclass Classification-uNTtvxwfox0.mp4

01:19

03. Neural Network Architecture.html

04. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4

04:19

04. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4

01:20

04. Feedforward.html

05. Backpropagation.html

05. Backpropagation V2-1SmY3TZTyUk.mp4

05:12

05. Calculating The Gradient 1 -tVuZDbUrzzI.mp4

02:20

05. Chain Rule-YAhIBOnbt54.mp4

01:14

05. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4

04:37

06. Training Optimization.html

06. Training Optimization-UiGKhx9pUYc.mp4

00:27

07. Testing.html

07. Testing-EeBZpb-PSac.mp4

01:42

08. Overfitting and Underfitting.html

08. Underfitting And Overfitting-xj4PlXMsN-Y.mp4

05:07

09. Early Stopping.html

09. Model Complexity Graph-NnS0FJyVcDQ.mp4

03:40

10. DL 53 Q Regularization-KxROxcRsHL8.mp4

00:52

10. Regularization.html

11. Regularization 2.html

11. Regularization-ndYnUrx8xvs.mp4

06:00

12. Dropout.html

12. Dropout-Ty6K6YiGdBs.mp4

03:08

13. Local Minima.html

13. Local Minima-gF_sW_nY-xw.mp4

00:42

14. Vanishing Gradient.html

14. Vanishing Gradient-W_JJm_5syFw.mp4

01:04

15. Other Activation Functions.html

15. Other Activation Functions-kA-1vUt6cvQ.mp4

01:49

16. Batch vs Stochastic Gradient Descent.html

16. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4

03:04

17. Learning Rate Decay.html

17. Learning Rate-TwJ8aSZoh2U.mp4

00:43

18. Random Restart.html

18. Random Restart-idyBBCzXiqg.mp4

00:17

19. Momentum.html

19. Momentum-r-rYz_PEWC8.mp4

01:46

index.html

luis-circle.zip
regularization-quiz.zip
sigmoid-derivative.zip

Part 06-Module 01-Lesson 02_Convolutional Neural Networks

01. Introducing Cezanne.html

02. 03 Data And Lesson Outline RENDER V2-jPr-5aZA6NE.mp4

01:04

02. Lesson Outline and Data.html

03. CNN Architecture, VGG-16.html

04. Convolutional Layers.html

04. Convolutional Layers (Part 2)-LX-yVob3c28.mp4

07:51

05. Defining Layers in PyTorch.html

06. Notebook Visualizing a Convolutional Layer.html

07. Pooling, VGG-16 Architecture.html

08. Pooling Layers.html

08. Pooling Layers-OkkIZNs7Cyc.mp4

04:14

09. Notebook Visualizing a Pooling Layer.html

10. Fully-Connected Layers, VGG-16.html

11. Notebook Visualizing FashionMNIST.html

12. Training in PyTorch.html

13. Notebook Fashion MNIST Training Exercise.html

14. Notebook FashionMNIST, Solution 1.html

15. Dropout-Ty6K6YiGdBs.mp4

03:08

15. Review Dropout.html

15.-r-rYz_PEWC8.mp4

01:46

16. Notebook FashionMNIST, Solution 2.html

17. 04 Feature Visualization V1 RENDER V2-xwGa7RFg1EQ.mp4

01:46

17. Feature Visualization.html

18. 05 Feature Maps V1RENDER V3-oRhsJHHWtu8.mp4

01:09

18. Feature Maps.html

19. 06 First Convolutional Layer T1 V1 RENDER V2-hIHDMWVSfsM.mp4

02:05

19. First Convolutional Layer.html

20. Visualizing CNNs (Part 2).html

21. 10 Visualizing Activations V1 RENDER V2-CJLNTOXqt3I.mp4

00:54

21. Visualizing Activations.html

22. Notebook Feature Viz for FashionMNIST.html

23. Notebook Visualize Your Net Layers.html

24. Last Feature Vector and t-SNE.html

25. Occlusion, Saliency, and Guided Backpropagation.html

26. 20 Summary Of Feature Viz V2 RENDER V2-r2LBoEkXskU.mp4

01:05

26. Summary of Feature Viz.html

index.html

alexis.zip
cezanne-circle.zip
diagonal-line-1.zip
diagonal-line-2.zip
embedding.zip
grid-layer-1.zip
layer-1-grid.zip
screen-shot-2016-11-24-at-12.08.11-pm.zip
screen-shot-2016-11-24-at-12.09.02-pm.zip
screen-shot-2016-11-24-at-12.09.24-pm.zip
screen-shot-2018-04-06-at-4.54.39-pm.zip
screen-shot-2018-04-23-at-8.35.17-pm.zip
screen-shot-2018-04-23-at-8.35.25-pm.zip
screen-shot-2018-04-24-at-4.49.51-pm.zip
screen-shot-2018-04-24-at-5.08.30-pm.zip
screen-shot-2018-04-24-at-5.08.36-pm.zip
screen-shot-2018-04-24-at-5.47.43-pm.zip
screen-shot-2018-04-24-at-12.35.07-pm.zip
screen-shot-2018-04-24-at-12.47.51-pm.zip
screen-shot-2018-04-24-at-12.58.16-pm.zip
t-sne-mnist.zip
vgg-16.zip

Part 06-Module 01-Lesson 03_Deep Learning with PyTorch

01. Introducing Mat.html

02. Introducing PyTorch.html

03. Part 1 V2-n4mbZYIfKb4.mp4

07:32

03. PyTorch Tensors.html

04. Defining Networks.html

04. Py Part 2 V1-u50_ZyKqt8g.mp4

17:17

05. Py Part 3 V2-u8hDj5aJK6I.mp4

13:09

05. Training Networks.html

06. Fashion-MNIST Exercise.html

06. PyTorch - Part 4-AEJV_RKZ7VU.mp4

01:25

07. Inference Validation.html

07. Py Part 5 V2-coBbbrGZXI0.mp4

13:02

08. Py Part 6 V1-HiTih59dCWQ.mp4

06:09

08. Saving and Loading Trained Networks.html

09. Loading Data Sets with Torchvision.html

09. PyTorch - Part 7-hFu7GTfRWks.mp4

07:04

10. Py Part 8 V1-3eqn5sgCOsY.mp4

09:57

10. Transfer Learning.html

index.html

mat-leonard-circle.zip

Part 07-Module 01-Lesson 01_Cloud Computing

01. Overview.html

02. Create an AWS Account.html

03. Get Access to GPU Instances.html

04. Apply Credits.html

05. Launch an Instance.html

06. Login to the Instance.html

07. Test the Instance.html

index.html

aws-add-sec-group.zip
aws-create-account.zip
aws-inst-stats.zip
edit-security-group.zip
launch.zip
launch-instance.zip
p2xlarge-limit-request.zip
p2-limit-increase.zip
review-and-launch.zip
screen-shot-2017-06-13-at-12.58.03-pm.zip
screen-shot-2017-11-26-at-9.38.24-am.zip
screen-shot-2017-11-26-at-9.55.20-am.zip
screen-shot-2017-11-26-at-10.30.15-am.zip
screen-shot-2018-01-08-at-5.37.22-am.zip
screen-shot-2018-01-08-at-5.38.03-am.zip
stop.zip
ud272-l2-16-10-aws-account.zip

Part 07-Module 01-Lesson 02_Udacity Workspaces

01. Overview.html

02. Introduction to Workspaces.html

03. Workspaces Best Practices.html

index.html

jupyter-logo.zip
workspaces-jupyter.zip
workspaces-menu.zip
workspaces-new.zip
workspaces-notebook.zip
workspaces-submit.zip
workspaces-terminal.zip

Part 08-Module 01-Lesson 01_C++ Getting Started

01. Introduction.html

01. Introduction-ahoiVrq4qAk.mp4

00:58

02. Lesson Overview.html

02. Lesson Overview C++-lR3PH3bL-9U.mp4

01:08

02. Nd113 C3 L1 04 L Lesson Overview 2 V1-DjT2E23xhj8.mp4

00:49

03. Elecia White.html

04. Why C++.html

04. Why C++-_t4ZvwfnuCA.mp4

01:43

05. Python and C++ Comparison.html

06. Static Vs Dynamic Typing-D7v6iIAORkE.mp4

01:47

06. Static vs Dynamic Typing.html

07. C++ - A Statically Typed Language.html

08. Basic Data Types.html

09. Floating versus Double [demonstration].html

10. Doubles Are Bigger-uhwTWgmM2iY.mp4

00:52

10. Doubles are Bigger.html

11. Common Errors and Error Messages.html

12. C++ Functions.html

13. Anatomy of a Function.html

14. Multiple Outputs.html

15. Two Functions Same Name.html

15. Two Functions Same Name-0ZF649G58l4.mp4

00:45

15. Two Functions Same Name-9SgmzOfBmRU.mp4

01:46

16. Function Signatures 1.html

16. Function Signatures 1-T6kQ_4w98IQ.mp4

01:48

17. Function Signatures 2.html

17. Function Signatures 2-Sx4AWTmXl2U.mp4

01:07

17. Function Signatures 3 V1-U3QAFb3AS1M.mp4

00:16

18. If and Boolean Logic.html

19. While and For Loops.html

20. Switch Statement.html

21. Libraries.html

22. Forge on!.html

22. Nd113 C Basics Last Video V1-dtu-RXovl0U.mp4

00:20

index.html

copy-of-template-1.zip
copy-of-template-2.zip
cover.zip
embeddedlogo-04.zip
for.zip
my-drawing.zip
switch.zip
while.zip

Part 08-Module 01-Lesson 02_C++ Vectors

01. C++ Vectors.html

02. Namespaces.html

03. Python Lists vs. C++ Vectors.html

04. Initializing Vector Values.html

05. Vector Methods.html

06. Vectors and For Loops.html

07. Math and Vectors.html

08. 1D Vector Playground.html

09. 2D Vectors.html

10. 2D Vectors and For Loops.html

11. 2D Vector Playground.html

12. Next Lesson.html

index.html

copy-of-template.zip
vectors.zip

Part 08-Module 01-Lesson 03_Practical C++

01. Introduction To Compilation-dyzGEB8YDGg.mp4

03:04

01. Introduction to Compilation.html

02. Running Code Locally.html

03. C++ Versions.html

04. Structuring Functions and File Organization.html

05. Input and Output.html

06. Reading in Text Files.html

07. Outputting to Text Files.html

08. Exercises.html

index.html

Part 08-Module 01-Lesson 04_C++ Object Oriented Programming

01. Introduction.html

01. Introduction-4xHI5LFX-cQ.mp4

00:39

02. Python vs. C++.html

03. Why Use Object Oriented Programming-G2KzZfNu9Ak.mp4

01:30

03. Why use Object Oriented Programming.html

04. Using a Class in C++ [Demo].html

05. Explanation of the Main.cpp File.html

06. Practice Using a Class.html

07. Review Anatomy of a Class.html

08. Other Facets of C++ Classes.html

09. Private and Public.html

10. Header Files.html

11. Inclusion Guards.html

12. Implement a Class.html

13. Class Variables.html

14. Class Function Declarations.html

15. Constructor Functions.html

16. Set and Get Functions.html

17. Matrix Functions.html

18. Use an Inclusion Guard.html

19. Instantiate an Object.html

20. Running your Program Locally.html

index.html

Part 08-Module 02-Lesson 01_C++ Intro to Optimization

01. Course Introduction.html

01. Course Introduction-Lwc5oYApdUM.mp4

00:43

02. C Opt 01 L V2-Kdx1_BI5ddc.mp4

02:10

02. Empathize with the Computer.html

03. 02 L Intro To Comp HW V1 RENDER V1-WDMGkq9mkB8.mp4

03:40

03. Intro to Computer Hardware.html

04. Embedded Terminal Explanation.html

04. Nd113 Embedded Terminal V1-Bhl5JQ_N9V8.mp4

07:54

05. Demo Machine Code.html

06. Assembly Language.html

07. 03 L Binary V1 RENDER V1-K6CpHxnhc2s.mp4

04:40

07. Binary.html

08. Demo Binary.html

09. Demo Binary Floats.html

10. 04 L C And RAM V1 RENDER V1-60jEbKV1UOI.mp4

02:36

10. Memory and the CPU.html

11. Demo Stack vs Heap.html

12. C Opt 05 L V3-rTtZVyWxYG8.mp4

01:28

12. Outro.html

index.html

cli.zip
emptyterminal.zip
files.zip
menu.zip
menuopen.zip
texteditor.zip
untitled-drawing.zip

Part 08-Module 02-Lesson 02_C++ Optimization Practice

01. Introduction.html

02. Software Development and Optimization.html

03. Optimization Techniques.html

04. Dead Code.html

05. Exercise Remove Dead Code.html

06. If Statements.html

07. Exercise If Statements.html

08. For Loops.html

09. Exercise For Loops.html

10. Intermediate Variables.html

11. Exercise Intermediate Variables.html

12. Vector Storage.html

13. Exercise Vector Storage.html

14. References.html

15. Exercise References.html

16. Nd113 Story 1 V1-lIe2zso8A-w.mp4

02:24

16. Sebastian's Synchronization Story.html

17. Static Keyword.html

18. Exercise Static Keyword.html

19. Nd113 C L2 01 V1-h_P7ceb5ido.mp4

00:31

19. Speed Challenge.html

index.html

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Deep Learning

Udacity

View courses Udacity

Udacity, Inc. is an American for-profit educational organization founded by Sebastian Thrun, David Stavens, and Mike Sokolsky offering massive open online courses. According to Thrun, the origin of the name Udacity comes from the company's desire to be "audacious for you, the student".