وب سایت تخصصی شرکت فرین
دسته بندی دوره ها

Nanodegree Program – Become a Deep Reinforcement Learning Expert

سرفصل های دوره

Part 01-Module 01-Lesson 01_Welcome to Deep Reinforcement Learning
  • 01. Welcome To DRLND-i1-l0n1ntes
  • 01. Welcome to the Deep Reinforcement Learning Nanodegree.html
  • 02. RL In The Real World-IGlAyGbOTHo
  • 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
  • 05. Play Tennis!.html
  • 06. Deadline Policy.html
  • 07. Udacity Support.html
  • 08. Community Guidelines.html
  • index.html
      img
    • 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
      img
    • 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
    • screen-shot-2018-11-07-at-10.23.07-pm.zip
    • 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
      img
    • 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
      img
    • 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
  • 02. Applications.html
  • 02. Applications-CV6B84mKRNM
  • 03. The Setting.html
  • 03. The Setting-nh8Gwdu19nc
  • 04. Resources.html
  • 04. Resources-_YPqfAnCqtk
  • index.html

  • Part 01-Module 01-Lesson 06_The RL Framework The Problem
  • 01. Introduction.html
  • 01. Introduction-X_9l_ZqXXBA
  • 02. The Setting, Revisited.html
  • 02. The Setting, Revisited-V6Q1uF8a6kA
  • 03. Episodic vs. Continuing Tasks.html
  • 03. Episodic vs. Continuing Tasks-E1I-BPanSM8
  • 04. Quiz Test Your Intuition.html
  • 05. Quiz Episodic or Continuing.html
  • 06. The Reward Hypothesis.html
  • 06. The Reward Hypothesis-uAqNwgZ49JE
  • 07. Goals and Rewards, Part 1.html
  • 07. Goals and Rewards, Part 1-XPnj3Ya3EuM
  • 08. Goals and Rewards, Part 2.html
  • 08. Goals and Rewards, Part 2-pVIFc72VYH8
  • 09. Quiz Goals and Rewards.html
  • 10. Cumulative Reward.html
  • 10. Cumulative Reward-ysriH65lV9o
  • 11. Discounted Return.html
  • 11. Discounted Return-opXGNPwwn7g
  • 12. Quiz Pole-Balancing.html
  • 13. MDPs, Part 1.html
  • 13. MDPs, Part 1-NBWbluSbxPg
  • 14. MDPs, Part 2.html
  • 14. MDPs, Part 2-CUTtQvxKkNw
  • 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
  • 18. Finite MDPs.html
  • 19. Summary.html
  • index.html
      img
    • 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
    • screen-shot-2017-09-21-at-3.46.12-pm.zip
    • screen-shot-2017-09-21-at-4.34.08-pm.zip
    • 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
  • 02. Policies.html
  • 02. Policies-hc3LrvaC13U
  • 03. Quiz Interpret the Policy.html
  • 04. Gridworld Example.html
  • 04. Gridworld Example-XeHBmPFqTsE
  • 05. State-Value Functions.html
  • 05. State-Value Functions-llakAjwox_8
  • 06. Bellman Equations.html
  • 06. Bellman Equations-UgIaDMvSdUo
  • 07. Quiz State-Value Functions.html
  • 08. Optimality.html
  • 08. Optimality-j231aRV74QM
  • 09. Action-Value Functions.html
  • 09. Action-Value Functions-KJLaRfOOPGA
  • 10. Quiz Action-Value Functions.html
  • 11. Optimal Policies.html
  • 11. Optimal Policies-2rguYpVyCto
  • 12. Quiz Optimal Policies.html
  • 13. Summary.html
  • index.html
      img
    • screen-shot-2017-08-31-at-3.27.10-pm.zip
    • 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
  • 01. Review.html
  • 02. Gridworld Example.html
  • 02. L602 Gridworld Example RENDER V2-2-Lwibg_IfmrA
  • 03. L603 Monte Carlo Methods RENDER V3-2-titaMCRl224
  • 03. Monte Carlo Methods.html
  • 04. L604 MC Prediction Part 1RENDER V2-6ts9gdIS6vg
  • 04. MC Prediction - Part 1.html
  • 05. L605 MC Prediction Part 2 RENDER V3-jR49ZyKuJ98
  • 05. MC Prediction - Part 2.html
  • 06. L606 MC Prediction Part 3 RENDERv1 V4-9LP6uXdmWxQ
  • 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
  • 10. Workspace.html
  • 11. Greedy Policies.html
  • 11. L611 Greedy Policies RENDER V4-DH6c-aODMLU
  • 12. Epsilon-Greedy Policies.html
  • 12. L612 Epsilon Greedy Policies RENDER V4-PxJMtlR06MY
  • 13. MC Control.html
  • 14. Exploration vs. Exploitation.html
  • 15. Incremental Mean.html
  • 15. L615 Incremental Mean RENDER V4-h-8MB7V1LiE
  • 16. Constant-alpha.html
  • 16. L617 Constant Alpha Edits RENDER V1-LetHoOtNdJc
  • 16. MC Control Constant-alpha-QFV1nI9Zpoo
  • 17. Coding Exercise.html
  • 17. M1 L6 S2 V1-6E_3NJcoxmU
  • 18. Workspace.html
  • 19. Summary.html
  • index.html
      img
    • 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
    • screen-shot-2018-04-30-at-10.27.56-am.zip
    • screen-shot-2018-05-01-at-11.12.36-pm.zip
    • screen-shot-2018-05-04-at-2.49.48-pm.zip
    • screen-shot-2018-05-04-at-2.51.59-pm.zip
    • 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
  • 02. Review MC Control Methods.html
  • 03. L602 Gridworld Example RENDER V2-2-Lwibg_IfmrA
  • 03. Quiz MC Control Methods.html
  • 03. Quiz MC Control Methods-ZwIg6LDMyuo
  • 04. TD Control Sarsa.html
  • 04. TD Control Sarsa Part 1-HYV0SP9wm7g
  • 04. TD Control Sarsa Part 2-U_CV-UC9G2c
  • 05. Quiz Sarsa.html
  • 06. TD Control Q-Learning.html
  • 06. TD Control Sarsamax-4DxoYuR7aZ4
  • 07. Quiz Q-Learning.html
  • 08. TD Control Expected Sarsa.html
  • 08. TD Control Expected Sarsa-kEKupCyU0P0
  • 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
      img
    • 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
    • screen-shot-2018-03-07-at-2.43.07-pm.zip
    • 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
      img
    • 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
  • 02. Lesson Overview.html
  • 03. Discrete vs. Continuous Spaces.html
  • 03. Discrete vs. Continuous Spaces-uHstLeRzaE8
  • 04. Quiz Space Representations.html
  • 05. Discretization.html
  • 05. Discretization-j2eZyUpy--E
  • 06. Exercise Discretization.html
  • 07. Workspace Discretization.html
  • 08. Tile Coding.html
  • 08. Tile Coding-BRs7AnTZ_8k
  • 09. Exercise Tile Coding.html
  • 10. Workspace Tile Coding.html
  • 11. Coarse Coding.html
  • 11. Coarse Coding-Uu1J5KLAfTU
  • 12. Function Approximation.html
  • 12. Function Approximation-UTGWVY6jEdg
  • 13. Linear Function Approximation.html
  • 13. Linear Function Approximation-OJ5wrB7o-pI
  • 14. Kernel Functions.html
  • 14. Kernel Functions-RdkPVYyVOvU
  • 15. Non-Linear Function Approximation.html
  • 15. Non-Linear Function Approximation-rITnmpD2mN8
  • 16. Summary.html
  • 16. Summary-MTEBk43oByU
  • index.html
      img
    • 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
  • 01. Congratulations!.html
  • 02. What can you do now.html
  • index.html
      img
    • 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
  • index.html
      img
    • output.zip
    • udacitylogo.zip

  • Part 02-Module 01-Lesson 02_Deep Q-Networks
  • 01. DQN Overview-WgiAvr7COR0
  • 01. From RL to Deep RL.html
  • 01. From RL to Deep RL-7HLJ0uaR1F0
  • 02. Deep Q-Networks.html
  • 02. Deep Q-Networks-GgtR_d1OB-M
  • 03. Experience Replay.html
  • 03. Experience Replay-wX_-SZG-YMQ
  • 04. Fixed Q-Targets.html
  • 04. Fixed Q-Targets-SWpyiEezfp4
  • 05. Deep Q-Learning Algorithm.html
  • 05. Deep Q-Learning Algorithm-MqTXoCxQ_eY
  • 06. Coding Exercise.html
  • 07. Workspace.html
  • 08. Deep Q-Learning Improvements.html
  • 09. 10 Double DQN V2-PGCEMLujiGI
  • 09. Double DQN.html
  • 10. 10 Prioritized Experience Replay V1-cN8z-7Ze9L8
  • 10. Prioritized Experience Replay.html
  • 11. 10 Dueling DQN V2-zZeHbPs39Ls
  • 11. Dueling DQN.html
  • 12. Rainbow.html
  • 13. Summary.html
  • 13. Summary-x6JggcDTcys
  • index.html
      img
    • 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
  • 01. Introduction.html
  • 02. 02 Welcome!-1oElWzRt-lU
  • 02. 03 Transitioning-BvDvxw8e0CY
  • 02. C++ for Robotics.html
  • 03. 03 CC API HSSC HS RENDER V3-a9-HdpCaYW4
  • 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
  • 09. Jetson Overview-i56qM6NNW9A
  • 09. Jetson TX2.html
  • 10. 10 Summary HS V3-cb1FGgZIitc
  • 10. Summary.html
  • index.html
      img
    • 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
  • 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
      img
    • 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
  • 02. Meet the Careers Team.html
  • 03. Access Your Career Portal.html
  • 04. Your Udacity Professional Profile.html
  • index.html
      img
    • 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
  • 03. GitHub profile important items.html
  • 03. GitHub profile important items-prvPVTjVkwQ
  • 04. Good GitHub repository.html
  • 04. Good GitHub repository-qBi8Q1EJdfQ
  • 05. Interview with Art - Part 1.html
  • 05. Interview with Art - Part 1-ClLYamtaO-Q
  • 06. Identify fixes for example bad profile.html
  • 06. Identify fixes for example bad profile-AF07y1oAim0
  • 06. Identify fixes for example bad profile-ncFtwW5urHk
  • 07. Quick Fixes #1.html
  • 07. Quick Fixes-Lb9e2KemR6I
  • 08. Quick Fixes #2.html
  • 08. Quick Fixes #2-It6AEuSDQw0
  • 09. Writing READMEs with Walter.html
  • 09. Writing READMEs with Walter-DQEfT2Zq5_o
  • 10. Interview with Art - Part 2.html
  • 10. Interview with Art - Part 2-Vvzl2J5K7-Y
  • 11. Commit messages best practices.html
  • 12. Reflect on your commit messages.html
  • 12. Reflect on your commit messages-_0AHmKkfjTo
  • 13. Participating in open source projects.html
  • 13. Participating in open source projects-OxL-gMTizUA
  • 14. Interview with Art - Part 3.html
  • 14. Interview with Art - Part 3-M6PKr3S1rPg
  • 15. Participating in open source projects 2.html
  • 15. Participating in open source projects 2-elZCLxVvJrY
  • 16. Starring interesting repositories.html
  • 16. Starring interesting repositories-U3FUxkm1MxI
  • 16. Starring interesting repositories-ZwMY5rAAd7Q
  • 17. Next Steps.html
  • Project Description - Optimize Your GitHub Profile.html
  • Project Rubric - Optimize Your GitHub Profile.html
  • index.html
      img
    • 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
      img
    • udacitylogo.zip

  • Part 03-Module 01-Lesson 02_Introduction to Policy-Based Methods
  • 01. M3 L2 C01 V2-mMnhi8yzwKk
  • 01. Policy-Based Methods.html
  • 02. M3 L2 C02 V1-v8tGjlc2aG4
  • 02. Policy Function Approximation.html
  • 03. More on the Policy.html
  • 04. Hill Climbing.html
  • 04. M3 L2 C04 V3-5E86a0OyVyI
  • 05. Hill Climbing Pseudocode.html
  • 05. M3 L2 C05 V1-0XzzqIXyax0
  • 06. Beyond Hill Climbing.html
  • 06. M2L3 04 V1-QicxmyE5vTo
  • 07. M3 L2 C07 V3-2poDljPvY58
  • 07. More Black-Box Optimization.html
  • 08. Coding Exercise.html
  • 09. Workspace.html
  • 10. OpenAI Request for Research.html
  • 11. M2L3 02 V2-ToS8vXGdODE
  • 11. Why Policy-Based Methods.html
  • 12. Summary.html
  • index.html
      img
    • 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
  • 01. What are Policy Gradient Methods.html
  • 02. M3L3 C02 V6-zoOgRoaxGiU
  • 02. The Big Picture.html
  • 03. Connections to Supervised Learning.html
  • 03. M3L3 C03 V2-dJz_p4FKE-g
  • 04. M3L3 C04 V2-St9ftvMQ_ks
  • 04. Problem Setup.html
  • 05. M3L3 C05 V2-o6CI2q3IXEs
  • 05. REINFORCE.html
  • 06. (Optional) Derivation.html
  • 07. Coding Exercise.html
  • 08. Workspace.html
  • 09. What's Next.html
  • 10. Summary.html
  • index.html
      img
    • 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
  • 02. Lesson Preview.html
  • 02. Training an agent to play atari-pong!-w27mvWFBnvQ
  • 03. Beyond REINFORCE.html
  • 04. Noise Reduction.html
  • 04. Noise Reduction-GCGqT2knFJ0
  • 05. Credit Assignment.html
  • 05. Credit Assignment-tfZw1moB25Y
  • 06. Policy Gradient Quiz.html
  • 07. pong with REINFORCE walkthrough-eKIjPrQWIgg
  • 07. pong with REINFORCE (code walkthrough).html
  • 08. pong with REINFORCE (workspace).html
  • 09. Importance Sampling.html
  • 09. Importance Sampling-cYPvWriOPIk
  • 10. PPO Part 1 The Surrogate Function-Y-boYZlpO7g
  • 10. PPO part 1- The Surrogate Function.html
  • 11. PPO Part 2 Clipping Policy Updates-NRzjGGX6c34
  • 11. PPO part 2- Clipping Policy Updates.html
  • 12. PPO summary.html
  • 12. TLPPO Summary V1-qRAUAAWA_kc
  • 13. Pong with PPO walkthrough-XhfhR7Z01S0
  • 13. pong with PPO (code walkthrough).html
  • 14. pong with PPO (workspace).html
  • index.html
      img
    • 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
  • 02. M3 L5 02 Motivation V1-dpFPlDtdxyQ
  • 02. Motivation.html
  • 03. Bias and Variance.html
  • 03. M3 L5 03 Bias And Variance V2-_vnkkwm46uU
  • 04. M3 L5 04 Two Ways For Estimating Expected Returns V3-2W6yIBDvfsQ
  • 04. Two Ways for Estimating Expected Returns.html
  • 05. Baselines and Critics.html
  • 05. M3 L5 05 Baselines And Critics V1-wqmqoiUuQHI
  • 06. M3 L5 06 Policybased Valuebased And ActorCritic V1-iyin896PNEc
  • 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
  • 08. A3C Asynchronous Advantage Actor-Critic, N-step.html
  • 08. M3 L5 08 A3C Asynchronous Advantage ActorCritic V2-twNXFplIAP8
  • 09. A3C Asynchronous Advantage Actor-Critic, Parallel Training.html
  • 09. M3 L5 09 A3C Asynchronous Advantage ActorCritic Parallel Training V2-kKRbAKhjACo
  • 10. A3C Asynchronous Advantage Actor-Critic, Off- vs On-policy.html
  • 10. M3 L5 10 A3C Asynchronous Advantage ActorCritic Offpolicy Vs Onpolicy V1-AZiy5R0DESU
  • 11. A2C Advantage Actor-Critic.html
  • 11. M3 L5 11 A2C Advantage ActorCritic V2-fIWe3xA97DA
  • 12. A2C Code Walk-through.html
  • 12. A2c Export V1-LiUBJje2N0c
  • 13. GAE Generalized Advantage Estimation.html
  • 13. M3 L5 13 GAE Generalized Advantage Estimation V2-oLFocWp0dt0
  • 14. DDPG Deep Deterministic Policy Gradient, Continuous Actions.html
  • 14. M3 L5 14 DDPG Deep Deterministic Policy Gradient Continuous Actionspace V1-0NVOPIyrr98
  • 15. DDPG Deep Deterministic Policy Gradient, Soft Updates.html
  • 15. M3 L5 15 DDPG Deep Deterministic Policy Gradient Soft Updates V1-RT-HDnAVe9o
  • 16. DDPG Code Walk-through.html
  • 16. DDPG Export V1-08V9r3NgFSE
  • 17. M3L517 Summary HS 1 V1-rRuiMhijw_s
  • 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
  • 02. High Frequency Trading.html
  • 02. M3L602 High Frequency Trading HFT RENDER V2-oM1zZdZ-8fE
  • 03. Challenges of Supervised Learning.html
  • 03. M3L603 Challenges Of Supervised Learning RENDER V1-_hAPnbDtteM
  • 04. Advantages of RL for Trading.html
  • 04. M3L04 Advantages Of Reinforcemnt Learning For Trading RENDER V1-rqHL4BZocI8
  • 05. M3L606 Optimization SC PT1 V1-6NiRtFyA2DU
  • 05. Optimal Liquidation Problem - Part 1 - Introduction.html
  • 06. M3L607 Optimization SC PT2 V1-JzL66ZbTC9U
  • 06. Optimal Liquidation Problem - Part 2 - Market Impact.html
  • 07. M3L608 Optimization SC PT3 V1-3pN77gMg788
  • 07. Optimal Liquidation Problem - Part 3 - Price Model.html
  • 08. M3L609 Optimization SC PT4 V2-N2LP-wg1jEI
  • 08. Optimal Liquidation Problem - Part 4 - Expected Shortfall.html
  • 09. Almgren and Chriss Model.html
  • 09. M3L610 Almgren And Chriss Model SC V1-rokcEQ4LXbU
  • 10. M3L611 Trading Lists SC V1-cGT-ADpHR74
  • 10. Trading Lists.html
  • 11. M3L612 The Efficient Frontier V1-EwM7Ksbs-ds
  • 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
  • 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
      img
    • 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
  • 02. Meet Chris-0ccflD9x5WU
  • 02. Use Your Story to Stand Out.html
  • 03. Elevator Pitch-S-nAHPrkQrQ
  • 03. Why Use an Elevator Pitch.html
  • 04. Create Your Elevator Pitch.html
  • 04. Elevator Pitch-0QtgTG49E9I
  • 04. Pitching to a Recruiter-LxAdWaA-qTQ
  • 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
      img
    • 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
      media
    • unnamed-project-desc-0.zip
    • unnamed-project-desc-1.zip

  • Part 04-Module 01-Lesson 01_Study Plan
  • 01. Study Plan.html
  • index.html
      img
    • 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
  • 02. Introduction to Multi-Agent Systems.html
  • 02. M4 L2 C02 Introduction To Multi Agent Systems V1-ra-w63kzq6I
  • 03. M4 L2 C03 Motivation For Multi Agent Systems V1-i_s22qgQYL4
  • 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
  • 05. Benefits of Multi-Agent Systems.html
  • 05. M4 L2 C05 Benefits Of Multi Agent Systems V2-NXDv9cEZTaw
  • 06. M4 L2 C06 Markov Games 2 V1-Y9qq4Jqnwls
  • 06. Markov Games.html
  • 07. Markov Games.html
  • 08. Approaches to MARL.html
  • 08. M4 L2 C07 Approaches To MARL V1-uKV9AJykin0
  • 09. Cooperation, Competition, Mixed Environments.html
  • 09. M4 L2 C08 Cooperation Competition Mixed Environments A V1-vx6PIH5_oFg
  • 10. M4 L2 C09 Paper Description Part I HSAEG V1-nRKrQamUISs
  • 10. Research Topics.html
  • 11. M4 L2 C10a Paper Description Part II V1-Ks9-TeCg3Fs
  • 11. Paper Description, Part 1.html
  • 12. M4 L2 C10b Paper Description Part II V2-4hFAhtLJR5U
  • 12. Paper Description, Part 2.html
  • 13. M4 L2 C11 Summary HS V1-yGPHGYHqjq8
  • 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
  • 02. Zero-Sum Game.html
  • 02. Zero-Sum Game-uPw1dHVqdXQ
  • 03. Monte Carlo Tree Search 1 - Random Sampling.html
  • 03. Monte Carlo Tree Search 1 - Random Sampling-wn2B3j_Qz6E
  • 04. Monte Carlo Tree Search 2 - Expansion and Back-propagation.html
  • 04. Monte Carlo Tree Search 2 - Expansion and Back-propagation-H34Wtk1iNDY
  • 05. AlphaZero 1 Guided Tree Search.html
  • 05. AlphaZero 1 Guided Tree Search-LinuRy47xbw
  • 06. AlphaZero 2 Self-Play Training.html
  • 06. Alpha Zero 2 Self-Play Training-wl1qfPXqRuQ
  • 07. Alphazero python classes walkthrough-hKnBQvtJ_zQ
  • 07. TicTacToe using AlphaZero - notebook walkthrough-uUFuBscf98I
  • 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
  • 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
  • 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
      img
    • 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
  • 02. OpenAI Gym FrozenLakeEnv.html
  • 03. Your Workspace.html
  • 04. Another Gridworld Example.html
  • 04. Another Gridworld Example-n9SbomnLb-U
  • 05. An Iterative Method, Part 1.html
  • 05. An Iterative Method-AX-hG3KvwzY
  • 06. An Iterative Method, Part 2.html
  • 07. Quiz An Iterative Method.html
  • 08. Iterative Policy Evaluation.html
  • 08. Iterative Policy Evaluation-eDXIL_oOJHI
  • 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
  • 15. Implementation.html
  • 16. Mini Project DP (Part 3).html
  • 17. Policy Iteration.html
  • 17. Policy Iteration-gqv7o1kBDc0
  • 18. Implementation.html
  • 19. Mini Project DP (Part 4).html
  • 20. Truncated Policy Iteration.html
  • 20. Truncated Policy Iteration-a-RvCxlPMho
  • 21. Implementation.html
  • 22. Mini Project DP (Part 5).html
  • 23. Value Iteration.html
  • 23. Value Iteration-XNeQn8N36y8
  • 24. Implementation.html
  • 25. Mini Project DP (Part 6).html
  • 26. Check Your Understanding.html
  • 27. Summary.html
  • index.html
      img
    • 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
  • 03. 29 Neural Network Architecture 2-FWN3Sw5fFoM
  • 03. Combinando modelos-Boy3zHVrWB4
  • 03. Layers-pg99FkXYK0M
  • 03. Multiclass Classification-uNTtvxwfox0
  • 03. Neural Network Architecture.html
  • 04. DL 41 Feedforward FIX V2-hVCuvMGOfyY
  • 04. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs
  • 04. Feedforward.html
  • 05. Backpropagation.html
  • 05. Backpropagation V2-1SmY3TZTyUk
  • 05. Calculating The Gradient 1 -tVuZDbUrzzI
  • 05. Chain Rule-YAhIBOnbt54
  • 05. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4
  • 06. Training Optimization.html
  • 06. Training Optimization-UiGKhx9pUYc
  • 07. Testing.html
  • 07. Testing-EeBZpb-PSac
  • 08. Overfitting and Underfitting.html
  • 08. Underfitting And Overfitting-xj4PlXMsN-Y
  • 09. Early Stopping.html
  • 09. Model Complexity Graph-NnS0FJyVcDQ
  • 10. DL 53 Q Regularization-KxROxcRsHL8
  • 10. Regularization.html
  • 11. Regularization 2.html
  • 11. Regularization-ndYnUrx8xvs
  • 12. Dropout.html
  • 12. Dropout-Ty6K6YiGdBs
  • 13. Local Minima.html
  • 13. Local Minima-gF_sW_nY-xw
  • 14. Vanishing Gradient.html
  • 14. Vanishing Gradient-W_JJm_5syFw
  • 15. Other Activation Functions.html
  • 15. Other Activation Functions-kA-1vUt6cvQ
  • 16. Batch vs Stochastic Gradient Descent.html
  • 16. Batch vs Stochastic Gradient Descent-2p58rVgqsgo
  • 17. Learning Rate Decay.html
  • 17. Learning Rate-TwJ8aSZoh2U
  • 18. Random Restart.html
  • 18. Random Restart-idyBBCzXiqg
  • 19. Momentum.html
  • 19. Momentum-r-rYz_PEWC8
  • index.html
      img
    • 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
  • 02. Lesson Outline and Data.html
  • 03. CNN Architecture, VGG-16.html
  • 04. Convolutional Layers.html
  • 04. Convolutional Layers (Part 2)-LX-yVob3c28
  • 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
  • 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
  • 15. Review Dropout.html
  • 15.-r-rYz_PEWC8
  • 16. Notebook FashionMNIST, Solution 2.html
  • 17. 04 Feature Visualization V1 RENDER V2-xwGa7RFg1EQ
  • 17. Feature Visualization.html
  • 18. 05 Feature Maps V1RENDER V3-oRhsJHHWtu8
  • 18. Feature Maps.html
  • 19. 06 First Convolutional Layer T1 V1 RENDER V2-hIHDMWVSfsM
  • 19. First Convolutional Layer.html
  • 20. Visualizing CNNs (Part 2).html
  • 21. 10 Visualizing Activations V1 RENDER V2-CJLNTOXqt3I
  • 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
  • 26. Summary of Feature Viz.html
  • index.html
      img
    • 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
  • 03. PyTorch Tensors.html
  • 04. Defining Networks.html
  • 04. Py Part 2 V1-u50_ZyKqt8g
  • 05. Py Part 3 V2-u8hDj5aJK6I
  • 05. Training Networks.html
  • 06. Fashion-MNIST Exercise.html
  • 06. PyTorch - Part 4-AEJV_RKZ7VU
  • 07. Inference Validation.html
  • 07. Py Part 5 V2-coBbbrGZXI0
  • 08. Py Part 6 V1-HiTih59dCWQ
  • 08. Saving and Loading Trained Networks.html
  • 09. Loading Data Sets with Torchvision.html
  • 09. PyTorch - Part 7-hFu7GTfRWks
  • 10. Py Part 8 V1-3eqn5sgCOsY
  • 10. Transfer Learning.html
  • index.html
      img
    • 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
      img
    • 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
      img
    • 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
  • 02. Lesson Overview.html
  • 02. Lesson Overview C++-lR3PH3bL-9U
  • 02. Nd113 C3 L1 04 L Lesson Overview 2 V1-DjT2E23xhj8
  • 03. Elecia White.html
  • 04. Why C++.html
  • 04. Why C++-_t4ZvwfnuCA
  • 05. Python and C++ Comparison.html
  • 06. Static Vs Dynamic Typing-D7v6iIAORkE
  • 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
  • 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
  • 15. Two Functions Same Name-9SgmzOfBmRU
  • 16. Function Signatures 1.html
  • 16. Function Signatures 1-T6kQ_4w98IQ
  • 17. Function Signatures 2.html
  • 17. Function Signatures 2-Sx4AWTmXl2U
  • 17. Function Signatures 3 V1-U3QAFb3AS1M
  • 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
  • index.html
      img
    • 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
      img
    • copy-of-template.zip
    • vectors.zip

  • Part 08-Module 01-Lesson 03_Practical C++
  • 01. Introduction To Compilation-dyzGEB8YDGg
  • 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
  • 02. Python vs. C++.html
  • 03. Why Use Object Oriented Programming-G2KzZfNu9Ak
  • 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
  • 02. C Opt 01 L V2-Kdx1_BI5ddc
  • 02. Empathize with the Computer.html
  • 03. 02 L Intro To Comp HW V1 RENDER V1-WDMGkq9mkB8
  • 03. Intro to Computer Hardware.html
  • 04. Embedded Terminal Explanation.html
  • 04. Nd113 Embedded Terminal V1-Bhl5JQ_N9V8
  • 05. Demo Machine Code.html
  • 06. Assembly Language.html
  • 07. 03 L Binary V1 RENDER V1-K6CpHxnhc2s
  • 07. Binary.html
  • 08. Demo Binary.html
  • 09. Demo Binary Floats.html
  • 10. 04 L C And RAM V1 RENDER V1-60jEbKV1UOI
  • 10. Memory and the CPU.html
  • 11. Demo Stack vs Heap.html
  • 12. C Opt 05 L V3-rTtZVyWxYG8
  • 12. Outro.html
  • index.html
      img
    • 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
  • 16. Sebastian's Synchronization Story.html
  • 17. Static Keyword.html
  • 18. Exercise Static Keyword.html
  • 19. Nd113 C L2 01 V1-h_P7ceb5ido
  • 19. Speed Challenge.html
  • index.html
  • 45,900 تومان
    بیش از یک محصول به صورت دانلودی میخواهید؟ محصول را به سبد خرید اضافه کنید.
    خرید دانلودی فوری

    در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.

    ایمیل شما:
    شناسه: 7998
    حجم: 2345 مگابایت
    مدت زمان: 709 دقیقه
    تاریخ انتشار: 20 اسفند 1401
    طراحی سایت و خدمات سئو

    45,900 تومان
    افزودن به سبد خرید