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Part 02-Module 01-Lesson 06_Sentiment Analysis/13. Mini Project 3 Solution-imnxzCev4SI.mp4 57.2 MBPart 02-Module 01-Lesson 06_Sentiment Analysis/14. Understanding Neural Noise-ubqhh4Iv7O4.mp4 52.7 MBPart 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.mp4 50.7 MBPart 06-Module 02-Lesson 03_Policy-Based Methods/07. M2L3 07 V2-ZBLLGIN1EfU.mp4 45.7 MBPart 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.mp4 41.3 MBPart 02-Module 01-Lesson 06_Sentiment Analysis/21. Mini Project 6 Solution-ji0famK7gOQ.mp4 41.0 MBPart 01-Module 01-Lesson 02_Applying Deep Learning/03. Traffic Navigation with Deep Reinforcement Learning-az5ElmV4DhY.mp4 40.0 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA.mp4 38.9 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.mp4 37.9 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/08. 08 Backpropagation Theory V6 Final-Xlgd8I3TWUg.mp4 36.5 MBPart 02-Module 01-Lesson 06_Sentiment Analysis/22. Analysis What's Going on in the Weights-UHsT35pbpcE.mp4 35.3 MBPart 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.mp4 35.0 MBPart 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.mp4 34.8 MBPart 06-Module 02-Lesson 03_Policy-Based Methods/02. M2L3 02 V2-ToS8vXGdODE.mp4 34.1 MBPart 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.mp4 31.9 MBPart 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0)-CsD6b0csU7o.mp4 31.6 MBPart 02-Module 01-Lesson 06_Sentiment Analysis/18. Mini Project 5 Solution-Hv86B_jjWTI.mp4 30.3 MBPart 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.mp4 30.1 MBPart 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.mp4 28.9 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/06. 07 FeedForward B V3-kTYbTVh1d0k.mp4 27.9 MBPart 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.mp4 27.9 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0.mp4 27.0 MBPart 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.mp4 26.9 MBPart 02-Module 01-Lesson 06_Sentiment Analysis/07. Mini Project 1 Solution-l4r5l0HvHRI.mp4 26.0 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/03. 02 RNN History V4 Final-HbxAnYUfRnc.mp4 25.4 MBPart 02-Module 01-Lesson 06_Sentiment Analysis/02. Andrew Trask - Intro-da1I0mea1jQ.mp4 24.9 MBPart 04-Module 01-Lesson 05_Embeddings and Word2vec/02. Implementing Word2Vec-7M431_f9HgE.mp4 24.5 MBPart 04-Module 01-Lesson 06_Sentiment Prediction RNN/07. Sentiment RNN 2-V9YGGjmoHS0.mp4 24.2 MBPart 02-Module 01-Lesson 06_Sentiment Analysis/19. Further Noise Reduction-Kl3hWxizKVg.mp4 23.4 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/16. 18 RNN Example V5 Final-MDLk3fhpTx0.mp4 23.2 MBPart 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.mp4 23.1 MBPart 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.mp4 23.1 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.mp4 22.6 MBPart 03-Module 01-Lesson 05_Autoencoders/05. Convolutional Autoencoders-18SZVRaumGs.mp4 22.6 MBPart 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.mp4 22.4 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/13. 16 RNN B V4 Final-wsif3p5t7CI.mp4 22.1 MBPart 06-Module 02-Lesson 03_Policy-Based Methods/04. M2L3 04 V1-QicxmyE5vTo.mp4 22.0 MBPart 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.mp4 22.0 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/02. 01 RNN Intro V6 Final-AIQEqg6F38A.mp4 21.9 MBPart 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.mp4 21.7 MBPart 02-Module 01-Lesson 06_Sentiment Analysis/16. Understanding Inefficiencies in our Network-4MuS-6ATxCU.mp4 21.7 MBPart 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.mp4 21.2 MBPart 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.mp4 21.1 MBPart 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.mp4 21.0 MBPart 03-Module 01-Lesson 02_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.mp4 20.8 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/05. 05 RNN FFNN Reminder B V6 Final-FfPjaGcZODc.mp4 20.6 MBPart 04-Module 01-Lesson 06_Sentiment Prediction RNN/05. Building The RNN 1-XTD6slf64fM.mp4 20.0 MBPart 06-Module 02-Lesson 03_Policy-Based Methods/03. M2L3 03 V2-TePX-0Bs23E.mp4 19.8 MBPart 03-Module 01-Lesson 02_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.mp4 19.0 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/18. 20 RNN BPTT B V5 Final-bUU9BEQw0IA.mp4 19.0 MBPart 05-Module 01-Lesson 04_Semi-Supervised Learning/05. Building The Generator And Discriminator-OWytckbbeGQ.mp4 18.8 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/06. 06 FeedForward A V7 Final-4rCfnWbx8-0.mp4 18.6 MBPart 03-Module 01-Lesson 02_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.mp4 18.6 MBPart 02-Module 01-Lesson 06_Sentiment Analysis/05. Framing the Problem-IsTOnkAKaJw.mp4 18.5 MBPart 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.mp4 18.3 MBPart 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.mp4 18.2 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/04. 03 RNN Applications V3 Final-6JbTNARuKII.mp4 18.1 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.mp4 18.1 MBPart 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.mp4 17.8 MBPart 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.mp4 17.7 MBPart 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.mp4 17.5 MBPart 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax-4DxoYuR7aZ4.mp4 17.3 MBPart 05-Module 01-Lesson 02_Deep Convolutional GANs/05. DCGAN And The Generator-CH6BxLTKt7s.mp4 16.8 MBPart 05-Module 01-Lesson 01_Generative Adversarial Networks/05. GANs Architecture -gaEs7ccZv_Q.mp4 16.7 MBPart 05-Module 01-Lesson 04_Semi-Supervised Learning/06. Model Loss Exercise-W7TawMNxBds.mp4 16.6 MBPart 03-Module 01-Lesson 05_Autoencoders/03. A-Simple-Autoencoders 21718-lXGdkCT8E1c.mp4 16.4 MBPart 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.mp4 16.4 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.mp4 15.5 MBPart 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.mp4 15.5 MBPart 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.mp4 15.0 MBPart 01-Module 01-Lesson 01_Welcome to Deep Learning/01. 01 Welcome To The Deep Learning Program-3QPEmwq2NaE.mp4 15.0 MBPart 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.mp4 14.8 MBPart 04-Module 01-Lesson 03_Implementation of RNN and LSTM/05. Implementing a Character-wise RNN-KPCMn_jg2oY.mp4 14.3 MBPart 05-Module 01-Lesson 01_Generative Adversarial Networks/04. Games, Equilibrium, GANs Solution Render-2zi8DOWIVas.mp4 14.1 MBPart 03-Module 01-Lesson 02_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.mp4 14.0 MBPart 04-Module 01-Lesson 03_Implementation of RNN and LSTM/13. Build The Network And Results-hu8iMMqajmQ.mp4 13.9 MBPart 03-Module 01-Lesson 02_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.mp4 13.7 MBPart 03-Module 01-Lesson 02_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.mp4 13.6 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.mp4 13.4 MBPart 03-Module 01-Lesson 05_Autoencoders/06. Convolutional Autoencoder Solutions-w3iPs6YnqmY.mp4 13.3 MBPart 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.mp4 13.3 MBPart 05-Module 01-Lesson 04_Semi-Supervised Learning/07. Model Optimization Exercise-wNpI1wUA4Io.mp4 13.3 MBPart 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.mp4 13.2 MBPart 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.mp4 13.1 MBPart 05-Module 01-Lesson 01_Generative Adversarial Networks/02. Cool Things To Do With GANs-bo-ToTdhgew.mp4 12.6 MBPart 02-Module 01-Lesson 06_Sentiment Analysis/23. Andrew Trask - Outro-nIF0GLOQglQ.mp4 12.4 MBPart 05-Module 01-Lesson 01_Generative Adversarial Networks/16. A Trained GAN-TR-uEJcjig4.mp4 12.2 MBPart 02-Module 01-Lesson 02_Implementing Gradient Descent/03. Gradient Descent-Math-7sxA5Ap8AWM.mp4 11.8 MBPart 04-Module 01-Lesson 06_Sentiment Prediction RNN/02. Sentiment Prediction-uGN3rZJRiMY.mp4 11.8 MBPart 03-Module 01-Lesson 04_Weight Initialization/03. Weight Initialization 2-BI3f0Cdc_nU.mp4 11.6 MBPart 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.mp4 11.6 MBPart 03-Module 01-Lesson 04_Weight Initialization/02. Weight Initialization 1-6vXMYu_TQIA.mp4 11.3 MBPart 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.mp4 11.2 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.mp4 11.2 MBPart 01-Module 01-Lesson 05_Matrix Math and NumPy Refresher/02. Data Has Dimensions-F4NSv776X0c.mp4 11.1 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/14. 17 RNN Unfolded V3 Final-xLIA_PTWXog.mp4 11.0 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/12. 14 RNN A V4 Final-ofbnDxGSUcg.mp4 10.9 MBPart 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.mp4 10.9 MBPart 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.mp4 10.9 MBPart 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.mp4 10.8 MBPart 01-Module 01-Lesson 03_Anaconda/02. Why Anaconda-VXukXZv7SCQ.mp4 10.8 MBPart 05-Module 01-Lesson 01_Generative Adversarial Networks/03. Other Generative Models, How GANs Work-MF0QCP1OC9I.mp4 10.8 MBPart 03-Module 01-Lesson 02_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.mp4 10.8 MBPart 02-Module 01-Lesson 06_Sentiment Analysis/11. Building a Neural Network-aM2k7RTjjJI.mp4 10.7 MBPart 05-Module 01-Lesson 04_Semi-Supervised Learning/02. Semi-Supervised Learning-_LRpHPxZaX0.mp4 10.6 MBPart 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.mp4 10.6 MBPart 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.mp4 10.5 MBPart 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.mp4 10.4 MBPart 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.mp4 10.4 MBPart 04-Module 01-Lesson 05_Embeddings and Word2vec/09. Training Results-uISA5ns47s8.mp4 10.3 MBPart 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.mp4 10.2 MBPart 04-Module 01-Lesson 05_Embeddings and Word2vec/03. Subsampling Solution-MAUM_mV_lj8.mp4 10.1 MBPart 04-Module 01-Lesson 04_Hyperparameters/03. Learning Rate-HLMjeDez7ps.mp4 10.1 MBPart 03-Module 01-Lesson 04_Weight Initialization/05. Weight Initialization 4-FM6t7AsodGQ.mp4 10.0 MBPart 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.mp4 9.9 MBPart 03-Module 01-Lesson 06_Transfer Learning in TensorFlow/05. Data Preparation-WfsDMq-b3y4.mp4 9.7 MBPart 02-Module 01-Lesson 06_Sentiment Analysis/10. Mini Project 2 Solution-45ihpPaeO8E.mp4 9.7 MBPart 03-Module 01-Lesson 02_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.mp4 9.6 MBPart 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.mp4 9.5 MBPart 01-Module 01-Lesson 05_Matrix Math and NumPy Refresher/09. Matrix Transposes-NVK5xCY3CZE.mp4 9.5 MBPart 06-Module 02-Lesson 02_Deep Q-Learning/01. 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Training The Network-nknJ3Xu3ld0.mp4 8.8 MBPart 03-Module 01-Lesson 06_Transfer Learning in TensorFlow/02. Transfer Learning--WmQwYr0DYjY.mp4 8.7 MBPart 05-Module 01-Lesson 02_Deep Convolutional GANs/02. Deconvolution-sX_AxtB6CHI.mp4 8.7 MBPart 03-Module 01-Lesson 06_Transfer Learning in TensorFlow/10. Training And Testing-NLPtmQjGYCA.mp4 8.6 MBPart 03-Module 01-Lesson 08_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.mp4 8.6 MBPart 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.mp4 8.5 MBPart 04-Module 01-Lesson 05_Embeddings and Word2vec/06. Building The Network-fhSb5c6UX6M.mp4 8.5 MBPart 03-Module 01-Lesson 02_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4 8.5 MBPart 05-Module 01-Lesson 02_Deep Convolutional GANs/09. Building And Training The Network-nXKk9GI4X14.mp4 8.5 MBPart 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.mp4 8.4 MBPart 08-Module 02-Lesson 01_MiniFlow/01. Miniflow Introduction-Nqp_UifEwt0.mp4 8.4 MBPart 03-Module 01-Lesson 02_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.mp4 8.4 MBPart 03-Module 01-Lesson 02_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.mp4 8.4 MBPart 04-Module 01-Lesson 03_Implementation of RNN and LSTM/07. LSTM Cell-ajC-5uWB8S4.mp4 8.2 MBPart 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.mp4 8.1 MBPart 04-Module 01-Lesson 05_Embeddings and Word2vec/04. Making Batches-jx7qwdw-94k.mp4 8.1 MBPart 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.mp4 8.0 MBPart 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.mp4 8.0 MBPart 02-Module 01-Lesson 03_Training Neural Networks/07. Regularization-ndYnUrx8xvs.mp4 7.9 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/chess-game.jpg 7.9 MBPart 02-Module 01-Lesson 01_Introduction to Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.9 MBPart 05-Module 01-Lesson 04_Semi-Supervised Learning/09. Discriminator Solution-_X8ssUzu_Bo.mp4 7.8 MBPart 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.mp4 7.7 MBPart 01-Module 01-Lesson 05_Matrix Math and NumPy Refresher/07. Matrix Multiplication Part 2-8jtk8BzBdj8.mp4 7.7 MBPart 04-Module 01-Lesson 05_Embeddings and Word2vec/08. Building The Network Solution-pkBAhQ2Ki-8.mp4 7.7 MBPart 04-Module 01-Lesson 01_Recurrent Neural Networks/24. RNN Summary-nXP0oGGRrO8.mp4 7.6 MBPart 05-Module 01-Lesson 02_Deep Convolutional GANs/06. Generator Solution-jyPwUEZg05Q.mp4 7.6 MBPart 02-Module 01-Lesson 01_Introduction to Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.6 MBPart 06-Module 02-Lesson 02_Deep Q-Learning/13. 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