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Stanford CS224N - NLP w/ DL | Winter 2021 | Lecture 5 - Recurrent Neural networks (RNNs)

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Stanford CS224N - NLP w/ DL | Winter 2021 | Lecture 4 - Syntactic Structure and Dependency Parsing

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Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 3 - Backprop and Neural Networks

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Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 2 - Neural Classifiers

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RL Debugging and Diagnostics | Stanford CS229: Machine Learning Andrew Ng - Lecture 20 (Autumn 2018)

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Lecture 19 - Reward Model & Linear Dynamical System | Stanford CS229: Machine Learning (Autumn 2018)

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Lecture 18 - Continous State MDP & Model Simulation | Stanford CS229: Machine Learning (Autumn 2018)

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Lecture 17 - MDPs & Value/Policy Iteration | Stanford CS229: Machine Learning Andrew Ng (Autumn2018)

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Lecture 16 - Independent Component Analysis & RL | Stanford CS229: Machine Learning (Autumn 2018)

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Lecture 15 - EM Algorithm & Factor Analysis | Stanford CS229: Machine Learning Andrew Ng -Autumn2018

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Lecture 14 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018)

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Lecture 13 - Debugging ML Models and Error Analysis | Stanford CS229: Machine Learning (Autumn 2018)

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Lecture 12 - Backprop & Improving Neural Networks | Stanford CS229: Machine Learning (Autumn 2018)

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Lecture 11 - Introduction to Neural Networks | Stanford CS229: Machine Learning (Autumn 2018)

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Lecture 10 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018)

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Lecture 9 - Approx/Estimation Error & ERM | Stanford CS229: Machine Learning (Autumn 2018)

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Lecture 8 - Data Splits, Models & Cross-Validation | Stanford CS229: Machine Learning (Autumn 2018)

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Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)

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Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)

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Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)

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