Home └Natural Language with deep learning, Reinforcement learning Computer Science 8
Natural Language with deep learning, Reinforcement learning Computer Science 8

Natural Language with deep learning, Reinforcement learning Computer Science 8


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Last Update 24/02/2022
Completion Time 5 days 7 hours 20 minutes
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Advanced Technical Computer Science
  • Natural language processing with deep learning
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 2 – Word Vectors and Word Senses
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 3 – Neural Networks
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 4 – Backpropagation
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 5 – Dependency Parsing
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 6 – Language Models and RNNs
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 7 – Vanishing Gradients, Fancy RNNs
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 – Translation, Seq2Seq, Attention
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 9 – Practical Tips for Projects
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 10 – Question Answering
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 11 – Convolutional Networks for NLP
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 12 – Subword Models
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 13 – Contextual Word Embeddings
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 14 – Transformers and Self-Attention
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 15 – Natural Language Generation
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 16 – Coreference Resolution
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 17 – Multitask Learning
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 18 – Constituency Parsing, TreeRNNs
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 19 – Bias in AI
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 20 – Future of NLP + Deep Learning
    • Stanford CS224N: NLP with Deep Learning | Winter 2020 | Low Resource Machine Translation
    • Stanford CS224N: NLP with Deep Learning | Winter 2020 | BERT and Other Pre-trained Language Models
    • Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors
  • Reinforcement learning
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 1 - Introduction
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 2 - Given a Model of the World
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 3 - Model-Free Policy Evaluation
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 4 - Model Free Control
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 5 - Value Function Approximation
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 6 - CNNs and Deep Q Learning
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 7 - Imitation Learning
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 8 - Policy Gradient I
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 9 - Policy Gradient II
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 10 - Policy Gradient III & Review
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 11 - Fast Reinforcement Learning
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 12 - Fast Reinforcement Learning II
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 13 - Fast Reinforcement Learning III
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 15 - Batch Reinforcement Learning
    • Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 16 - Monte Carlo Tree Search
    • Q Learning Intro/Table - Reinforcement Learning p.1
    • Q Learning Algorithm and Agent - Reinforcement Learning p.2
    • Q-Learning Agent Analysis - Reinforcement Learning p.3
    • Creating A Reinforcement Learning (RL) Environment - Reinforcement Learning p.4
    • Deep Q Learning w/ DQN - Reinforcement Learning p.5
    • Training & Testing Deep reinforcement learning (DQN) Agent - Reinforcement Learning p.6
  • Introduction to bioinformatics
    • 1. Introduction to Computational and Systems Biology
    • 2. Local Alignment (BLAST) and Statistics
    • 3. Global Alignment of Protein Sequences (NW, SW, PAM, BLOSUM)
    • 4. Comparative Genomic Analysis of Gene Regulation
    • 5. Library Complexity and Short Read Alignment (Mapping)
    • 6. Genome Assembly
    • 7. ChIP-seq Analysis; DNA-protein Interactions
    • 8. RNA-sequence Analysis: Expression, Isoforms
    • 9. Modeling and Discovery of Sequence Motifs
    • 10. Markov and Hidden Markov Models of Genomic and Protein Features
    • 11. RNA Secondary Structure; Biological Functions and Predictions
    • 12. Introduction to Protein Structure; Structure Comparison and Classification
    • 13. Predicting Protein Structure
    • 14. Predicting Protein Interactions
    • 15. Gene Regulatory Networks
    • 16. Protein Interaction Networks
    • 17. Logic Modeling of Cell Signaling Networks
    • 18. Analysis of Chromatin Structure
    • 19. Discovering Quantitative Trait Loci (QTLs)
    • 20. Human Genetics, SNPs, and Genome Wide Associate Studies
    • 21. Synthetic Biology: From Parts to Modules to Therapeutic Systems
    • 22. Causality, Natural Computing, and Engineering Genomes
    • Bioinformatics in Python: Intro
    • Bioinformatics in Python: DNA Toolkit. Part 1: Validating and counting nucleotides.
    • Rosalind Problems: Counting DNA Nucleotides
    • Rosalind Problems: Python Village
    • Bioinformatics in Python: DNA Toolkit. Part 2: Transcription, Reverse Complement
    • Rosalind Problems: Transcription and Reverse Complement
    • Bioinformatics in Python: DNA Toolkit. Part 3: GC Content Calculation
    • Rosalind Problems: GC Content, FASTA File Format, Data Processing
    • Bioinformatics in Python: DNA Toolkit. Part 4: Translation, Codon Usage
    • Bioinformatics Tips & Tricks: Development Tools Setup
    • Bioinformatics in Python: DNA Toolkit. Part 5: Open Reading Frames
    • Rosalind Problems: Fibonacci, Rabbits and Recurrence Relations
    • Bioinformatics in Python: DNA Toolkit. Part 6: Protein search in a reading frame
    • Bioinformatics in Python: DNA Toolkit. Part 7: A search for a real protein from NCBI database
    • Bioinformatics in Python: DNA Toolkit. Part 8.1: Code refactoring into a bio_seq class
    • Bioinformatics in Python: DNA Toolkit. Part 8.2: Code refactoring into a bio_seq class
    • Bioinformatics in Python: DNA Toolkit. Part 9: RNA, Helper functions
    • Bioinformatics Tips & Tricks: Hamming Distance
  • Self-driving cars
    • Self-Driving Cars: State of the Art (2019)
    • Drago Anguelov (Waymo) - MIT Self-Driving Cars
    • MIT Self-Driving Cars (2018)
    • Sacha Arnoud, Director of Engineering, Waymo - MIT Self-Driving Cars
    • Emilio Frazzoli, CTO, nuTonomy - MIT Self-Driving Cars
    • Sterling Anderson, Co-Founder, Aurora - MIT Self-Driving Cars
    • Sertac Karaman (MIT) on Motion Planning in a Complex World - MIT Self-Driving Cars
    • Chris Gerdes (Stanford) on Technology, Policy and Vehicle Safety - MIT Self-Driving Cars
    • Oliver Cameron (CEO, Voyage) - MIT Self-Driving Cars
    • Karl Iagnemma & Oscar Beijbom (Aptiv Autonomous Mobility) - MIT Self-Driving Cars
    • Self-Driving Cars: State of the Art (2019)
  • Machine learning for healthcare
    • 1. What Makes Healthcare Unique?
    • 2. Overview of Clinical Care
    • 3. Deep Dive Into Clinical Data
    • 4. Risk Stratification, Part 1
    • 5. Risk Stratification, Part 2
    • 6. Physiological Time-Series
    • 7. Natural Language Processing (NLP), Part 1
    • 8. Natural Language Processing (NLP), Part 2
    • 9. Translating Technology Into the Clinic
    • 10. Application of Machine Learning to Cardiac Imaging
    • 11. Differential Diagnosis
    • 12. Machine Learning for Pathology
    • 13. Machine Learning for Mammography
    • 14. Causal Inference, Part 1
    • 15. Causal Inference, Part 2
    • 16. Reinforcement Learning, Part 1
    • 17. Reinforcement Learning, Part 2
    • 18. Disease Progression Modeling and Subtyping, Part 1
    • 19. Disease Progression Modeling and Subtyping, Part 2
    • 20. Precision Medicine
    • 21. Automating Clinical Work Flows
    • 22. Regulation of Machine Learning / Artificial Intelligence in the US
    • 23. Fairness
    • 24. Robustness to Dataset Shift
    • 25. Interpretability
    • 1. What Makes Healthcare Unique?