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Machine & Deep Learning Compendium
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The Machine & Deep Learning Compendium
Types Of Machine Learning
Data Science
Data Science Tools
Management
Data Science Management
Calculus
Probability & Statistics
Probability
Feature Types
Features
Calibration
Multi Label Classification
Distribution
Distribution Transformation
Information Theory
Game Theory
Datasets
Dataset Confidence
Normalization & Scaling
Regularization
Validation
Meta Learning
Evaluation Metrics
Benchmarking
Hyper Parameter Optimization
Multi CPU Processing
Algorithms 101
Training Strategies
Classic Machine Learning
Label Algorithms
Clustering Algorithms
Anomaly Detection
Decision Trees
Active Learning Algorithms
Linear Separator Algorithms
Ensembles
Reinforcement Learning
Incremental Learning
Dimensionality Reduction Methods
Genetic Algorithms & Genetic Programming
Learning Classifier Systems
Recommender Systems
Timeseries
Fourier Transform
Digital Signal Processing (DSP)
Propensity Score Matching
Natural Language Processing
Graphs
Deep Learning
Experimental Design
Product
Business Domains For Data Science
MLOPs Engineering
Model Formats
Data Engineering
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Recommender Systems
  1. 1.
    ​Beginner guide vidhya
  2. 2.
    ​Real python on CF​
  3. 3.
    ​Intro to, using item-item or user-item, validating using imdb data, git
  4. 4.
    ​Tfidf cosine similarity, countvec cosine​
  5. 5.
    ​Various implementations of CF, a serious review of algorithms
  6. 6.
    ​Collaborative filtering, SVD​
  7. 7.
    ​Part1, Spotlight, item2vec, Neural nets for Recommender systems​
  8. 8.
    ​A general tutorial, has a nice intro​
  9. 9.
    Medium on Movies
    1. 1.
      Part 1 matrix factorization in movies, users vs movies.​ ​
    2. 2.
      ​Part 2 using collaborative filtering using open ai
    3. 3.
      ​Part 3 using col-filtering with neural nets​
  10. 10.
    Medium series on collaborative filtering and embeddings Part 1, part 2, git​
  11. 11.
    ​Movie recommender systems on kaggle
    1. 1.
      ​On git​
  12. 12.
    ​Matrix factorization ​
  13. 13.
    ​Collaborative filtering with binary countvec data, item-item, didnt work well on another domain​
  14. 14.
    ​Netflix competition, matrix factorization over classical algorithms, a survey paper​
  15. 15.
    ​Movie similarity based on genre ​
  16. 16.
    ​Similar entities, matrix multiplication high sparsity
  17. 17.
    ​Euclidean distance with high sparse data​
  18. 18.
    Excel & fastai, git​
  19. 19.
    ​CF for movie recommendation​
  20. 20.
    ​Comparison item vs user cf​

TOOLS

​Recsys git
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