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Machine & Deep Learning Compendium
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The Machine & Deep Learning Compendium
The Ops Compendium
Types Of Machine Learning
Overview
Model Families
Weakly Supervised
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Active Learning
Online Learning
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Foundation Knowledge
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Data Science Tools
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Calculus
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Probability
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Feature Types
Multi Label Classification
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Distribution Transformation
Normalization & Scaling
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Information Theory
Game Theory
Multi CPU Processing
Benchmarking
Validation & Evaluation
Features
Evaluation Metrics
Datasets
Dataset Confidence
Hyper Parameter Optimization
Training Strategies
Calibration
Datasets Reliability & Correctness
Data & Model Tests
Fairness, Accountability, and Transparency
Interpretable & Explainable AI (XAI)
Federated Learning
Machine Learning
Algorithms 101
Meta Learning (AutoML)
Rules, Probabilistic, Regression
Label Algorithms
Clustering Algorithms
Anomaly Detection
Decision Trees
Active Learning Algorithms
Linear Separator Algorithms
Regression
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
Diffusion models
Classical Graph Models
Graph Theory
Social Network Analysis
Deep Learning
Deep Neural Nets Basics
Deep Neural Frameworks
Embedding
Deep Learning Models
Deep Network Optimization
Attention
Deep Neural Machine Vision
Deep Neural Audio
Deep Neural Tabular
Natural Language Processing
A Reality Check
NLP Tools
Foundation NLP
Name Matching
String Matching
TF-IDF
Language Detection Identification Generation (NLD, NLI, NLG)
Topics Modeling
Named Entity Recognition (NER)
SEARCH
Neural NLP
Tokenization
Decoding Algorithms For NLP
Multi Language
Augmentation
Knowledge Graphs
Annotation & Disagreement
Sentiment Analysis
Question Answering
Summarization
Chat Bots
Foundational Models
Methods
Generative AI
Speech
Prompt
Large Language Models (LLMs)
Vision
GPT
Mix N Match
Stable Diffusion
GenAI Applications
Experimental Design
Design Of Experiments
DOE Tools
A/B Testing
Multi Armed Bandits
Contextual Bandits
Factorial Design
Business Domains
Follow the regularized leader
Growth
Root Cause Effects (RCE/RCA)
Log Parsing / Templatization
Fraud Detection
Life Time Value (LTV)
Survival Analysis
Propaganda Detection
NYC TAXI
Drug Discovery
Intent Recognition
Churn Prediction
Product Management
Expanding Your Data Science Skills
Product Vision & Strategy
Product / Program Managers
Product Management Resources
Product Tools
User Experience Design (UX)
Business
Marketting
MLOps (www.OpsCompendium.com)
DataOps (www.OpsCompendium.com)
Humor
Powered By
GitBook
Recommender Systems
1.
Beginner guide
vidhya
2.
Real python on CF
3.
Intro to, using item-item or user-item
, validating using imdb data, git
4.
Tfidf cosine similarity
,
countvec cosine
5.
Various implementations of CF
, a serious review of algorithms
6.
Collaborative filtering, SVD
7.
Part1,
Spotlight, item2vec, Neural nets for Recommender systems
8.
A general tutorial, has a nice intro
9.
Medium on Movies
1.
Part 1
matrix factorization in movies, users vs movies.
2.
Part 2 using collaborative filtering
using open ai
3.
Part 3 using col-filtering with neural nets
10.
Medium series on collaborative filtering and embeddings
Part 1
,
part 2
,
git
11.
Movie recommender systems
on kaggle
1.
On git
12.
Matrix factorization
13.
Collaborative filtering with binary countvec data, item-item, didnt work well on another domain
14.
Netflix competition, matrix factorization over classical algorithms, a survey paper
15.
Movie similarity based on genre
16.
Similar entities, matrix multiplication
high sparsity
17.
Euclidean distance with high sparse data
18.
Excel & fastai,
git
19.
CF for movie recommendation
20.
Comparison item vs user cf
21.
build a recommendation engine with collaborative filtering
Evaluating Recommender Systems
1.
An exhaustive list of methods to evaluate
2.
Choosing the best for your business
3.
Evaluating
4.
survey of accuracy eval metrics for RS by Microsoft
5.
Building a validation framework
6.
Evaluation Metrics for RS
7.
offline vs online validation
8.
Evaluating RS
TOOLS
1.
Surprise
,
docs
,
2.
Grover prince
,
related article
3.
Recsys
git
Machine Learning - Previous
Learning Classifier Systems
Next - Machine Learning
Timeseries
Last modified
1mo ago