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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
Semi Supervised
Active Learning
Online Learning
N-Shot Learning
Foundation Knowledge
Data Science
Data Science Tools
Management
Project & Program Management
Data Science Management
Calculus
Probability & Statistics
Probability
Hypothesis Testing
Feature Types
Multi Label Classification
Distribution
Distribution Transformation
Normalization & Scaling
Regularization
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
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GitBook
Data Science Management
INTERVIEW Qs
1.
40 questions on ensembles
2.
30 on trees
3.
30 on knns
Politics
1.
The most difficult thing in ds, politics
HIRING / RECRUITING
1.
Data engineer skills
on medium
1.
Coding (Typically Python)
2.
SQL
3.
Database design
4.
Data architecture/big data technologies
5.
Soft skills
WRITING DOCS
1.
Design docs at google
LEGAL & CONTRACTS
1.
(FAST) Advisory board saas agreement
General
1.
The Care and Feeding of Data Scientists - O'reilly
Due Diligence
1.
by Inbal Budowski Tal
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Last modified
1yr ago