📒
Machine & Deep Learning Compendium
  • The Machine & Deep Learning Compendium
    • Thanks Page
  • The Ops Compendium
  • Types Of Machine Learning
    • Overview
    • Model Families
    • Weakly Supervised
    • Semi Supervised
    • Active Learning
    • Online Learning
    • N-Shot Learning
    • Unlearning
  • 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)
    • Probabilistic, Regression
    • Data Mining
    • Process Mining
    • 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 Tabular
    • Deep Neural Time Series
  • Audio
    • Basics
    • Terminology
    • Feature Engineering
    • Deep Neural Audio
    • Algorithms
  • 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
    • Conversation
  • Generative AI
    • Methods
    • Gen AI Industry
    • Speech
    • Prompt
    • Fairness, Accountability, and Transparency In Prompts
    • Large Language Models (LLMs)
    • Vision
    • GPT
    • Mix N Match
    • Diffusion Models
    • GenAI Applications
    • Agents
    • RAG
    • Chat UI/UX
  • 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
    • Electronic Network Frequency Analysis
    • Marketing
  • Product Management
    • Expanding Your Data Science Skills
    • Product Vision & Strategy
    • Product / Program Managers
    • Product Management Resources
    • Product Tools
    • User Experience Design (UX)
    • Business
    • Marketing
    • Ideation
  • MLOps (www.OpsCompendium.com)
  • DataOps (www.OpsCompendium.com)
  • Humor
Powered by GitBook
On this page
  • INTERVIEW Qs
  • Politics
  • HIRING / RECRUITING
  • WRITING DOCS
  • LEGAL & CONTRACTS
  • General
  • Due Diligence

Was this helpful?

  1. Foundation Knowledge

Data Science Management

PreviousProject & Program ManagementNextCalculus

Last updated 3 years ago

Was this helpful?

INTERVIEW Qs

Politics

HIRING / RECRUITING

  1. on medium

    1. Coding (Typically Python)

    2. SQL

    3. Database design

    4. Data architecture/big data technologies

    5. Soft skills

WRITING DOCS

LEGAL & CONTRACTS

General

Due Diligence

40 questions on ensembles
30 on trees
30 on knns
The most difficult thing in ds, politics
Data engineer skills
Design docs at google
(FAST) Advisory board saas agreement
The Care and Feeding of Data Scientists - O'reilly
by Inbal Budowski Tal