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      • 0-README.md
      • 1 requirements.txt
      • 2 a-super-harsh-guide-to-ml
      • 3 Choose Your Fighter
      • 4 Checklists
      • 5 Pre-Interview
      • 6 In-Interview
      • 7 Post-Interview
        • 1.1-intro-questions
        • 1.2 Easy Questions
        • 1.3 Hard Questions
        • 1.4 linear-algebra-and-calculus-for-ml
        • 1.1 Databases Crash Course
        • 1.2 SQL
        • 1.2 SQL - Minecraft Case Study
        • 1.3 Excel
        • 1.3 json and xml
        • 1.4 NoSQL
        • 1.8 Blockchain
        • 2.5 Regex
        • html and json
        • Web apis
        • 2.0 Preface
        • 2.2 Logistic Regression
        • 2.3 Decision Trees
        • 2.4 Random Forests
        • 2.5 Bagging and Boosting
        • 2.5 KNN
        • 2.6 Ada-Boost
        • 2.7 Gradient Boosting
        • 2.8 XG-Boost
        • 2.9.1 Support Vector Classifiers
        • 2.9.2 Support Vector Machines
        • 2.9.3 Radial Basis Function
        • 2.10 Multinomial Naive Bayes
        • 2.11 Gaussian Naive Bayes
        • 2.12.1 t-SNE
        • 2.13 Factor Analysis of Correspondences
        • 2.13 UMAP
        • 2.14 Clustering (5 Models)
        • 2.16 Hyperparameter Tuning
        • 2.17 Correlation Matrices and Heatmaps
        • 2.18 Monte-Carlo
        • 2.19 Time Series Models
        • 2.20 Recommender Systems
        • 2.21 Matrix Factorization for Recsys
        • 2.21 Page Rank
        • 2.X Algorithms and Deep Learning
        • 2.X Monte Carlo Simulation
        • BoW
        • clustering-algorithms
        • Gaussian Mixture Models
        • Independent Component Analysis
        • Linear Regression
        • NLP
        • Principal Component Analysis
        • ROC
        • Word2Vec
        • (3.0) how-would-you-model
        • (3.1) Case Study Questions
        • (3.3) Cambridge Analytica
        • (3.X) Cloudflare
        • 3.5 Iconic Datasets
      • ml-index
        • 1.1 Easy Questions
        • 1.2 Hard Questions
        • 1.3 Case Studies
        • (0) index
        • (1.1) Intro to Probability Distributions
        • (1.2) Bernoulli
        • (1.3) Normal Distribution
        • (1.4) Binomial
        • (1.5) Multinomial
        • (1.6) Negative Binomial
        • (1.7) Geometric
        • (1.8) The Poisson Process
        • (1.9) Poisson
        • (1.10) Exponential
        • (1.11) Gamma
        • (1.12) Chi-Squared
        • (1.13) Beta
        • (1.14) Hypergeometric
        • (1.15) Bivariate Normal
        • (1.16) Log-Normal
        • (1.17) Weibull
        • (1.18) Log-Weibull (Cauchy)
        • (1.19) Pareto
        • (1.XX) Hard Distribution Questions
        • (1.1) Easy Questions
        • (1.2) Hard Questions
        • (1.2) Hard Questions 2
        • Correlation Matrices and Heatmaps
        • (4.1) Combinatorics
        • (4.2) Time Series Analysis
        • (4.3) Markov Chains
        • brownian-motion
      • probstat-index
        • (0.2) Gradient Descent
        • (0.4) Stochastic Gradient Descent
        • (1.3) Backpropogation
        • 1.1 neural-networks
        • 1.1-intro-questions
        • (1.2) CNN's
        • (1.3) RNN's
        • (1.4) LSTM's
        • (1.5) GNN's
        • (1.6) Autoencoders
        • (1.7) GAN's
        • (1.8) BERT
        • (1.9) Transformer Models
        • (1.10) GPT's Generative Pretrained Transformers
        • (1.11) LLM's
        • (1.21) U-Nets
        • Bigram models
        • CBOW
        • Discriminative Models
        • Efficient-Net
        • Hidden Markov Model
        • More CNN's
        • Multimodal Parallel Network
        • Q*
        • Reinforcement Learning
        • Residual Neural Network
        • Stable Diffusion
        • WAVENET
        • 3.0 how-would-you-model
        • 3.1 chatgpt
        • 3.2 alphago
        • 3.3 eliza
        • 3.4 google-deepmind
        • 3.5 zoom
        • 3.6 wifi-through-walls
        • 3.7 3d-gaussian-splatting
        • 3.8 4d-gaussian-splatting
        • 3.9 hide-and-seek
      • dl-index
        • (1) Live Data Manipulation
        • (2) Kaggle
        • 2.1 Introduction
        • 2.2 Pythonese (easy)
        • 2.3 Pythonese (hard)
        • 2.3 Pythonese (superhard)
        • 2.4 pandas
        • 4.4 matplotlib
        • 4.5 numpy
        • 4.5 scipy
        • 4.5 sklearn
        • 4.6 jupyter pytorch tensorflow
        • 4.7 Scipy Optimize
        • Tensorflow
          • (5.1) Hyper Log Log
          • (5.2) Simulated Annealing
          • 5.1 Linear Programming
          • A*
          • Beam Search
          • Dijkstras
          • Heuristic
        • 2.1 Introduction
        • 2.2 Arrays and Hashing
        • 2.3 Two Pointers
        • 2.4 Stack
        • 2.5 Binary Search
        • 2.6 Sliding Window
        • 2.7 Linked List
        • 2.8 Trees
        • 2.9 Tries
        • 2.10 Backtracking
        • 2.11 Heap & Priority Queue
        • 2.12 Graphs
        • 2.13 1-D Dynamic Programming
        • 2.14 Intervals
        • 2.15 Intervals
        • 2.16 Advanced Graphs
        • 2.17 2-D Dynamic Programming
        • 2.18 Bit Manipulation
        • 2.19 Math & Geometry
        • Case Study
        • Iconic Algorithms
        • Things to Learn
        • (2.8) Git
        • 1.5 Compute
        • 2.1 Software Development
        • 2.2 Object Oriented Programming
        • 2.3 C++ <3
        • 2.3 Command Line
        • 2.5 Go
        • 2.6 Numerical Methods
        • 2.7 Docker
        • 2.8 YAML
        • 2.9 AWS
        • 2.10 Hadoop and Spark
        • Case Study Questions
        • django
        • vim-nvim
      • coding-index
        • 1.1 Easy Questions
        • 1.2 Hard Questions
        • 1.3 Quantitative Riddles
        • 2.4 Twenty Four (practice)
        • 1.4 Linear Algebra (easy)
        • 1.4 Linear Algebra (hard)
        • 1.5 Calculus
        • 1.6 real-analysis
        • Case Study
        • probability-hard
        • 6.1 Questions
        • numerical-optimization
        • 1.1 intro
        • 1.2 finance-fundamentals
        • 1.3 portfolio-management-and-investments
        • 1.4 cdfm
        • 1.6 options-101
        • 1.7 futures
        • martingale
        • 10.1 black-scholes
        • capm
        • Untitled
        • linear-programming
        • operations-research
        • pigeonhole-principle
            • delta
            • gamma
            • rho
            • theta
            • vega
          • debt
          • derivatives
          • equity
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      • Euler's constant
      • linear algebra
      • Untitled
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