mwickerson / Learn_DATA_SCIENCE_Visually

Becoming a Data Scientist with Python in Grasshopper at Wickerson Studios

Learn_DATA_SCIENCE_Visually

Becoming a Data Scientist with Python in Grasshopper at Wickerson Studios

Files include:

Grasshopper Scripts with

ONE BEGINNER PYTHON 3 NODE SCRIPT &

ONE PARAMETRIC PYTHON 3 NODE SCRIPT &

ONE ADVANCED PYTHON 3 NODE SCRIPT

PART 1:

001 Mathematics

A LINEAR ALGEBRA

001 vectors
002 matrices
003 eigenvectors
004 Factorization
005 Singular Value Decomposition (SVD)
006 Gradient
007 Tensors
008 Jacobian Matrix
009 Curse of Dimensionality

B CALCULUS

001 differentiation
002 integration
003 limits
004 derivatives

C PROBABILITY AND STATISTICS

001 probability theory
002 random variables
003 distributions
004 basic statistics
005 Mean Median Mode
006 Standard Deviation
007 Variance
008 Range
009 Percentile
010 Skewness
011 Kurtosis
012 Sampling Distributions
013 Central Limit Theorem
014 Hypothesis Testing
015 Confidence Intervals
016 T-Tests
017 Analysis of Variance (ANOVA)
018 Chi-Square Test
020 Regression Analysis
021 Bayesian Inference
022 Maximum Likelihood Estimation (MLE)
023 Probability Distributtion
024 Conditional Probability
025 Bayes' Theorem
026 Joint and Marginal Probabilities
027 Independence and Conditional Independence

D OBJECTIVE FUNCTIONS

001 Mean Squared Error (MSE)
002 Mean Absolute Error (MAE)
003 Huber Loss
004 Binary Cross-Entropy (Log Loss)
005 Categorical Cross-Entropy
006 Maximun Likelihood Estimation (MLE)
007 Sparse Categorical Cross-Entropy
008 Hinge Loss
009 Kullback-Leiber Divergence
010 Gini Impurity

E REGULARIZATION

001 L1 Regularization (Lasso Regression)
002 L2 Regularization (Ridge Regression)
003 Elastic Net Regularization
004 Dropout Regularization
005 Data Augmentation
006 Early Stopping
007 Max-Norm Regularization
008 Batch Normalization
009 Weight Decay
010 Total Variation Regularization

F INFORMATION THEORY

001 Entropy
002 Conditional Entropy
003 Joint Entropy
004 Mutual Information
005 Relative Entropy (Kullback-Leibler Divergence)
006 Cross-entropy
007 Information Gain
008 Shannon-Fano Coding
009 Huffman Coding
010 Data Entropy
011 Gradient Descent
012 Stochastic Gradient Descent (SGD)
013 Mini-Batch Gradient Descent
004 Momentum
005 Nesterov Accelerated Gradient (NAG)
006 Adagrad (Adaptive Gradient Algorithm)
007 RMSprop (Root Mean Square Propagation)
008 Adam (Adaptive Moment Estimation)

DISTRIBUTION

001 Bernoulli Distribution
002 Binomial Distribution
003 Multinominal Distribution
004 Normal (Gaussian) Distribution
005 Uniform Distribution
006 Exponential Distribution
007 Poisson Distribution