Канал: StatQuest with Josh Starmer

Human Stories in AI: Tommy Tang

Human Stories in AI: Tommy Tang

Luis Serrano + Josh Starmer Q&A Livestream!!!

Luis Serrano + Josh Starmer Q&A Livestream!!!

Human Stories in AI: Simon Stochholm

Human Stories in AI: Simon Stochholm

Log_e Song - Official Lyric Video

Log_e Song - Official Lyric Video

Human Stories in AI: Brian Risk@devra.ai

Human Stories in AI: Brian Risk@devra.ai

The matrix math behind transformer neural networks, one step at a time!!!

The matrix math behind transformer neural networks, one step at a time!!!

Human Stories in AI: Fabio Urbina

Human Stories in AI: Fabio Urbina

Human Stories in AI: Khushi Jain

Human Stories in AI: Khushi Jain

Human Stories in AI: Achal Dixit

Human Stories in AI: Achal Dixit

Human Stories in AI: Rick Marks

Human Stories in AI: Rick Marks

Essential Matrix Algebra for Neural Networks, Clearly Explained!!!

Essential Matrix Algebra for Neural Networks, Clearly Explained!!!

Word Embedding in PyTorch + Lightning

Word Embedding in PyTorch + Lightning

The Golden Play Button, Clearly Explained!!!’

The Golden Play Button, Clearly Explained!!!’

Another 3 lessons from my Pop!!!

Another 3 lessons from my Pop!!!

Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!

Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!

Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!

Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!

What is a Logit?

What is a Logit?

Logistic vs Logit Functions

Logistic vs Logit Functions

Attention for Neural Networks, Clearly Explained!!!

Attention for Neural Networks, Clearly Explained!!!

Likelihood vs Probability

Likelihood vs Probability

p-values

p-values

Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!

Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!

Matrix Multiplication

Matrix Multiplication

Matrix Notation

Matrix Notation

The Ukulele: Clearly Explained!!!

The Ukulele: Clearly Explained!!!

PCA Eigenvalues

PCA Eigenvalues

PCA Eigenvectors

PCA Eigenvectors

Normalized Data

Normalized Data

Standardized Data

Standardized Data

Type 2 Errors

Type 2 Errors

Type 1 Errors

Type 1 Errors

Word Embedding and Word2Vec, Clearly Explained!!!

Word Embedding and Word2Vec, Clearly Explained!!!

The AI Buzz, Episode #5: A new wave of AI-based products and the resurgence of personal applications

The AI Buzz, Episode #5: A new wave of AI-based products and the resurgence of personal applications

CatBoost Part 2: Building and Using Trees

CatBoost Part 2: Building and Using Trees

CatBoost Part 1: Ordered Target Encoding

CatBoost Part 1: Ordered Target Encoding

The AI Buzz, Episode #4: ChatGPT + Bing and How to start an AI company in 3 easy steps.

The AI Buzz, Episode #4: ChatGPT + Bing and How to start an AI company in 3 easy steps.

One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!

One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!

The AI Buzz, Episode #3: Constitutional AI, Emergent Abilities and Foundation Models

The AI Buzz, Episode #3: Constitutional AI, Emergent Abilities and Foundation Models

Mutual Information, Clearly Explained!!!

Mutual Information, Clearly Explained!!!

Cosine Similarity, Clearly Explained!!!

Cosine Similarity, Clearly Explained!!!

Long Short-Term Memory with PyTorch + Lightning

Long Short-Term Memory with PyTorch + Lightning

The AI Buzz, Episode #2: Big data, Reinforcement Learning and Aligning Models

The AI Buzz, Episode #2: Big data, Reinforcement Learning and Aligning Models

The AI Buzz, Episode #1: ChatGPT, Transformers and Attention

The AI Buzz, Episode #1: ChatGPT, Transformers and Attention

Design Matrix Examples in R, Clearly Explained!!!

Design Matrix Examples in R, Clearly Explained!!!

Design Matrices For Linear Models, Clearly Explained!!!

Design Matrices For Linear Models, Clearly Explained!!!

Using Linear Models for t tests and ANOVA, Clearly Explained!!!

Using Linear Models for t tests and ANOVA, Clearly Explained!!!

Multiple Regression in R, Step by Step!!!

Multiple Regression in R, Step by Step!!!

Multiple Regression, Clearly Explained!!!

Multiple Regression, Clearly Explained!!!

Linear Regression in R, Step by Step

Linear Regression in R, Step by Step

Linear Regression, Clearly Explained!!!

Linear Regression, Clearly Explained!!!

R-squared, Clearly Explained!!!

R-squared, Clearly Explained!!!

Long Short-Term Memory (LSTM), Clearly Explained

Long Short-Term Memory (LSTM), Clearly Explained

The Cosine Similarity for NLP and CatBoost

The Cosine Similarity for NLP and CatBoost

Happy Halloween (Neural Networks Are Not Scary)

Happy Halloween (Neural Networks Are Not Scary)

Handmade Pasta, Clearly Explained!!!

Handmade Pasta, Clearly Explained!!!

Live Stream - More details about Target Encoding/AMA/Silly Songs

Live Stream - More details about Target Encoding/AMA/Silly Songs

Live Stream - Target Encoding/AMA/Silly Songs!!!

Live Stream - Target Encoding/AMA/Silly Songs!!!

StatQuest with Josh Starmer Live Stream

StatQuest with Josh Starmer Live Stream

Introduction to Coding Neural Networks with PyTorch and Lightning

Introduction to Coding Neural Networks with PyTorch and Lightning

Three more lessons from my Pop!!!

Three more lessons from my Pop!!!

Recurrent Neural Networks (RNNs), Clearly Explained!!!

Recurrent Neural Networks (RNNs), Clearly Explained!!!

The StatQuest Illustrated Guide To Machine Learning, Theme Song!!!

The StatQuest Illustrated Guide To Machine Learning, Theme Song!!!

The StatQuest Introduction to PyTorch

The StatQuest Introduction to PyTorch

Troll 2, Clearly Explained!!!

Troll 2, Clearly Explained!!!

The Binomial Distribution in 30 Seconds!!!

The Binomial Distribution in 30 Seconds!!!

UMAP: Mathematical Details (clearly explained!!!)

UMAP: Mathematical Details (clearly explained!!!)

UMAP Dimension Reduction, Main Ideas!!!

UMAP Dimension Reduction, Main Ideas!!!

Tensors for Neural Networks, Clearly Explained!!!

Tensors for Neural Networks, Clearly Explained!!!

The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!

The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!

The Exponential Distribution

The Exponential Distribution

The mean, the median, and the mode.

The mean, the median, and the mode.

Clustering with DBSCAN, Clearly Explained!!!

Clustering with DBSCAN, Clearly Explained!!!

Frank Starmer Clearly Explained (How my pop influenced StatQuest!!!)

Frank Starmer Clearly Explained (How my pop influenced StatQuest!!!)

Entropy (for data science) Clearly Explained!!!

Entropy (for data science) Clearly Explained!!!

Bayes' Theorem, Clearly Explained!!!!

Bayes' Theorem, Clearly Explained!!!!

Conditional Probabilities, Clearly Explained!!!

Conditional Probabilities, Clearly Explained!!!

Using Bootstrapping to Calculate p-values!!!

Using Bootstrapping to Calculate p-values!!!

Bootstrapping Main Ideas!!!

Bootstrapping Main Ideas!!!

Ken Jee's #66DaysOfData Challenge Clearly Explained!!!

Ken Jee's #66DaysOfData Challenge Clearly Explained!!!

Expected Values for Continuous Variables!!!

Expected Values for Continuous Variables!!!

Three (3) things to do when starting out in Data Science

Three (3) things to do when starting out in Data Science

How to make your own StatQuest!!!

How to make your own StatQuest!!!

Decision and Classification Trees, Clearly Explained!!!

Decision and Classification Trees, Clearly Explained!!!

Silly Songs, Clearly Explained!!!

Silly Songs, Clearly Explained!!!

Expected Values, Main Ideas!!!

Expected Values, Main Ideas!!!

Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)

Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)

Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation

Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation

Neural Networks Part 6: Cross Entropy

Neural Networks Part 6: Cross Entropy

The SoftMax Derivative, Step-by-Step!!!

The SoftMax Derivative, Step-by-Step!!!

Neural Networks Part 5: ArgMax and SoftMax

Neural Networks Part 5: ArgMax and SoftMax

Neural Networks Pt. 4: Multiple Inputs and Outputs

Neural Networks Pt. 4: Multiple Inputs and Outputs

US Census Data and Contest!!!

US Census Data and Contest!!!

Neural Networks Pt. 3: ReLU In Action!!!

Neural Networks Pt. 3: ReLU In Action!!!

Backpropagation Details Pt. 2: Going bonkers with The Chain Rule

Backpropagation Details Pt. 2: Going bonkers with The Chain Rule

Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.

Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.

Neural Networks Pt. 2: Backpropagation Main Ideas

Neural Networks Pt. 2: Backpropagation Main Ideas

What is AutoML? A conversation with Gnosis Data Analysis

What is AutoML? A conversation with Gnosis Data Analysis

The Essential Main Ideas of Neural Networks

The Essential Main Ideas of Neural Networks

XGBoost in Python from Start to Finish

XGBoost in Python from Start to Finish

The Elements of StatQuest (Webinar)

The Elements of StatQuest (Webinar)

The Chain Rule

The Chain Rule

Hypothesis Testing and The Null Hypothesis, Clearly Explained!!!

Hypothesis Testing and The Null Hypothesis, Clearly Explained!!!

Alternative Hypotheses: Main Ideas!!!

Alternative Hypotheses: Main Ideas!!!

Support Vector Machines in Python from Start to Finish.

Support Vector Machines in Python from Start to Finish.

Live 2020-06-15!!! Bootstrapping, Main Ideas

Live 2020-06-15!!! Bootstrapping, Main Ideas

Classification Trees in Python from Start to Finish

Classification Trees in Python from Start to Finish

Наивный байесовский гауссов, ясно объяснил !!!

Наивный байесовский гауссов, ясно объяснил !!!

Naive Bayes, Clearly Explained!!!

Naive Bayes, Clearly Explained!!!

Live 2020-06-01!!! Hypothesis Testing

Live 2020-06-01!!! Hypothesis Testing

Ridge vs Lasso Regression, Visualized!!!

Ridge vs Lasso Regression, Visualized!!!

Live 2020-05-18!!! Bayes' Theorem

Live 2020-05-18!!! Bayes' Theorem

Live 2020-05-04!!! Conditional Probability

Live 2020-05-04!!! Conditional Probability

Statistical Power, Clearly Explained!!!

Statistical Power, Clearly Explained!!!

Power Analysis, Clearly Explained!!!

Power Analysis, Clearly Explained!!!

p-hacking: What it is and how to avoid it!

p-hacking: What it is and how to avoid it!

Live 2020-04-20!!! Expected Values

Live 2020-04-20!!! Expected Values

Live 2020-04-06!!! Naive Bayes: Gaussian

Live 2020-04-06!!! Naive Bayes: Gaussian

Бам !!! Ясно объяснил !!!

Бам !!! Ясно объяснил !!!

How to calculate p-values

How to calculate p-values

p-values: What they are and how to interpret them

p-values: What they are and how to interpret them

Live 2020-03-16!!! Naive Bayes

Live 2020-03-16!!! Naive Bayes

Live 2020-03-02!!! Virus Models and p-hacking

Live 2020-03-02!!! Virus Models and p-hacking

XGBoost Part 4 (of 4): Crazy Cool Optimizations

XGBoost Part 4 (of 4): Crazy Cool Optimizations

Live 2020-02-17!!! Imbalanced Data and Post-Hoc Tests

Live 2020-02-17!!! Imbalanced Data and Post-Hoc Tests

XGBoost Part 3 (of 4): Mathematical Details

XGBoost Part 3 (of 4): Mathematical Details

Live 2020-02-03!!! Statistical Models, Regularization, Best ML Algorithm.

Live 2020-02-03!!! Statistical Models, Regularization, Best ML Algorithm.

Live 2020-01-20!!! Favorite ML, Data Leakage, How to Learn ML

Live 2020-01-20!!! Favorite ML, Data Leakage, How to Learn ML

StatQuest: Random Forests Part 2: Missing data and clustering

StatQuest: Random Forests Part 2: Missing data and clustering

XGBoost Part 2 (of 4): Classification

XGBoost Part 2 (of 4): Classification

Live 2020-01-06!!! Sample Sizes, ML vs Statistics and a Poem

Live 2020-01-06!!! Sample Sizes, ML vs Statistics and a Poem

XGBoost Part 1 (of 4): Regression

XGBoost Part 1 (of 4): Regression

Machine Learning Fundamentals: Sensitivity and Specificity

Machine Learning Fundamentals: Sensitivity and Specificity

How to Prune Regression Trees, Clearly Explained!!!

How to Prune Regression Trees, Clearly Explained!!!

Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)

Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)

Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)

Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)

Support Vector Machines Part 1 (of 3): Main Ideas!!!

Support Vector Machines Part 1 (of 3): Main Ideas!!!

Regression Trees, Clearly Explained!!!

Regression Trees, Clearly Explained!!!

Pearson's Correlation, Clearly Explained!!!

Pearson's Correlation, Clearly Explained!!!

Covariance, Clearly Explained!!!

Covariance, Clearly Explained!!!

Calculating the Mean, Variance and Standard Deviation, Clearly Explained!!!

Calculating the Mean, Variance and Standard Deviation, Clearly Explained!!!

Why Dividing By N Underestimates the Variance

Why Dividing By N Underestimates the Variance

Доступное объяснение ROC и AUC!

Доступное объяснение ROC и AUC!

Population and Estimated Parameters, Clearly Explained!!!

Population and Estimated Parameters, Clearly Explained!!!

Stochastic Gradient Descent, Clearly Explained!!!

Stochastic Gradient Descent, Clearly Explained!!!

Gradient Boost Part 4 (of 4): Classification Details

Gradient Boost Part 4 (of 4): Classification Details

Gradient Boost Part 3 (of 4): Classification

Gradient Boost Part 3 (of 4): Classification

Saturday

Saturday

Gradient Boost Part 2 (of 4): Regression Details

Gradient Boost Part 2 (of 4): Regression Details

Gradient Boost Part 1 (of 4): Regression Main Ideas

Gradient Boost Part 1 (of 4): Regression Main Ideas

Last Night

Last Night

Gradient Descent, Step-by-Step

Gradient Descent, Step-by-Step

A Drink From The Well

A Drink From The Well

AdaBoost, Clearly Explained

AdaBoost, Clearly Explained

Design Matrices For Linear Models, Clearly Explained!!!

Design Matrices For Linear Models, Clearly Explained!!!

Wildest Dreams

Wildest Dreams

ROC and AUC in R

ROC and AUC in R

Christmas Morning

Christmas Morning

Доступное Введение в Машинное Обучение

Доступное Введение в Машинное Обучение

You Mean So Much

You Mean So Much

Machine Learning Fundamentals: The Confusion Matrix

Machine Learning Fundamentals: The Confusion Matrix

Ridge, Lasso and Elastic-Net Regression in R

Ridge, Lasso and Elastic-Net Regression in R

Regularization Part 3: Elastic Net Regression

Regularization Part 3: Elastic Net Regression

Regularization Part 2: Lasso (L1) Regression

Regularization Part 2: Lasso (L1) Regression

Little Red Fiat

Little Red Fiat

Regularization Part 1: Ridge (L2) Regression

Regularization Part 1: Ridge (L2) Regression

Machine Learning Fundamentals: Bias and Variance

Machine Learning Fundamentals: Bias and Variance

Maximum Likelihood For the Normal Distribution, step-by-step!!!

Maximum Likelihood For the Normal Distribution, step-by-step!!!

The Central Limit Theorem, Clearly Explained!!!

The Central Limit Theorem, Clearly Explained!!!

Miss Carolina

Miss Carolina

Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!

Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!

The Binomial Distribution and Test, Clearly Explained!!!

The Binomial Distribution and Test, Clearly Explained!!!

Happy Days

Happy Days

Maximum Likelihood for the Exponential Distribution, Clearly Explained!!!

Maximum Likelihood for the Exponential Distribution, Clearly Explained!!!

Logistic Regression in R, Clearly Explained!!!!

Logistic Regression in R, Clearly Explained!!!!

Deviance Residuals

Deviance Residuals

Saturated Models and Deviance

Saturated Models and Deviance

joe and sue

joe and sue

Odds Ratios and Log(Odds Ratios), Clearly Explained!!!

Odds Ratios and Log(Odds Ratios), Clearly Explained!!!

Logistic Regression Details Pt 3: R-squared and p-value

Logistic Regression Details Pt 3: R-squared and p-value

Logistic Regression Details Pt 2: Maximum Likelihood

Logistic Regression Details Pt 2: Maximum Likelihood

Logistic Regression Details Pt1: Coefficients

Logistic Regression Details Pt1: Coefficients

A War That We Can Win

A War That We Can Win

StatQuest: K-means clustering

StatQuest: K-means clustering

Odds and Log(Odds), Clearly Explained!!!

Odds and Log(Odds), Clearly Explained!!!

Hey Dom

Hey Dom

Основы машинного обучения: Кросс-валидация.

Основы машинного обучения: Кросс-валидация.

StatQuest: A gentle introduction to ChIP-Seq

StatQuest: A gentle introduction to ChIP-Seq

StatQuest: 10,000 Subscriber Milestone

StatQuest: 10,000 Subscriber Milestone

StatQuest: PCA - Practical Tips

StatQuest: PCA - Practical Tips

StatQuest: Principal Component Analysis (PCA), Step-by-Step

StatQuest: Principal Component Analysis (PCA), Step-by-Step

Darling of Mine

Darling of Mine

Probability is not Likelihood. Find out why!!!

Probability is not Likelihood. Find out why!!!

StatQuest: Logistic Regression

StatQuest: Logistic Regression

Sunday Best

Sunday Best

StatQuest: Random Forests in R

StatQuest: Random Forests in R

StatQuest: Случайные леса Часть 1 - Создание, использование и оценка

StatQuest: Случайные леса Часть 1 - Создание, использование и оценка

Love Song

Love Song

StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data

StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data

StatQuest: PCA in Python

StatQuest: PCA in Python

She Blinded Me With Science!!!

She Blinded Me With Science!!!