1
The GELU, SiLU and SwiGLU activation functions, clearly explained!!!
24:18
2026-09-08
2
Another three (3) more lessons from my Pop!
3:57
2026-09-04
3
TRIPLE BAM!!! With Josh, Luis and special guest Brandon Rohrer
1:01:44
2026-08-21
4
The Simplex Algorithm, Mathematical Details!!!
26:38
2026-07-13
5
Optimization with Linear Programming (and the Simplex Algorithm), Main Ideas!!!
24:02
2026-07-06
6
StatQuest: Random Forests Part 2: Missing data and clustering
10:48
2026-06-08
7
False Discovery Rates, FDR, clearly explained
18:27
2026-06-08
8
The Essence of Linear Regression!!!
32:01
2026-05-18
9
How AI works in Super Simple Terms!!!
22:51
2026-01-12
10
StatQuest: Career Advice from Tech Industry Leaders
5:59
2025-09-04
11
Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!
18:02
2025-05-05
12
Reinforcement Learning with Neural Networks: Mathematical Details
25:01
2025-04-14
13
Reinforcement Learning with Neural Networks: Essential Concepts
24:00
2025-04-07
14
Reinforcement Learning: Essential Concepts
18:13
2025-03-31
15
StatQuest on DeepLearning.AI!!! Check out my short course on attention!
1:00
2025-02-12
16
StatQuest with Josh Starmer is live!
59:55
2025-02-06
17
Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!
18:52
2024-11-18
18
Luis Serrano + Josh Starmer Q&A Livestream!!!
54:35
2024-10-10
19
Human Stories in AI: Nana Janashia@TechWorld With Nana
24:08
2024-09-09
20
A few more lessons from my Pop!
4:40
2024-09-04
21
Human Stories in AI: Abbas Merchant@Matics Analytics
54:49
2024-07-29
22
Luis Serrano + Jay Alammar + Josh Starmer Q&A Livestream!!!
59:56
2024-07-13
23
Coding a ChatGPT Like Transformer From Scratch in PyTorch
31:11
2024-07-01
24
Human Stories in AI: Amy Finnegan
28:25
2024-06-17
25
Human Stories in AI: Xavier Moyá
32:33
2024-06-03
26
Human Stories in AI: Tommy Tang
36:55
2024-05-20
27
Luis Serrano + Josh Starmer Q&A Livestream!!!
57:30
2024-05-11
28
Human Stories in AI: Simon Stochholm
37:27
2024-04-29
29
Log_e Song - Official Lyric Video
3:21
2024-04-26
30
Human Stories in AI: Brian Risk@devra.ai
35:49
2024-04-15
31
The matrix math behind transformer neural networks, one step at a time!!!
23:43
2024-04-08
32
Human Stories in AI: Fabio Urbina
35:31
2024-04-01
33
Human Stories in AI: Khushi Jain
27:13
2024-03-18
34
Human Stories in AI: Achal Dixit
33:12
2024-03-04
35
Human Stories in AI: Rick Marks
31:13
2024-02-19
36
Essential Matrix Algebra for Neural Networks, Clearly Explained!!!
30:01
2023-12-11
37
Word Embedding in PyTorch + Lightning
32:02
2023-11-06
38
The Golden Play Button, Clearly Explained!!!’
2:30
2023-10-07
39
Another 3 lessons from my Pop!!!
6:46
2023-09-04
40
Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!
36:45
2023-08-28
41
Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!
36:15
2023-07-24
42
What is a Logit?
0:29
2023-06-19
43
Logistic vs Logit Functions
0:30
2023-06-12
44
Attention for Neural Networks, Clearly Explained!!!
15:51
2023-06-05
45
Likelihood vs Probability
0:30
2023-05-15
46
p-values
0:23
2023-05-15
47
Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!
16:50
2023-05-08
48
Matrix Multiplication
0:20
2023-04-16
49
Matrix Notation
0:20
2023-04-16
50
The Ukulele: Clearly Explained!!!
3:27
2023-04-01
51
PCA Eigenvalues
0:32
2023-03-21
52
PCA Eigenvectors
0:32
2023-03-20
53
Normalized Data
0:34
2023-03-20
54
Standardized Data
0:29
2023-03-20
55
Type 2 Errors
0:28
2023-03-20
56
Type 1 Errors
0:42
2023-03-20
57
Word Embedding and Word2Vec, Clearly Explained!!!
16:12
2023-03-13
58
The AI Buzz, Episode #5: A new wave of AI-based products and the resurgence of personal applications
35:57
2023-03-07
59
CatBoost Part 2: Building and Using Trees
16:16
2023-03-06
60
CatBoost Part 1: Ordered Target Encoding
8:32
2023-02-27
61
The AI Buzz, Episode #4: ChatGPT + Bing and How to start an AI company in 3 easy steps.
35:26
2023-02-21
62
One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!
15:23
2023-02-13
63
The AI Buzz, Episode #3: Constitutional AI, Emergent Abilities and Foundation Models
33:43
2023-02-07
64
Mutual Information, Clearly Explained!!!
16:14
2023-02-06
65
Cosine Similarity, Clearly Explained!!!
10:14
2023-01-30
66
Long Short-Term Memory with PyTorch + Lightning
33:24
2023-01-24
67
The AI Buzz, Episode #2: Big data, Reinforcement Learning and Aligning Models
36:52
2023-01-24
68
The AI Buzz, Episode #1: ChatGPT, Transformers and Attention
38:21
2023-01-11
69
Design Matrix Examples in R, Clearly Explained!!!
8:20
2022-11-18
70
Design Matrices For Linear Models, Clearly Explained!!!
14:40
2022-11-18
71
Using Linear Models for t tests and ANOVA, Clearly Explained!!!
11:38
2022-11-18
72
Multiple Regression in R, Step by Step!!!
7:43
2022-11-18
73
Multiple Regression, Clearly Explained!!!
5:25
2022-11-18
74
Linear Regression in R, Step by Step
5:01
2022-11-18
75
Linear Regression, Clearly Explained!!!
27:27
2022-11-18
76
R-squared, Clearly Explained!!!
11:01
2022-11-18
77
Long Short-Term Memory (LSTM), Clearly Explained
20:45
2022-11-07
78
The Cosine Similarity for NLP and CatBoost
43:40
2022-11-01
79
Happy Halloween (Neural Networks Are Not Scary)
0:59
2022-10-31
80
Handmade Pasta, Clearly Explained!!!
6:59
2022-10-25
81
Live Stream - More details about Target Encoding/AMA/Silly Songs
51:18
2022-10-18
82
Live Stream - Target Encoding/AMA/Silly Songs!!!
57:25
2022-10-05
83
Introduction to Coding Neural Networks with PyTorch and Lightning
20:43
2022-09-19
84
Three more lessons from my Pop!!!
5:28
2022-09-04
85
Recurrent Neural Networks (RNNs), Clearly Explained!!!
16:37
2022-07-11
86
The StatQuest Illustrated Guide To Machine Learning, Theme Song!!!
0:15
2022-05-09
87
The StatQuest Introduction to PyTorch
23:22
2022-04-25
88
Troll 2, Clearly Explained!!!
5:06
2022-04-01
89
The Binomial Distribution in 30 Seconds!!!
0:30
2022-03-22
90
UMAP: Mathematical Details (clearly explained!!!)
16:02
2022-03-14
91
UMAP Dimension Reduction, Main Ideas!!!
18:52
2022-03-07
92
Tensors for Neural Networks, Clearly Explained!!!
9:40
2022-02-28
93
The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!
0:42
2022-02-10
94
The Exponential Distribution
0:16
2022-01-14
95
The mean, the median, and the mode.
0:13
2022-01-14
96
Clustering with DBSCAN, Clearly Explained!!!
9:30
2022-01-10
97
Frank Starmer Clearly Explained (How my pop influenced StatQuest!!!)
6:28
2021-09-04
98
Entropy (for data science) Clearly Explained!!!
16:35
2021-08-25
99
Bayes' Theorem, Clearly Explained!!!!
14:00
2021-08-16
100
Conditional Probabilities, Clearly Explained!!!
10:56
2021-07-21
101
Using Bootstrapping to Calculate p-values!!!
8:08
2021-07-13
102
Bootstrapping Main Ideas!!!
9:27
2021-07-06
103
Ken Jee's #66DaysOfData Challenge Clearly Explained!!!
6:22
2021-06-23
104
Expected Values for Continuous Variables!!!
19:44
2021-06-22
105
Three (3) things to do when starting out in Data Science
11:10
2021-05-27
106
How to make your own StatQuest!!!
5:00
2021-05-03
107
Decision and Classification Trees, Clearly Explained!!!
18:08
2021-04-26
108
Silly Songs, Clearly Explained!!!
3:53
2021-04-01
109
Expected Values, Main Ideas!!!
13:39
2021-03-29
110
Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)
15:24
2021-03-08
111
Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation
22:08
2021-03-01
112
Neural Networks Part 6: Cross Entropy
9:31
2021-03-01
113
The SoftMax Derivative, Step-by-Step!!!
7:13
2021-02-08
114
Neural Networks Part 5: ArgMax and SoftMax
14:03
2021-02-08
115
Neural Networks Pt. 4: Multiple Inputs and Outputs
13:50
2021-02-01
116
US Census Data and Contest!!!
8:36
2020-12-08
117
Neural Networks Pt. 3: ReLU In Action!!!
8:58
2020-11-23
118
Backpropagation Details Pt. 2: Going bonkers with The Chain Rule
13:09
2020-11-02
119
Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.
18:32
2020-11-02
120
Neural Networks Pt. 2: Backpropagation Main Ideas
17:34
2020-10-19
121
What is AutoML? A conversation with Gnosis Data Analysis
15:21
2020-09-07
122
The Essential Main Ideas of Neural Networks
18:54
2020-08-31
123
XGBoost in Python from Start to Finish
56:43
2020-08-01
124
The Elements of StatQuest (Webinar)
1:13:39
2020-07-20
125
The Chain Rule, Clearly Explained!!!
18:24
2020-07-13
126
Hypothesis Testing and The Null Hypothesis, Clearly Explained!!!
14:41
2020-07-06
127
Alternative Hypotheses: Main Ideas!!!
9:50
2020-07-06
128
Support Vector Machines in Python from Start to Finish.
44:49
2020-06-30
129
Live 2020-06-15!!! Bootstrapping, Main Ideas
32:05
2020-06-15
130
Classification Trees in Python from Start to Finish
1:06:24
2020-06-07
131
Gaussian Naive Bayes, Clearly Explained!!!
9:26
2020-06-03
132
Naive Bayes, Clearly Explained!!!
15:12
2020-06-03
133
Live 2020-06-01!!! Hypothesis Testing
31:20
2020-06-01
134
Ridge vs Lasso Regression, Visualized!!!
9:06
2020-05-19
135
Live 2020-05-18!!! Bayes' Theorem
30:17
2020-05-18
136
Live 2020-05-04!!! Conditional Probability
24:32
2020-05-04
137
Statistical Power, Clearly Explained!!!
8:19
2020-05-04
138
Power Analysis, Clearly Explained!!!
16:45
2020-05-04
139
p-hacking: What it is and how to avoid it!
13:45
2020-05-04
140
Live 2020-04-20!!! Expected Values
33:00
2020-04-20
141
Live 2020-04-06!!! Naive Bayes: Gaussian
36:41
2020-04-06
142
Bam!!! Clearly Explained!!!
2:49
2020-04-01
143
How to calculate p-values
25:15
2020-03-23
144
p-values: What they are and how to interpret them
11:21
2020-03-23
145
Live 2020-03-16!!! Naive Bayes
33:51
2020-03-16
146
Live 2020-03-02!!! Virus Models and p-hacking
30:40
2020-03-03
147
XGBoost Part 4 (of 4): Crazy Cool Optimizations
24:27
2020-03-02
148
Live 2020-02-17!!! Imbalanced Data and Post-Hoc Tests
30:54
2020-02-17
149
XGBoost Part 3 (of 4): Mathematical Details
27:24
2020-02-10
150
Live 2020-02-03!!! Statistical Models, Regularization, Best ML Algorithm.
30:35
2020-02-04
151
Live 2020-01-20!!! Favorite ML, Data Leakage, How to Learn ML
25:42
2020-01-20
152
XGBoost Part 2 (of 4): Classification
25:18
2020-01-13
153
Live 2020-01-06!!! Sample Sizes, ML vs Statistics and a Poem
20:03
2020-01-07
154
XGBoost Part 1 (of 4): Regression
25:46
2019-12-16
155
Machine Learning Fundamentals: Sensitivity and Specificity
11:47
2019-12-02
156
How to Prune Regression Trees, Clearly Explained!!!
16:15
2019-11-25
157
Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)
15:52
2019-11-04
158
Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)
7:15
2019-11-04
159
Support Vector Machines Part 1 (of 3): Main Ideas!!!
20:32
2019-09-30
160
Regression Trees, Clearly Explained!!!
22:33
2019-08-20
161
Pearson's Correlation, Clearly Explained!!!
19:13
2019-08-05
162
Covariance, Clearly Explained!!!
22:23
2019-07-29
163
Calculating the Mean, Variance and Standard Deviation, Clearly Explained!!!
14:22
2019-07-15
164
Why Dividing By N Underestimates the Variance
17:15
2019-07-15
165
ROC and AUC, Clearly Explained!
16:17
2019-07-11
166
Population and Estimated Parameters, Clearly Explained!!!
14:31
2019-07-01
167
Stochastic Gradient Descent, Clearly Explained!!!
10:53
2019-05-13
168
Gradient Boost Part 4 (of 4): Classification Details
37:00
2019-04-22
169
Gradient Boost Part 3 (of 4): Classification
17:03
2019-04-08
170
Saturday
3:10
2019-04-06
171
Gradient Boost Part 2 (of 4): Regression Details
26:46
2019-04-01
172
Gradient Boost Part 1 (of 4): Regression Main Ideas
15:52
2019-03-25
173
Last Night
3:32
2019-03-02
174
Gradient Descent, Step-by-Step
23:54
2019-02-05
175
A Drink From The Well
3:02
2019-02-01
176
AdaBoost, Clearly Explained
20:54
2019-01-14
177
Design Matrices For Linear Models, Clearly Explained!!!
14:40
2019-01-08
178
Wildest Dreams
4:03
2018-12-31
179
ROC and AUC in R
15:13
2018-12-18
180
Christmas Morning
3:46
2018-12-01
181
A Gentle Introduction to Machine Learning
12:45
2018-11-26
182
You Mean So Much
2:50
2018-10-31
183
Machine Learning Fundamentals: The Confusion Matrix
7:13
2018-10-29
184
Ridge, Lasso and Elastic-Net Regression in R
17:51
2018-10-23
185
Regularization Part 3: Elastic Net Regression
5:19
2018-10-08
186
Regularization Part 2: Lasso (L1) Regression
8:19
2018-10-01
187
Little Red Fiat
3:35
2018-10-01
188
Regularization Part 1: Ridge (L2) Regression
20:27
2018-09-24
189
Machine Learning Fundamentals: Bias and Variance
6:36
2018-09-17
190
Maximum Likelihood For the Normal Distribution, step-by-step!!!
19:50
2018-09-10
191
The Central Limit Theorem, Clearly Explained!!!
7:35
2018-09-03
192
Miss Carolina
2:42
2018-09-01
193
Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!
11:24
2018-08-13
194
The Binomial Distribution and Test, Clearly Explained!!!
15:47
2018-08-06
195
Happy Days
2:55
2018-08-02
196
Maximum Likelihood for the Exponential Distribution, Clearly Explained!!!
9:39
2018-07-30
197
Logistic Regression in R, Clearly Explained!!!!
17:15
2018-07-26
198
Deviance Residuals
6:18
2018-07-16
199
Saturated Models and Deviance
18:40
2018-07-09
200
joe and sue
3:39
2018-07-01
201
Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
16:20
2018-06-21
202
Logistic Regression Details Pt 3: R-squared and p-value
15:25
2018-06-18
203
Logistic Regression Details Pt 2: Maximum Likelihood
10:23
2018-06-11
204
Logistic Regression Details Pt1: Coefficients
19:02
2018-06-04
205
A War That We Can Win
3:56
2018-06-02
206
StatQuest: K-means clustering
8:30
2018-05-23
207
Odds and Log(Odds), Clearly Explained!!!
11:31
2018-05-07
208
Hey Dom
2:18
2018-04-28
209
Machine Learning Fundamentals: Cross Validation
6:05
2018-04-24
210
StatQuest: A gentle introduction to ChIP-Seq
8:30
2018-04-16
211
StatQuest: 10,000 Subscriber Milestone
1:13
2018-04-11
212
StatQuest: PCA - Practical Tips
8:20
2018-04-09
213
StatQuest: Principal Component Analysis (PCA), Step-by-Step
21:58
2018-04-02
214
Darling of Mine
3:18
2018-04-01
215
In Statistics, Probability is not Likelihood.
5:01
2018-03-09
216
StatQuest: Logistic Regression
8:48
2018-03-05
217
Sunday Best
3:53
2018-03-01
218
StatQuest: Random Forests in R
15:10
2018-02-26
219
StatQuest: Random Forests Part 1 - Building, Using and Evaluating
9:54
2018-02-05
220
Love Song
3:14
2018-01-29
221
StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
5:16
2018-01-29
222
StatQuest: PCA in Python
11:37
2018-01-08
223
She Blinded Me With Science!!!
3:42
2018-01-01
224
StatQuest: MDS and PCoA in R
7:45
2017-12-18
225
StatQuest: MDS and PCoA
8:18
2017-12-11
226
StatQuest: PCA main ideas in only 5 minutes!!!
6:05
2017-12-04
227
Snow
2:50
2017-11-29
228
StatQuest: PCA in R
8:57
2017-11-27
229
Quantile Normalization, Clearly Explained!!!
4:52
2017-11-20
230
Quantile-Quantile Plots (QQ plots), Clearly Explained!!!
6:56
2017-11-13
231
Quantiles and Percentiles, Clearly Explained!!!
6:30
2017-11-06
232
A Song For Only You
3:26
2017-10-31
233
Multiple Regression in R, Step-by-Step!!!
7:43
2017-10-30
234
Multiple Regression, Clearly Explained!!!
5:25
2017-10-30
235
Sample Size and Effective Sample Size, Clearly Explained!!!
6:33
2017-10-23
236
The Difference Between Technical and Biological Replicates
5:27
2017-10-09
237
The Normal Distribution, Clearly Explained!!!
5:13
2017-10-09
238
Design Matrix Examples in R, Clearly Explained!!!
8:20
2017-10-03
239
Employee Of The Week
3:17
2017-10-01
240
StatQuest: Histograms, Clearly Explained
3:42
2017-09-25
241
StatQuest: t-SNE, Clearly Explained
11:48
2017-09-18
242
I'm Alive
3:26
2017-09-02
243
StatQuest: A gentle introduction to RNA-seq
18:26
2017-08-31
244
StatQuest: How to make a Mean Pizza Crust!!!
8:12
2017-08-31
245
Using Linear Models for t-tests and ANOVA, Clearly Explained!!!
11:38
2017-08-07
246
Brothers
3:26
2017-07-31
247
Maximum Likelihood, clearly explained!!!
6:12
2017-07-31
248
Linear Regression in R, Step-by-Step
5:01
2017-07-25
249
Linear Regression, Clearly Explained!!!
27:27
2017-07-24
250
What is a (mathematical) model?
3:45
2017-07-17
251
Boxplots are Awesome!!!
2:33
2017-07-11
252
Your Dark Side
3:39
2017-07-01
253
StatQuest: K-nearest neighbors, Clearly Explained
5:30
2017-06-26
254
StatQuest: Hierarchical Clustering
11:19
2017-06-20
255
Lowess and Loess, Clearly Explained!!!
10:10
2017-06-05
256
The Sum of Regrets
3:57
2017-05-31
257
The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)
9:22
2017-05-22
258
StatQuest: edgeR and DESeq2, part 2 - Independent Filtering
21:24
2017-05-16
259
Sampling from a Distribution, Clearly Explained!!!
3:49
2017-05-08
260
Evil Genius
2:57
2017-04-30
261
StatQuest: One or Two Tailed P-Values
7:06
2017-04-24
262
The Main Ideas behind Probability Distributions
5:15
2017-04-17
263
StatQuest: edgeR, part 1, Library Normalization
14:17
2017-04-03
264
The Rainbow
3:33
2017-03-31
265
StatQuest: DESeq2, part 1, Library Normalization
12:42
2017-03-27
266
Standard Deviation vs Standard Error, Clearly Explained!!!
2:52
2017-03-20
267
Fisher's Exact Test and the Hypergeometric Distribution
5:15
2017-03-13
268
StatQuickie: Which t test to use
5:10
2017-03-06
269
Mr Hattie
3:05
2017-02-27
270
Bar Charts Are Better than Pie Charts
1:45
2017-02-27
271
Logs (logarithms), Clearly Explained!!!
15:37
2017-02-23
272
StatQuickie: Thresholds for Significance
6:40
2017-02-22
273
A New Song
3:32
2017-01-31
274
False Discovery Rates, FDR, clearly explained
18:27
2017-01-10
275
Psycho Killer
4:35
2017-01-02
276
The Coldest Day of the Year
3:56
2016-11-29
277
I Love You
3:04
2016-10-31
278
p-hacking and power calculations
19:12
2016-10-11
279
Roses
3:29
2016-10-01
280
Nasty Weather
3:33
2016-08-31
281
Maybe It'll Go Away
3:50
2016-07-26
282
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
15:12
2016-07-10
283
Another Day
3:35
2016-07-01
284
Say Your Goodbyes
4:09
2016-05-30
285
Deal With It
2:16
2016-05-01
286
Rachel's Song (the ballad of Hazel Motes)
3:54
2016-04-01
287
Drawing and Interpreting Heatmaps
16:49
2016-01-06
288
Christmas In Rio! (now on iTunes!)
2:20
2015-12-05
289
That's Alright
2:27
2015-09-30
290
StatQuest: RNA-seq - the problem with technical replicates
12:55
2015-08-27
291
Principal Component Analysis (PCA) clearly explained (2015)
20:16
2015-08-13
292
RPKM, FPKM and TPM, Clearly Explained!!!
10:15
2015-07-09
293
Confidence Intervals, Clearly Explained!!!
6:42
2015-07-09
294
How to puree garlic
0:47
2015-07-01
295
That Dude (in the movies)
3:18
2015-06-21
296
The standard error, Clearly Explained!!!
11:44
2015-05-12
297
Wrapping up dumplings for pot stickers.
0:23
2015-04-19
298
R-squared, Clearly Explained!!!
11:01
2015-02-03
299
onion-dice
0:36
2014-09-28
300
Cutting Butter
0:38
2014-08-24
