Jon Krohn - Channel

Jon Krohn

Jon Krohn

Dr. Jon Krohn is CEO of Y Carrot, author of the bestselling book Deep Learning Illustrated, and host of the SuperDataScience podcast. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and leading conferences like Web Summit and Collision. Copies of Deep Learning Illustrated are available at bit.ly/iTkrohn. Use KROHN during checkout for 35% off! Also available from Amazon at amzn.to/32TB6rB To keep up with the latest from Jon, sign up for his newsletter here at jonkrohn.com, follow him on Twitter @JonKrohnLearns, and on LinkedIn at linkedin.com/in/jonkrohn.

146 videos

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1:41

90-Second Teaser for 4-Hour "Agentic AI Engineering" Workshop

1:01

AI Meets World: AI is Finding Alpha | Presented by Dell & NVIDIA

3:34:17

Agentic AI Engineering: Complete 4-Hour Workshop feat. MCP, CrewAI and OpenAI Agents SDK

2:17:12

Generative AI with Large Language Models: Hands-On Training feat. Hugging Face and PyTorch Lightning

1:07:02

Getting Value from Artificial Intelligence — Jon Krohn at Hg Capital "Digital Forum" 2023

1:01

Four Major Weightlifting PRs (DL, OHS, PJ, FS)

3:02

#shorts Plotting a System of Linear Equations

1:26

#Shorts System of Linear Equations — Topic 1 of Machine Learning Foundations

3:19

The Staggering Pace of Technological Change in One Lifetime

0:27

Big Olympic Lift PRs: 255# Clean & 185# Snatch

0:43

380# Back Squat & 170# Snatch: New All-Time PRs

0:09

440-pound Deadlift: First Lift 2x Bodyweight

3:38

Exercises on Event Probabilities — Topic 98 of Machine Learning Foundations

7:08

Combinatorics — Topic 97 of Machine Learning Foundations

8:23

Multiple Independent Observations — Topic 96 of Machine Learning Foundations

9:25

Events and Sample Spaces — Topic 95 of Machine Learning Foundations

5:39

What Probability Theory Is — Topic 94 of Machine Learning Foundations

4:00

A Brief History of Probability Theory — Topic 93 of Machine Learning Foundations

8:00

Probability & Information Theory — Subject 5 of Machine Learning Foundations

4:36

My Favorite Calculus Resources — Topic 92 of Machine Learning Foundations

3:40

Finding the Area Under the ROC Curve — Topic 91 of Machine Learning Foundations

1:41

Definite Integral Exercise — Topic 90 of Machine Learning Foundations

4:57

Numeric Integration with Python — Topic 89 of Machine Learning Foundations

7:22

Definite Integrals — Topic 88 of Machine Learning Foundations

1:10

Indefinite Integral Exercises — Topic 87 of Machine Learning Foundations

5:42

The Integral Calculus Rules — Topic 86 of Machine Learning Foundations

6:20

What Integral Calculus Is — Topic 85 of Machine Learning Foundations

10:17

The ROC Curve (Receiver-Operating Characteristic Curve) — Topic 84 of Machine Learning Foundations

0:08

All-Time Snatch PR: 160 pounds

2:35

The Confusion Matrix — Topic 83 of Machine Learning Foundations

9:19

Binary Classification — Topic 82 of Machine Learning Foundations

2:50

Integral Calculus — The Final Segment of Calculus Videos in my ML Foundations Series

1:01

Exercise on Higher-Order Partial Derivatives — Topic 81 of Machine Learning Foundations

12:29

Higher-Order Partial Derivatives — Topic 80 of Machine Learning Foundations

0:24

More than Bodyweight Overhead for the First Time

6:06

Backpropagation — Topic 79 of Machine Learning Foundations

24:22

The Gradient of Mean Squared Error — Topic 78 of Machine Learning Foundations

12:59

Gradient Descent (Hands-on with PyTorch) — Topic 77 of Machine Learning Foundations

15:51

The Gradient of Quadratic Cost — Topic 76 of Machine Learning Foundations

0:27

All-Time Clean & Jerk PR: 205 Pounds

15:30

Linear Regression Fit Point by Point — Topic 75 of Machine Learning Foundations

1:11

Exercises on the Multivariate Chain Rule — Topic 74 of Machine Learning Foundations

9:23

The Chain Rule for Partial Derivatives — Topic 73 of Machine Learning Foundations

2:33

Partial Derivative Notation — Topic 72 of Machine Learning Foundations

1:58

Mathematical Foundations of Machine Learning (Course Intro Video)

2:37

Advanced Partial-Derivative Exercises — Topic 71 of Machine Learning Foundations

14:46

Advanced Partial Derivatives — Topic 70 of Machine Learning Foundations

5:24

Calculating Partial Derivatives with PyTorch AutoDiff — Topic 69 of Machine Learning Foundations

3:07

Partial Derivative Exercises — Topic 68 of Machine Learning Foundations

29:57

What Partial Derivatives Are (Hands-on Introduction) — Topic 67 of Machine Learning Foundations

0:19

First Greater-than-Bodyweight Clean (225 pounds)

0:14

First 300-pound Front Squat

22:44

Calculus II: Partial Derivatives & Integrals — Subject 4 of Machine Learning Foundations

40:46

Machine Learning from First Principles, with PyTorch AutoDiff — Topic 66 of ML Foundations

20:16

The Line Equation as a Tensor Graph — Topic 65 of Machine Learning Foundations

3:58

Automatic Differentiation with TensorFlow — Topic 64 of Machine Learning Foundations

1:44

Machine Learning, Math, and Data Science — The Official Guide to my Channel

6:23

Automatic Differentiation with PyTorch — Topic 63 of Machine Learning Foundations

3:03

We're on Pause for Now! – A4N Ep 5

4:53

What Automatic Differentiation Is — Topic 62 of Machine Learning Foundations

0:18

360-pound Back Squat: All-Time PR

1:55

Automatic Differentiation – Segment 3 of Subject 3, "Limits & Derivatives" – ML Foundations

5:07

The Power Rule on a Function Chain — Topic 61 of Machine Learning Foundations

2:03

Advanced Exercises on Derivative Rules — Topic 60 of Machine Learning Foundations

6:46

The Chain Rule for Derivatives — Topic 59 of Machine Learning Foundations

4:05

The Quotient Rule for Derivatives — Topic 58 of Machine Learning Foundations

3:51

The Product Rule for Derivatives — Topic 57 of Machine Learning Foundations

3:10

Exercises on Derivative Rules — Topic 56 of Machine Learning Foundations

2:27

The Sum Rule for Derivatives — Topic 55 of Machine Learning Foundations

3:11

The Constant Multiple Rule for Derivatives — Topic 54 of Machine Learning Foundations

1:17

The Power Rule for Derivatives – Topic 53 of Machine Learning Foundations

1:30

The Derivative of a Constant –Topic 52 of Machine Learning Foundations

4:20

Derivative Notation — Topic 51 of Machine Learning Foundations

14:23

How Derivatives Arise from Limits – Topic 50 of Machine Learning Foundations

15:47

The Delta Method – Topic 49 of Machine Learning Foundations

1:17

Derivatives and Differentiation — Segment 2 of Subject 3, "Limits & Derivatives" – ML Foundations

1:54

Exercises on Limits – Topic 48 of Machine Learning Foundations

18:19

Calculating Limits – Topic 47 of Machine Learning Foundations

0:29

First 400-pound Deadlift

8:36

Calculus Applications – Topic 46 of Machine Learning Foundations

10:04

Calculus of the Infinitesimals – Topic 45 of Machine Learning Foundations

6:46

The Method of Exhaustion – Topic 44 of Machine Learning Foundations

2:25

Intro to Integral Calculus –Topic 43 of Machine Learning Foundations

0:23

New Video Coming Soon!

13:26

Intro to Differential Calculus —Topic 42 of Machine Learning Foundations

3:40

Calculus I: Limits & Derivatives — Subject 3 of Machine Learning Foundations

10:50

Singular Value Decomposition — Topic 35 of Machine Learning Foundations

6:11

Linear Algebra Resources — Topic 41 of Machine Learning Foundations

8:27

Principal Component Analysis (PCA) — Topic 40 of Machine Learning Foundations

4:37

The Trace Operator — Topic 39 of Machine Learning Foundations

18:57

Regression with the Pseudoinverse — Topic 38 of Machine Learning Foundations

12:23

The Moore-Penrose Pseudoinverse — Topic 37 of Machine Learning Foundations

11:33

Data Compression with SVD — Topic 36 of Machine Learning Foundations

3:22

Matrix Operations for Machine Learning — Final Segment of Subject 2, "Linear Algebra II"

1:09

Please Nominate Me for Data Community Content Creator Awards :)

13:02

Eigenvector and Eigenvalue Applications — Topic 34 of Machine Learning Foundations

12:49

Eigendecomposition — Topic 33 of Machine Learning Foundations

16:16

Determinants and Eigenvalues — Topic 32 of Machine Learning Foundations

1:28

Determinant Exercises — Topic 31 of Machine Learning Foundations

8:42

Determinants of Larger Matrices — Topic 30 of Machine Learning Foundations