09:26

Depth Anything 3:Recovering the Visual Space from Any Views - Paper Walkthrough

04:11

Triangular Matrices and LU Decomposition - Explained

04:13

Nested Learning: The Illusion of Deep Learning Architectures - Paper Walkthrough

04:04

Cracking ML Interviews: Covariance vs Correlation (Question 15)

03:03

The Identity Matrix - Explained

03:03

Cracking ML Interviews: Self-Attention Mechanism (Question 14)

04:28

Rotation and Reflection Matrices - Explained

04:40

Cracking ML Interviews: Precision, Recall and F1-Score (Question 13)

08:08

Orthogonal Matrices - Explained

06:13

Cracking ML Interviews: ROC and AUC (Question 12)

06:22

Symmetric Matrices and the Positive Definiteness

03:12

Cracking ML Interviews: K-Fold Cross-Validation (Question 11)

01:09

Categorical Distribution - ML Snippets

06:44

The Hessian Matrix - Explained

05:30

Cracking ML Interviews: Batch Normalization (Question 10)

02:44

Cracking ML Interviews: L1/L2 Regularization (Question 9)

05:06

Cracking ML Interviews: Skip Connection Layer (Question 8)

04:45

The Jacobian Matrix - Explained

02:36

Cracking ML Interviews: Activation Functions in Neural Nets (Question 7)

01:25

Vector Databases & Vector Search - ML Snippets

05:07

Cracking ML Interviews: Convolutional Neural Networks (Question 6)

08:04

Cracking ML Interviews: Stochastic Gradient Descent (Question 5)

04:03

Cracking ML Interviews: Solving the Linear Regression Equation (Question 4)

03:35

Statistical Moments: Mean, Variation, Skewness, Kurtosis

04:14

Cracking ML Interviews: Underfitting vs Overfitting (Question 3)

01:42

Binomial Distribution - ML Snippets

05:39

Cracking ML Interviews: Maximum Likelihood Estimation (Question 2)

05:44

Cracking ML Interviews: Bayes Theorem Exercise (Question 1)

04:31

Simpson's Paradox - Explained

08:15

Frequentist vs Bayesian Thinking

02:03

L1 Regularization - ML Snippets

08:11

Kernel Density Estimation - Explained

01:09

t-SNE Intuition - ML Snippets

05:10

Accept-Reject Sampling - Explained

08:41

Dirichlet Distribution - Explained

01:47

Kernel Trick - ML Snippets

01:42

Cross-Entropy - ML Snippets

09:17

Markov Chain Monte Carlo (MCMC) - Explained

01:09

Bernoulli Distribution - ML Snippets

02:37

Gaussian Distribution - ML Snippets

05:27

Bayes' Theorem - Explained

01:15

Markov Chains - ML Snippets

04:49

Taylor Series - Explained

03:48

Linear Regression (Least Squared Errors) - Explained

04:13

Monte Carlo Simulation - Explained

04:42

Lagrange Multipliers - Explained

04:03

Variational Autoencoder - Explained

04:41

Central Limit Theorem - Explained

05:27

Degrees of Freedom - Explained

05:57

Qwen3 - Paper Walkthrough

05:31

Gamma Function - Explained

05:35

Variational Inference - Explained

08:01

Forward-Backward Algorithm | Hidden Markov Models Part 3

09:01

Magistral - Paper Walkthrough

08:21

Student's t-Distribution - Explained

03:57

Why Larger Language Models Do In-context Learning Differently? - Paper Walkthrough

08:36

DeepSeek-R1 - Paper Walkthrough

08:02

t-SNE - Explained

08:04

The Illusion of Thinking - Paper Walkthrough

05:18

Perception Encoder - Paper Walkthrough