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Kapil Sachdeva @UCk1u5_qq_nrdz13-o5K6beQ@youtube.com

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"Work like Hell. Share all you know. Abide by your handshake


08:30
Eliminate Grid Sensitivity | Bag of Freebies (Yolov4) | Essentials of Object Detection
19:29
GIoU vs DIoU vs CIoU | Losses | Essentials of Object Detection
14:38
Feature Pyramid Network | Neck | Essentials of Object Detection
10:10
Bounding Box Prediction | Yolo | Essentials of Object Detection
09:27
Anchor Boxes | Essentials of Object Detection
05:59
Intersection Over Union (IoU) | Essentials of Object Detection
06:29
A Better Detection Head | Essentials of Object Detection
15:32
Detection Head | Essentials of Object Detection
12:09
Reshape,Permute,Squeeze,Unsqueeze made simple using einops | The Gems
11:53
Image & Bounding Box Augmentation using Albumentations | Essentials of Object Detection
07:56
Bounding Box Formats | Essentials of Object Detection
24:06
Object Detection introduction and an overview | Essentials of Object Detection
13:31
Softmax (with Temperature) | Essentials of ML
09:27
Grouped Convolution - Visually Explained + PyTorch/numpy code | Essentials of ML
11:13
Convolution, Kernels and Filters - Visually Explained + PyTorch/numpy code | Essentials of ML
09:17
Matching patterns using Cross-Correlation | Essentials of ML
09:07
Let's make the Correlation Machine | Essentials of ML
25:27
Reparameterization Trick - WHY & BUILDING BLOCKS EXPLAINED!
35:33
Variational Autoencoder - VISUALLY EXPLAINED!
30:55
Probabilistic Programming - FOUNDATIONS & COMPREHENSIVE REVIEW!
24:45
Metropolis-Hastings - VISUALLY EXPLAINED!
33:07
Markov Chains - VISUALLY EXPLAINED + History!
31:37
Monte Carlo Methods - VISUALLY EXPLAINED!
12:49
Conjugate Prior - Use & Limitations CLEARLY EXPLAINED!
15:30
How to Read & Make Graphical Models?
26:33
Posterior Predictive Distribution - Proper Bayesian Treatment!
15:13
Sum Rule, Product Rule, Joint & Marginal Probability - CLEARLY EXPLAINED with EXAMPLES!
24:01
Noise-Contrastive Estimation - CLEARLY EXPLAINED!
15:11
Bayesian Curve Fitting - Your First Baby Steps!
24:07
Maximum Likelihood Estimation - THINK PROBABILITY FIRST!
31:31
The Battle of Polynomials | Towards Bayesian Regression
30:57
Kalman Filter - VISUALLY EXPLAINED!
24:10
Importance Sampling - VISUALLY EXPLAINED with EXAMPLES!
13:29
Inverse Transform Sampling - VISUALLY EXPLAINED with EXAMPLES!
15:27
Rejection Sampling - VISUALLY EXPLAINED with EXAMPLES!
11:33
Evidence Lower Bound (ELBO) - CLEARLY EXPLAINED!
11:35
KL Divergence - CLEARLY EXPLAINED!
32:24
MADE: Masked Autoencoder for Distribution Estimation
59:24
Normalizing Flows - Motivations, The Big Idea, & Essential Foundations
14:12
FitNets: Hints for Thin Deep Nets
19:05
Distilling the Knowledge in a Neural Network