10:44

Lecture 14.4 - Image Segmentation [Otsu Example]

08:25

Lecture 14.9 - Image Segmentation [K-means clustering algorithm]

06:10

Lecture 14.8 - Image Segmentation [Natural Grouping and Distance Metrics]

18:24

Lecture 13.12 - Object Detection [CNN based Approach RCNN]

04:11

Lecture 14.7 - Image Segmentation [Introduction to Clustering]

05:53

Lecture 14.6 - Image Segmentation [Region Splitting and Merging 1]

06:56

Lecture 14.3 - Image Segmentation [Thresholding Examples]

06:03

Lecture 14.2 - Image Segmentation [Image Binarization]

13:01

Lecture 13.11 - Object Detection [CNN Based Approach - A Simple Solution]

12:41

Lecture 13.13 - Object Detection [CNN based Approach Fast R-CNN]

02:20

Lecture 13.14 - Object Detection [Per class or class agnostic regression]

34:21

Lecture 16 - Instance Segmentation

07:06

Lecture 13.10 - Object Detection [Mean Average Precision]

03:58

Lecture 13.9 - Object Detection [Precision and Recall]

05:13

Lecture 13.8 - Object Detection [Evaluation using IOU]

07:00

Lecture 13.7 - Object Detection [Non maximum suppression]

04:42

Lecture 13.6 - Object Detection [Extract Features, Classify Features, Post Processing]

03:11

Lecture 13.5 - Object Detection [Gaussian Pyramid Construction and Challenges]

03:17

Lecture 13.4 - Object Detection [Gaussian pre-filtering]

04:31

Lecture 13.3 - Object Detection [Example of Sliding Window Approach]

03:42

Lecture 13.2 - Object Detection [Introduction to Sliding Window, Template Matching]

08:41

Lecture 13.1 - Object Detection {Introduction to Object Detection]

02:22

Lecture 12.7 - Classification II [Loss Function, Visualizing Convolution]

07:24

Lecture 12.6 - Classification II [Softmax Classification]

03:24

Lecture 12.5 - Classification II [Activation Functions]

01:28

Lecture 12.4 - Classification II [Introducing Non linearities]

05:15

Lecture 12.3 - Classification II [Converting Fully Convolution to Convolution]

06:21

Lecture 12.2 - Classification II [Fully Convolutional Network]

07:09

Lecture 12.1 - Classification II [Classification I Recap]

01:42

Lecture 11.15 - Classification I [Multi class SVM]

05:42

Lecture 11.14 - Classification I [Non linear SVM]

02:56

Lecture 11.13 - Classification I [Advantages of non linear boundary]

01:44

Lecture 11.12 - Classification I [Disadvantages of linear boundary]

05:20

Lecture 11.11 - Classification I [Support Vector Classifier Example]

03:21

Lecture 11.10 - Classification I [Support Vector Classifier - Soft Margin]

03:50

Lecture 11.9 - Classification I [Support Vector Classifier - Separable case]

02:34

Lecture 11.8 - Classification I [Understanding Non separable case]

16:02

Lecture 11.7 - Classification I [Maximum Margin Classifier - SVM]

02:32

Lecture 11.6 - Classification I [K-Nearest Neighbor]

07:04

Lecture 11.5 - Classification I [Nearest Neighbor Classifier]

03:55

Lecture 11.4 - Classification I [General Framework, Decision Boundaries for classification]

03:50

Lecture 11.3 - Classification I [Understanding how to Perform classification for the Given Data]

02:23

Lecture 11.2 - Classification I [Types of classification]

04:48

Lecture 11.1 Classification I [Introduction to classification]

05:21

Lecture 8.9 - PyTorch Tutorial [Defining a Network class part 2]

03:48

Lecture 8.15 - PyTorch Tutorial [Update Parameters]

03:00

Lecture 8.13 - PyTorch Tutorial [Loss Function]

00:47

Lecture 8.14 - PyTorch Tutorial [Gradient Computation]

01:09

Lecture 8.12 - PyTorch Tutorial [Process input through the Network​]

12:26

Lecture 8.11 - PyTorch Tutorial [Iterate over a Dataset of Inputs]

17:40

Lecture 8.10 - PyTorch Tutorial [Defining a CNN Network - Example]

02:50

Lecture 8.8 - PyTorch Tutorial [Defining a Network class]

02:46

Lecture 8.7 - PyTorch Tutorial [Building Neural Network]

03:23

Lecture 8.6 - PyTorch Tutorial [Training Procedure]

05:18

Lecture 8.5 - PyTorch Tutorial [Computational Graphs and Automatic Gradient Computation]

03:36

Lecture 8.4 - PyTorch Tutorial [Matrix Multiplication in PyTorch]

02:16

Lecture 8.3 - PyTorch Tutorial [Torch Tensor vs Numpy Array]

04:00

Lecture 8.2 - PyTorch Tutorial [PyTorch Operations]

05:24

Lecture 8.1 - PyTorch Tutorial [PyTorch Tensor]

06:22

Lecture 10.5 - Autoencoders [Applications, and Properties of Autoencoder]