Главная
Study mode:
on
1
Intro
2
SV Classifier
3
Nonlinear SVMS
4
SVMS: Pros and cons
5
The machine learning framework
6
Features
7
Neural Networks
8
Fully convolutional network
9
Converting FC into conv
10
Activation Functions
11
Binary classification
12
Softmax activation
13
Multi-label
14
Loss Function
15
Train with Gradient Descent
16
Optimization
17
Network training
18
UCF Visualizing Convolution
Description:
Explore advanced classification techniques in computer vision through this comprehensive lecture from the University of Central Florida's CAP5415 Computer Vision course. Delve into support vector machines (SVMs), including nonlinear SVMs and their advantages. Examine the machine learning framework, feature extraction, and neural networks, with a focus on fully convolutional networks and the conversion of fully connected layers to convolutional layers. Learn about various activation functions, binary and multi-label classification, loss functions, and optimization techniques like gradient descent. Gain insights into network training processes and visualize convolutional operations. This in-depth lecture equips you with essential knowledge for tackling complex classification problems in computer vision applications.

Classification in Computer Vision - Part II - Lecture 19

University of Central Florida
Add to list