Admissions Open for JANUARY Batch
Explore image processing math – convolution, pooling, and feature extraction.
Days : Tue & Thu
Duration : 3 Hours
Timings: 8 - 10 PM IST
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3 Hours
9 - 10 PM IST
Tue & Thu
Maths for Computer Vision
Explore image processing math – convolution, pooling, and feature extraction.
Online Live Instructor-Led Learning
3 Hours
8 - 10 PM IST
Tue & Thu
By end of this course
Get stronger in
Convolution and feature map calculation
Pooling and dimensionality reduction
Get familier with
Edge detection filters
Image classification math concepts
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is for learners interested in understanding the mathematical foundations of computer vision, including convolution operations, pooling, and feature extraction ideal for those pursuing expertise in image processing and vision AI
Prerequisites
Knowledge of matrix operations and CNN basics.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Knowledge of matrix operations and CNN basics.
Who Should Enroll?
This course is for learners interested in understanding the mathematical foundations of computer vision, including convolution operations, pooling, and feature extraction ideal for those pursuing expertise in image processing and vision AI
By end of this course
Get Stronger in
- Convolution and feature map calculation
- Pooling and dimensionality reduction
Get Familiar in
- Edge detection filters
- Image classification math concepts
Course Contents
Topics
- Convolution as linear operator
- Kernel math and feature extraction
- Padding, stride, dilation effects
Key Outcomes
Derive and interpret convolution layers
Topics
- Mathematical definition of feature maps
- Receptive field calculation
- Hierarchical feature extraction math
Key Outcomes
Understand CNN spatial transformations
Topics
- Pooling as down sampling operator
- Max vs average pooling derivation
- Feature pyramid network (FPN) formulation
Key Outcomes
Design and optimize vision architectures
Topics
- Convolution as linear operator
- Kernel math and feature extraction
- Padding, stride, dilation effects
Key Outcomes
Derive and interpret convolution layers
Topics
- Mathematical definition of feature maps
- Receptive field calculation
- Hierarchical feature extraction math
Key Outcomes
Understand CNN spatial transformations
Topics
- Pooling as down sampling operator
- Max vs average pooling derivation
- Feature pyramid network (FPN) formulation
Key Outcomes
Design and optimize vision architectures