Advanced

CNN: Deep Learning for Visual Intelligence

Name: CNN: Deep Learning for Visual Intelligence
Availability: InStock
Rating: 3.9 (1 reviews)

Convolutional Neural Networks (CNN) are a type of deep learning model designed for processing visual data. They automatically detect patterns like edges, shapes, and textures in images, making them highly effective for tasks such as image recognition and object detection.

3.9

|4h 27m

CNN: Deep Learning for Visual Intelligence course

What's Included

Lessons

4h 27m

Duration

Certificate

What You'll Master

Skills and outcomes you'll walk away with

Module 1: Foundations of Digital Images and Neural Networks

Module 2: The Convolutional Layer Mechanics

Module 3: Non-Linearity and Pooling Strategies

Practical Project - 1

Module 4: CNN Training and Optimization

Module 5: Evolution of CNN Architectures

Course Curriculum

16 lessons • 4h 27m total

Module 1: Foundations of Digital Images and Neural Networks

15m

Module 2: The Convolutional Layer Mechanics

21m

Module 3: Non-Linearity and Pooling Strategies

19m

Practical Project - 1

Module 4: CNN Training and Optimization

18m

Module 5: Evolution of CNN Architectures

19m

Module 6: Regularization and Data Augmentation

21m

Practical Project - 2

Module 7: Transfer Learning and Fine-Tuning

20m

Module 8: Object Detection and Localization

20m

Module 9: Image Segmentation (Semantic and Instance)

21m

Practical Project - 3

Module 10: Advanced Topics: Transformers and Attention

21m

Module 11: Generative Models for Images

22m

Module 12: Deployment and Productionizing Vision Models

19m

Practical Project - 4

Certification Path

Certification Exam

36 multiple-choice questions • 70% passing score required

Advanced Image Classification and Object Localization System

Students will design, implement, and evaluate a Convolutional Neural Network (CNN) architecture from scratch or using transfer learning (e.g., ResNet, EfficientNet) to solve a complex visual recognition task. The project involves comprehensive data preprocessing, data augmentation strategies, hyperparameter tuning, and a detailed performance analysis using metrics like precision-recall curves and visual interpretability tools such as Grad-CAM. The final submission must include a technical report and a functional Jupyter Notebook containing the full training and inference pipeline.

Verified Certificate

Earn a verified PDF certificate with unique verification ID upon completion • ₹299