Convolutional Neural Networks (CNNs): Vision and Pattern Recognition with AI
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📘 Structured Content:
Module 1: Introduction to CNNs
What are Convolutional Neural Networks?
Why CNNs are ideal for image and video tasks
Key areas of application: healthcare, security, robotics, automotive
Module 2: CNN Architecture
Input layer and image representation
Convolutional layers and feature extraction
Activation functions (ReLU)
Pooling layers (Max Pooling, Average Pooling)
Fully connected layers
Output layer for classification
Module 3: Convolution Operation in Depth
Filters and kernels explained
Stride and padding
Visualizing convolutions
Feature maps and edge detection
Module 4: Implementing CNN with TensorFlow/Keras
Using Conv2D, MaxPooling2D, and Dense
Building a CNN model with Sequential API
Compiling and training the model
Monitoring performance with accuracy/loss graphs
Module 5: Image Classification – Hands-On
MNIST digit recognition
CIFAR-10 object classification
Real-time image classification basics
Module 6: CNN Optimization Techniques
Data augmentation for better generalization
Dropout layers to prevent overfitting
Using pre-trained models (Transfer Learning with VGG16, ResNet)
Fine-tuning CNNs
Module 7: Advanced Use Cases of CNNs
Medical imaging and diagnostics
Face detection and recognition
Self-driving car vision systems
Object detection (YOLO, SSD basics)
Module 8: Capstone Project
Project: Design an Image Classifier with CNN
Choose a dataset (e.g., plant diseases, animal images, traffic signs)
Build and train a CNN
Evaluate accuracy
Export as a web-friendly model
🛠 Tools and Technologies Used
TensorFlow 2.x
Keras Sequential API
OpenCV for image handling
Python
Google Colab or Jupyter Notebooks
👥 Target Audience
This course is ideal for:
Beginners in computer vision
AI and ML learners wanting visual use cases
Web/mobile developers building AI apps
Educators and researchers in image analysis
Data scientists expanding into vision-based models
🎯 Learning Outcomes
By completing this lesson, you will:
Understand how CNNs process and classify images
Design and implement CNN models from scratch
Handle visual data and image preprocessing
Apply CNNs in real-world applications
Use pre-trained models to accelerate development
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