Thakshashila: Created page with "== Convolutional Neural Networks (CNNs) == A '''Convolutional Neural Network (CNN)''' is a type of deep learning model specially designed for working with '''image data''' 📷. CNNs are widely used in computer vision tasks like image classification, object detection, and face recognition. === 🧠 Why CNNs for Images? === Images are large (millions of pixels), and fully connected neural networks don't scale well with size. CNNs solve this by using convolution operati..."

2025-06-11T11:44:37Z

Created page with "== Convolutional Neural Networks (CNNs) == A '''Convolutional Neural Network (CNN)''' is a type of deep learning model specially designed for working with '''image data''' 📷. CNNs are widely used in computer vision tasks like image classification, object detection, and face recognition. === 🧠 Why CNNs for Images? === Images are large (millions of pixels), and fully connected neural networks don't scale well with size. CNNs solve this by using convolution operati..."

New page

== Convolutional Neural Networks (CNNs) ==

A '''Convolutional Neural Network (CNN)''' is a type of deep learning model specially designed for working with '''image data''' 📷. CNNs are widely used in computer vision tasks like image classification, object detection, and face recognition.

=== 🧠 Why CNNs for Images? ===

Images are large (millions of pixels), and fully connected neural networks don't scale well with size. CNNs solve this by using convolution operations to detect '''patterns''', '''edges''', and '''shapes''' in a smart and efficient way. 🎯

---

== ⚙️ Key Components of a CNN ==

{| class="wikitable"
! Layer
! Description
! Emoji Hint
|-
| '''Convolutional Layer'''
| Applies filters (kernels) to input image to extract features (edges, textures)
| 🔍🧱
|-
| '''Activation Layer'''
| Applies non-linearity (like ReLU) to activate features
| ⚡🧠
|-
| '''Pooling Layer'''
| Reduces spatial size by keeping the most important info (e.g., max pooling)
| 📉📦
|-
| '''Fully Connected Layer'''
| Final decision-making layer; connects features to output
| 🔗🎯
|-
| '''Softmax Layer'''
| Converts final output to probabilities (for classification)
| 📊✅
|}

---

== 🧮 Convolution Operation ==

The convolution layer slides a small filter (kernel) over the image and performs dot products between the filter and the image pixels.

:<math> y(i,j) = \sum_{m}\sum_{n} x(i+m, j+n) \cdot w(m,n) </math>

Where:
* <math>x</math> = input image
* <math>w</math> = filter
* <math>y</math> = feature map

---

== 🏗️ CNN Architecture Example ==

{| class="wikitable"
! Layer
! Size
! Description
|-
| Input
| 32x32x3
| Color image (RGB)
|-
| Conv Layer
| 28x28x16
| 16 filters, 5x5 kernel
|-
| ReLU
| 28x28x16
| Applies non-linearity
|-
| Max Pooling
| 14x14x16
| 2x2 pooling
|-
| Conv Layer
| 10x10x32
| 32 filters, 5x5 kernel
|-
| ReLU
| 10x10x32
| Activation
|-
| Max Pooling
| 5x5x32
| Reduce again
|-
| Fully Connected
| 1x1x128
| Flatten + dense layer
|-
| Output (Softmax)
| 1x1x10
| For 10-class classification
|}

---

== 📚 Applications of CNNs ==

* 👀 Image classification (e.g., Cats vs Dogs)
* 🧍 Object detection (e.g., YOLO, SSD)
* 🧠 Facial recognition
* 📦 Scene segmentation
* 🩺 Medical imaging (e.g., tumor detection)
* 📄 Optical Character Recognition (OCR)

---

== ⚠️ Advantages of CNNs ==

* Fewer parameters compared to fully connected networks
* Automatically learn features (no manual extraction)
* Translation-invariant — detects the same pattern anywhere in the image

---

== 🔧 Limitations ==

* Require large datasets for training
* Computationally expensive (need GPU for large models)
* Struggles with rotated or distorted objects (unless augmented)

---

== 📝 Summary ==

{| class="wikitable"
! Feature
! CNN Advantage
|-
| Parameter Efficiency
| Shared weights via filters
|-
| Feature Extraction
| Automatic, multi-level (edges → shapes → objects)
|-
| Task Suitability
| Great for images, videos, 2D signals
|-
| Common Use
| Classification, detection, segmentation
|}

---

== 📎 See Also ==

* [[Neural Networks]]
* [[Activation Functions]]
* [[Pooling Layer]]
* [[Convolution Operation]]
* [[Object Detection]]
* [[YOLO]]
* [[ResNet]]

Convolutional Neural Network - Revision history