Cnn

Convolutional Neural Networks

April 19, 2026
AI, Deep-Learning
Deep Learning, Cnn, Convolutional Neural Networks, Computer Vision, Feature Maps, Receptive Field, Padding, Pooling

Convolutional Neural Networks (CNN) #

Convolutional Neural Networks (CNNs) are specialised neural networks designed for data with spatial structure, especially images. They became the standard model for computer vision because they preserve spatial locality, reuse the same pattern detector across the image, and build representations hierarchically. In practical terms, a CNN starts by learning simple features such as edges and corners, then combines them into textures, shapes, object parts, and finally full semantic categories.

Deep CNN Architectures

April 19, 2026
AI, Deep-Learning
Deep Learning, Cnn, Deep CNN Architectures, LeNet, AlexNet, VGG, NiN, GoogLeNet, ResNet, Transfer Learning

Deep CNN Architectures #

Once the basic ideas of convolution, pooling, channels, and classifier heads are understood, the next step is to study how successful CNN architectures are designed in practice. The history of deep CNNs is not just a list of famous models. It is a progression of design ideas: smaller filters, more depth, better optimisation, bottlenecks, multi-scale processing, residual connections, and transfer learning.

Key takeaway:
Deep CNN architectures evolved by solving specific problems one by one: LeNet established the template, AlexNet proved deep learning could dominate large-scale vision, VGG simplified the design, NiN introduced powerful 1 × 1 ideas, GoogLeNet made multi-scale processing efficient, and ResNet solved the optimisation problem of very deep networks.

CNN Pipeline

April 22, 2026
AI, Deep-Learning
Deep Learning, Cnn, Keras

CNN Pipeline: Preprocessing & Models #

  • Understand CNN concepts deeply
  • Build CNN models step-by-step
  • Apply CNNs in assignments using Keras

Think of CNN as a pipeline: Image → Features → Patterns → Prediction

1. Image Representation #

\[ X \in \mathbb{R}^{H \times W \times C} \]
  • H = Height
  • W = Width
  • C = Channels

2. Convolution Operation #

\[ Z(i,j) = \sum_{m,n} X(i+m, j+n) \cdot K(m,n) \]
  • Sliding filter extracts features
  • Produces feature maps

3. Stride & Padding #

\[ Output = \frac{N - F + 2P}{S} + 1 \]

4. Activation (ReLU) #

\[ ReLU(x) = max(0, x) \]

5. Pooling #

  • Max Pooling → strongest feature
  • Average Pooling → smooth

6. Global Average Pooling #

\[ y_k = \frac{1}{HW} \sum_{i,j} x_{i,j,k} \]

7. Loss Function #

\[ L = - \sum y \log(\hat{y}) \]

8. CNN Architecture #

graph LR
A[Input Image] --> B[Conv]
B --> C[ReLU]
C --> D[Pooling]
D --> E[Conv Layers]
E --> F[Flatten / GAP]
F --> G[Dense]
G --> H[Output]

9. Training #

  • Forward pass
  • Loss computation
  • Backpropagation
  • Weight update

10. Keras Implementation #

Model #

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten

model = Sequential()

model.add(Conv2D(32, (3,3), activation='relu', input_shape=(64,64,3)))
model.add(MaxPooling2D((2,2)))

model.add(Conv2D(64, (3,3), activation='relu'))
model.add(MaxPooling2D((2,2)))

model.add(Flatten())

model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

Compile #

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

Train #

model.fit(X_train, y_train, epochs=10, batch_size=32)

Predict #

pred = model.predict(X_test)

11. Tips #

  • Normalize images
  • Use small filters
  • Avoid too many dense layers

12. Summary #

CNN = Automatic feature extractor + classifier