CNN Architecture ?

Question

steps of CNN architecture?

Madhu Patel · Answer

CNN or Convolutional Neural Network is a type of deep learning algorithm, used for tasks like image classification, object-detection and many more. Breakdown of each layer in CNN Input Layer- This layer receives the input data. Convolutional Layer- This layer applies convolutional operations to extract features, detect patterns by using filters. Activation Layer- It uses Activation function like ReLU to introduce non-linearity to the network, allowing it to learn more complex patterns. Pooling Layer- This layer reduces spatial dimensions of the feature maps, decreases computational load and prevent overfitting. Common pooling technique used in this layer is Max pooling. Flattening- Pooled feature maps are flattened in a single vector, preparing for the fully conntected layers. Fully Connected Layer- This layer connects neuron to their upcoming neuron, allowing to learn complex relationships between extracted features. Output Layer- This layer produces the output, such as classification label or regression Value. Filters- Small matrices that slide over the input data like an image to detect specific features Activation function- Means that it will decide whether the neuron's input to the network is important or not in the process of prediction using simpler mathematical operations Spatial dimension- width and height of an image or a feature map

Sangeetha S · Answer

Input Layer Example: 28x28 grayscale image (1 channel) Convolution Layer Applies filters (kernels) to extract features like edges or textures ReLU Activation Introduces non-linearity to the model Max Pooling Layer Reduces spatial dimensions (e.g., 2x2 pooling) Flatten Layer Converts 2D feature maps into a 1D vector Fully Connected Layer Dense layer that learns complex patterns Output Layer (Softmax) Produces probabilities for classification tasks

CNN Architecture ?

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