Machine learning

CNN Architecture

Convolutional Layers: CNNs are built around layers called convolutional layers. These layers apply convolution operations to input images. Convolution involves sliding a small filter (also known as a kernel) over the input image to extract various features. Each convolutional layer can have multiple filters to capture different features.

Activation Function: After the convolution operation, an activation function (typically ReLU - Rectified Linear Unit) is applied element-wise to introduce non-linearity to the network. This allows the network to learn complex patterns and relationships in the data.

Pooling (Subsampling) Layers: After convolutional layers, pooling layers are often added to reduce spatial dimensions (width and height) and the number of parameters in the network. Max-pooling is a common pooling technique where the maximum value within a small region of the input is retained, and the rest is discarded.

Fully Connected Layers: After several convolutional and pooling layers, fully connected layers are used for classification or regression tasks. These layers are similar to those in traditional neural networks and help in making predictions based on the extracted features.

Flattening: Before entering the fully connected layers, the output from the previous layers is typically flattened into a vector. This vector is then passed through one or more fully connected layers.

Output Layer: The final layer of the CNN produces the network's output, which depends on the specific task. For image classification, this layer often has as many neurons as there are classes, and it uses a softmax activation function to produce class probabilities.

Loss Function: CNNs use a loss function (e.g., categorical cross-entropy for classification) to measure the difference between predicted values and actual labels during training. The goal during training is to minimize this loss.

Backpropagation: CNNs are trained using an optimization algorithm like stochastic gradient descent (SGD). Backpropagation is used to compute the gradients of the loss with respect to the network's weights and update the weights to minimize the loss.

Dropout: Dropout is a regularization technique used to prevent overfitting. It randomly drops a percentage of neurons during training to reduce the network's reliance on specific neurons.

Batch Normalization: This technique normalizes the activations within a layer during training, which can accelerate training and improve convergence.

Transfer Learning: CNNs often benefitfrom transfer learning, where pre-trained models (trained on large datasets like ImageNet) are fine-tuned for specific tasks. This helps leverage the knowledge learned from large datasets and reduces the need for training from scratch.