Deep Learning Unit Two Power Point Presentation

Convolutional Neural Networks (CNNs) are a specialized type of deep learning architecture designed to process data with a grid-like structure, such as images, video frames, or even audio spectrograms. Unlike traditional fully connected neural networks, which treat every input feature independently, CNNs exploit the spatial and local structure of data. They automatically learn to detect patterns such as edges, textures, shapes, and entire objects by applying small filters (kernels) that scan across the input. A CNN is built from several key components. The convolutional layers perform the core operation, where filters slide over the input to produce feature maps. Each filter is responsible for recognizing a particular local pattern, such as a horizontal edge or a color gradient. These layers share weights, meaning the same filter is applied across different parts of the input, making CNNs computationally efficient and translation invariant. After convolution, an activation function (commonly ReLU) introduces non-linearity, enabling the network to learn complex relationships. Pooling layers (such as max pooling or average pooling) reduce the spatial dimensions of the feature maps while preserving important information. This not only decreases computational load but also helps make the network more robust to small translations or distortions in the input. After several convolutional and pooling layers, the network flattens the high-level feature maps into a vector, which is passed through fully connected layers to produce the final output, such as class scores in image classification. Training a CNN follows the same general principles as other neural networks: forward propagation to compute outputs, a loss function (for example, cross-entropy in classification), and backpropagation with gradient descent to update filter weights. However, CNNs often require large datasets to learn effectively because they have many parameters. Data augmentation techniques, such as flipping, rotating, or cropping images, are commonly used to prevent overfitting and improve generalization. CNNs have powered some of the most important breakthroughs in computer vision. Early architectures like LeNet were used for digit recognition, while AlexNet (2012) showed the power of deep CNNs when combined with GPUs, sparking the modern deep learning revolution. Subsequent architectures such as VGGNet (with its simple stacked convolutions), GoogLeNet/Inception (using multi-scale filters), and ResNet (with residual connections to train very deep networks) have dramatically increased accuracy on benchmarks like ImageNet. The applications of CNNs extend far beyond image classification. They are used in object detection (for example, YOLO, Faster R-CNN), semantic segmentation (for example, U-Net), facial recognition, medical imaging diagnostics, autonomous driving, video analysis, and even style transfer or super-resolution image generation.