Convolutional Neural Network
A convolutional neural network is a specialized deep learning architecture that applies learned filters across input data to detect patterns. CNNs excel at image recognition, object detection, and visual understanding tasks.
Understanding Convolutional Neural Network
Convolutional Neural Networks are specialized deep learning architectures that process grid-structured data by applying learnable filters that slide across input to detect spatial patterns at multiple scales of abstraction. Convolutional layers extract local features like edges and textures, progressively combined in deeper layers to represent complex structures such as object parts and entire objects. This hierarchical feature learning, combined with parameter sharing and spatial invariance through pooling, makes CNNs extraordinarily effective for computer vision. The architecture powers breakthroughs in image classification with models like ResNet, object detection with systems like YOLO using bounding boxes, medical imaging for disease identification, and autonomous driving where visual perception is safety-critical. Training relies on backpropagation with optimizers like Adam, and techniques such as batch normalization are essential for generalization. While attention mechanism-based transformers are emerging as alternatives, CNNs remain fundamental to modern AI.
Category
Deep Learning
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Activation Function
An activation function is a mathematical function applied to a neuron's output to introduce non-linearity into a neural network. Common activation functions include ReLU, sigmoid, and tanh, each with different properties for gradient flow.
Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm that combines the benefits of AdaGrad and RMSProp. It adapts learning rates for each parameter using estimates of first and second moments of gradients.
Adapter Layers
Adapter layers are small trainable modules inserted into a pre-trained model to enable parameter-efficient fine-tuning. They allow task adaptation while keeping the original model weights frozen.
Attention Mechanism
An attention mechanism allows neural networks to focus on the most relevant parts of the input when producing each element of the output. Attention is the foundational innovation behind the Transformer architecture and modern large language models.
Autoencoder
An autoencoder is a neural network trained to compress input data into a compact representation and then reconstruct it. Autoencoders are used for dimensionality reduction, denoising, and learning latent representations.
Backpropagation
Backpropagation is the algorithm used to train neural networks by computing gradients of the loss function with respect to each weight. It propagates error signals backward through the network to update weights and minimize prediction errors.
Batch Normalization
Batch normalization is a technique that normalizes layer inputs across mini-batches during training to stabilize and accelerate neural network training. It reduces internal covariate shift and allows higher learning rates.
Batch Size
Batch size is the number of training examples used in one iteration of gradient descent. Larger batches provide more stable gradient estimates but require more memory, while smaller batches add beneficial noise.