CNN
A CNN (Convolutional Neural Network) is a deep learning architecture designed to process grid-structured data like images. CNNs use convolutional filters to automatically learn spatial hierarchies of features.
Understanding CNN
CNNs, or Convolutional Neural Networks, are the deep learning architecture that sparked the modern AI revolution in computer vision when AlexNet won the ImageNet competition in 2012 by a dramatic margin. The key innovation is the convolutional layer, which slides learned filters across input to detect local patterns like edges and textures, building increasingly abstract representations through successive layers. Pooling layers reduce spatial dimensions while preserving important features, and fully connected layers perform final classification. Landmark architectures include VGGNet, which demonstrated the power of depth; ResNet, which introduced skip connections for extremely deep networks; and EfficientNet, which optimized scaling of width, depth, and resolution. CNNs power applications from facial recognition and medical imaging to autonomous driving and manufacturing inspection. While attention mechanism-based Vision Transformers have recently challenged CNN dominance, convolutional architectures remain widely deployed for bounding box detection and image classification.
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.