Neural Network
A neural network is a computing system inspired by biological neurons that processes information through interconnected layers of nodes. Neural networks are the foundation of deep learning and power most modern AI applications.
Understanding Neural Network
A neural network is a computational model inspired by the structure of biological brains, consisting of interconnected layers of artificial neurons that learn to transform inputs into desired outputs through training. Each neuron applies a weighted sum followed by an activation function, and these simple operations, stacked across many layers, enable the network to learn complex patterns in data. Neural networks form the foundation of modern deep learning and power applications ranging from image recognition and speech synthesis to autonomous driving and drug discovery. Training occurs through backpropagation and optimization algorithms like Adam, which iteratively adjust the network's parameters to minimize a loss function. Variants include convolutional neural networks for visual tasks, recurrent neural networks for sequential data, and transformers for language, each tailored to exploit specific data structures.
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.