Perceptron
A perceptron is the simplest type of artificial neural network consisting of a single neuron that computes a weighted sum of inputs and applies a threshold function. It is the fundamental building block of more complex networks.
Understanding Perceptron
The perceptron is the simplest form of a neural network, consisting of a single artificial neuron that takes multiple weighted inputs, sums them, applies an activation function, and produces a binary output. Invented by Frank Rosenblatt in 1958, the perceptron was one of the earliest machine learning algorithms and could learn to classify linearly separable data. Its limitation, famously highlighted by Minsky and Papert, was the inability to solve nonlinear problems like the XOR function, which contributed to the first AI winter. The multi-layer perceptron addressed this by stacking multiple layers of neurons with nonlinear activation functions, trained via backpropagation, forming the basis of modern deep learning. Understanding the perceptron remains valuable because it introduces fundamental concepts like weighted sums, decision boundaries, and gradient-based optimization that scale directly to the complex neural network architectures used today.
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.