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.
Understanding Autoencoder
Autoencoders are a class of neural networks trained to learn efficient compressed representations of data by encoding inputs into a lower-dimensional latent space and then reconstructing the original input from that compressed form. The architecture consists of an encoder that maps input to a bottleneck representation and a decoder that reconstructs from it, with the network optimized to minimize reconstruction error. Variational autoencoders (VAEs) extend this concept by learning a continuous, structured latent space suitable for generating new data, making them an important generative model. Practical applications include anomaly detection, where data that reconstructs poorly is flagged as anomalous, image denoising where corrupted images are cleaned through the encode-decode process, and dimensionality reduction as an alternative to techniques like PCA. Autoencoders also serve as pretraining mechanisms for deep learning models and have influenced the development of contrastive learning approaches for self-supervised representation learning.
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.
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.
Boltzmann Machine
A Boltzmann machine is a stochastic recurrent neural network that can learn a probability distribution over its inputs. Restricted Boltzmann Machines (RBMs) were influential in the deep learning revolution as building blocks for deep belief networks.