Representation Learning
Representation learning is the automatic discovery of useful data representations needed for machine learning tasks. Deep learning is fundamentally a form of representation learning that builds hierarchical feature abstractions.
Understanding Representation Learning
Representation learning is the set of techniques that enable machine learning models to automatically discover the optimal way to encode and represent raw data for downstream tasks, replacing the manual feature engineering that dominated traditional approaches. Deep neural networks excel at representation learning by building hierarchical features through successive layers: early layers might detect edges in images or basic word patterns in text, while deeper layers compose these into complex concepts like faces or semantic meaning. The learned representations, often called embeddings, capture essential structure in dense vector spaces where semantic similarity corresponds to geometric proximity. Techniques like autoencoders, contrastive learning, and the pre-training phase of large language models all fall under representation learning. The quality of learned representations determines model performance across tasks from image classification to natural language understanding. Transfer learning leverages this by reusing representations learned on large datasets for new tasks with limited data.
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.