Unsupervised Pre-training
Unsupervised pre-training is the process of training a model on unlabeled data to learn general representations before fine-tuning on labeled data. It is the foundation of modern foundation models and transfer learning.
Understanding Unsupervised Pre-training
Unsupervised pre-training is a training strategy where a model first learns general representations from large amounts of unlabeled data before being fine-tuned on specific downstream tasks with labeled examples. This approach has become the dominant paradigm in modern AI, powering large language models that learn from vast text corpora and vision models trained on millions of images through techniques like masked autoencoders. Pre-training captures broad statistical patterns, linguistic structure, and visual features that transfer effectively across many tasks, dramatically reducing the amount of labeled data needed for specialization. The effectiveness of unsupervised pre-training is closely tied to scaling laws, as larger models pre-trained on more data consistently produce better representations. This paradigm enables capabilities like few-shot prompting and emergent behavior in generative models. Careful weight initialization and gradient clipping are critical technical considerations for stable pre-training of these increasingly large models.
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.