Pre-training
Pre-training is the initial phase of training a model on a large, general dataset before fine-tuning on specific tasks. Pre-training enables models to learn broad language or visual understanding that transfers to many applications.
Understanding Pre-training
Pre-training is the initial phase of training a large machine learning model on a vast, general-purpose dataset before it is adapted to specific downstream tasks through fine-tuning. In natural language processing, pre-training typically involves training a transformer model on billions of tokens of text using self-supervised objectives like next-token prediction or masked language modeling. This phase enables the model to learn grammar, factual knowledge, reasoning patterns, and contextual representations that transfer broadly across tasks. Models like BERT, GPT-4, and LLaMA invest enormous computational resources in pre-training, often requiring thousands of GPUs running for weeks. The pre-training and fine-tuning paradigm has revolutionized AI by enabling strong performance on specialized tasks with relatively little labeled data, since the model arrives at fine-tuning already possessing rich language understanding. This approach connects closely to transfer learning and 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.
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.