Fine-Tuning
Fine-tuning is the process of taking a pre-trained model and continuing training on a smaller, task-specific dataset. It adapts general knowledge to specialized domains while requiring far less data and compute than training from scratch.
Understanding Fine-Tuning
Fine-tuning is the process of taking a pre-trained model and further training it on a smaller, task-specific dataset to adapt its learned representations to a particular application. This transfer learning approach is central to modern AI workflows, especially with foundation models like GPT, BERT, and large vision models that are first pre-trained on massive corpora and then fine-tuned for specific tasks such as sentiment analysis, medical diagnosis, or code generation. Fine-tuning typically involves unfreezing some or all of the model's frozen layers and training with a lower learning rate to preserve useful pre-trained features while adapting to new data. Techniques like LoRA and adapter layers enable parameter-efficient fine-tuning that modifies only a small fraction of weights. Fine-tuning dramatically reduces the data and compute required compared to training from scratch, making advanced deep learning accessible to teams with limited resources.
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.