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.
Understanding Adam Optimizer
The Adam optimizer has become the default optimization algorithm for training deep learning models due to its adaptive learning rate mechanism and robust performance across diverse tasks. Adam combines the momentum-based approach of SGD with momentum and the per-parameter learning rate adaptation of RMSProp, maintaining running averages of both the first moment (mean) and second moment (variance) of gradients. This allows it to handle sparse gradients and noisy data effectively, making it well-suited for training large neural networks, convolutional neural networks, and transformer models like BERT. In practice, Adam often converges faster than vanilla stochastic gradient descent, especially in the early stages of training. Hyperparameters like the learning rate, beta values, and epsilon still require tuning, and variants such as AdamW add weight decay for better generalization in tasks like fine-tuning large language 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.
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.
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.