Gradient
A gradient is a vector of partial derivatives that indicates the direction and rate of steepest increase of a function. In neural networks, gradients are used to update weights in the direction that minimizes the loss function.
Understanding Gradient
A gradient is a vector of partial derivatives that indicates the direction and rate of steepest increase of a function with respect to its parameters. In machine learning, gradients are computed during backpropagation to determine how each weight in a neural network should be adjusted to minimize the loss function. The gradient tells the optimization algorithm which direction to move and by how much, forming the basis of gradient descent and its variants like Adam and SGD with momentum. Challenges related to gradients include vanishing gradients, where signals diminish in deep networks making early layers hard to train, and exploding gradients, where values grow uncontrollably. Techniques like batch normalization, residual connections, and careful initialization address these issues. Understanding gradients is fundamental to training any deep learning model and is central to concepts like backpropagation, fine-tuning, and distributed training.
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.