Vanishing Gradient
The vanishing gradient problem occurs when gradients become extremely small during backpropagation through many layers, making it difficult to train deep networks. Skip connections and normalization techniques were developed to address this issue.
Understanding Vanishing Gradient
The vanishing gradient problem occurs when gradients become exponentially smaller as they propagate backward through many layers during backpropagation, effectively preventing earlier layers from learning. This was a major obstacle in training deep neural networks, as the sigmoid function and tanh activation functions compress gradients into increasingly narrow ranges with each layer. The problem severely limited the depth of trainable networks for decades. Key solutions include the ReLU activation function, which maintains constant gradients for positive inputs, residual connections (skip connections) that provide gradient shortcuts as used in ResNet architectures, and gating mechanisms in LSTM networks. Batch normalization and careful weight initialization techniques like Xavier and He initialization also mitigate the problem. The resolution of vanishing gradients was a pivotal breakthrough enabling modern deep 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.