Neural Architecture Search
Neural Architecture Search (NAS) is an automated process for discovering optimal neural network architectures for a given task. NAS uses search algorithms to explore vast design spaces that would be impractical to navigate manually.
Understanding Neural Architecture Search
Neural Architecture Search (NAS) is an automated method for discovering optimal neural network designs without relying on human intuition or manual experimentation. Instead of hand-crafting layer configurations, NAS algorithms explore a vast search space of possible architectures using techniques like reinforcement learning, evolutionary algorithms, or gradient-based optimization to find models that maximize performance on a given task. Google's NASNet and EfficientNet families were designed using NAS and achieved state-of-the-art results in image classification while being computationally efficient. The approach falls under the broader umbrella of AutoML, aiming to democratize deep learning by reducing the expertise needed to design high-performing models. While NAS can be computationally expensive, advances in weight sharing and one-shot methods have made it increasingly practical for real-world applications.
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.