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.
Understanding Boltzmann Machine
Boltzmann machines are stochastic neural networks rooted in statistical physics that learn probability distributions over their inputs through an energy-based framework, where lower-energy configurations represent more probable states. The Restricted Boltzmann Machine (RBM), a simplified variant with no intra-layer connections, played a pivotal role in the deep learning renaissance of the mid-2000s when Geoffrey Hinton demonstrated that stacking RBMs could effectively pre-train deep neural networks. This approach, known as deep belief networks, provided a practical way to initialize deep architectures before backpropagation fine-tuning, overcoming the vanishing gradient problem that had hindered deep learning for decades. RBMs were applied to collaborative filtering (powering early recommendation systems like Netflix's), dimensionality reduction, and feature learning. Although modern techniques like batch normalization, better activation functions, and large-scale supervised training have largely supplanted RBM-based pre-training, Boltzmann machines remain theoretically important for understanding generative modeling and energy-based 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.