Stochastic gradient descent

As explained in Sutskever's List

Stochastic gradient descent is an iterative optimization method that updates parameters using gradients estimated from individual examples or randomly selected small batches.

How Sutskever's List covers it

How deep it goes

Explains noisy gradient estimates, momentum, and learning-rate scheduling, while distinguishing first-order updates from curvature information.

Examples

Uses a foggy-hiker analogy and AlexNet's 128-image batches, 0.01 initial rate, and validation-triggered tenfold reductions.

What you can do after

Apply mini-batch updates with momentum and reduce the learning rate when validation accuracy flattens.

How it connects

Connects neural-network training with loss functions, gradients, momentum, learning rates, and validation accuracy.

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Also explained in

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  Grokking Machine LearningA gradient-descent variant that updates model parameters using one training data point at a time.
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  Bayesian Optimization in ActionMini-batch gradient descent is an optimization technique that updates model parameters using loss and gradient estimates computed from small subsets of the training data.
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  Deep Learning with Python, Third EditionA gradient-descent training method that computes updates from small batches of training examples rather than the full dataset at once.
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  Machine Learning Algorithms in DepthStochastic gradient descent is an iterative optimization method that updates parameters using gradients estimated from random data subsets.
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  Math and Architectures of Deep Learning
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  Natural Language Processing in Action, Second EditionStochastic gradient descent is an iterative optimization technique that updates model parameters using randomized or sampled training examples rather than the entire dataset at once.
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  Deep Learning with R, Third EditionMini-batch stochastic gradient descent is an optimization method that updates model parameters using gradients computed from randomly sampled small batches of training data.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIMini-batch gradient descent is an optimization algorithm that updates model parameters using gradients computed from small batches of examples.
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  liveBookmanning.com
  How Large Language Models WorkStochastic gradient descent is a gradient-descent technique that estimates parameter updates using a small random subset of training data.
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  Deep Learning with PyTorch, Second EditionAn optimization method that updates parameters using stochastic gradient descent.
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  liveBookmanning.com
  Transformers in Action

Compare how 12 books explain stochastic gradient descent →

Related concepts

• gradient descentkind of · Gradient descent is an iterative optimization method that updates parameters opposite the gradient of an objective function to reduce its value.
• adamincludes · Adam is an adaptive stochastic-gradient optimization algorithm that updates model parameters using moving estimates of the first and second moments of gradients.
• batchinguses · Batching is the grouping of multiple data items, requests, or operations so they can be processed together as a unit.
• learning rateconfigured by · A learning rate is a step-size parameter that controls the magnitude of updates made by an iterative learning or optimization process.
• logistic regressionused by · Logistic regression is a statistical classification model that estimates categorical outcome probabilities by applying a logistic or softmax function to a linear combination of input features.

This explainer was generated by Manning's knowledge graph from the text of Sutskever's List. The section links point to the parts of the book it draws on.