Vanishing gradient problem

As explained in Data Without Labels

The vanishing gradient problem is an optimization difficulty in which gradients become extremely small during learning, hindering the retention of information across many sequence steps.

The vanishing gradient problem occurs when gradients become extremely small as they propagate backward through many layers or sequence steps. Learning then slows, and recurrent networks struggle to retain information from far back in a sequence. section 8.7.1 section 8.9.1

The problem appears in several neural-network settings:

• Plain recurrent neural networks can lose long-term dependencies because gradients vanish while information moves across time steps. section 8.7.1
• Deep belief networks can encounter diminishing gradients as errors propagate backward through many layers, complicating fine-tuning. section 8.9.1
• Long short-term memory networks mitigate the problem with input, forget, and output gates that regulate information flow. section 8.7.1

Figure 8.14 The RNN process. RNNs have internal memory, which allows them to use information from the previous inputs to influence the current input and outputs.

Use the RNN process diagram to trace how hidden-state information passes from previous inputs into current inputs and outputs, clarifying why long sequences challenge information retention. section 8.7.1

How Data Without Labels covers it

How deep it goes

Defines gradient diminution and its training effect, then identifies LSTM gates as mitigation without detailing their implementation.

Examples

Uses chatbot sequence handling, speech recognition, and deep belief network fine-tuning to illustrate the problem’s practical consequences.

What you can do after

Evaluate why plain recurrent networks and deep belief networks lose learning effectiveness, and recognize LSTM gating as a mitigation.

How it connects

Connects recurrent neural networks, long-term dependencies, long short-term memory, gated recurrent units, deep belief networks, and restricted Boltzmann machines.

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Also explained in

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  Sutskever's ListA vanishing gradient is an optimization failure in which derivatives become too small to support effective parameter updates during neural-network training.
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  Grokking Machine LearningA neural-network training problem in which gradients become extremely small as they are propagated through layers, slowing or preventing learning.
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  Math and Architectures of Deep LearningThe training difficulty in deep networks where repeated multiplication of small derivatives makes early-layer gradients nearly zero.
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  Deep Learning with R, Third EditionA vanishing gradient is a training condition in which gradients become extremely small as they propagate through a deep network, slowing or preventing parameter updates in earlier layers.
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  Deep Learning with Python, Third EditionA training problem in deep networks where gradient signals become too small to effectively update earlier layers.
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  Inside Deep Learning: Math, Algorithms, ModelsA training instability in which gradients become too small for effective learning updates.
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  Graph Neural Networks in ActionAn optimization problem in which gradients become progressively smaller as they are propagated through many computational steps, hindering learning of long-range dependencies.
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  Evolutionary Deep Learning: Genetic algorithms and neural networksA neural-network training failure in which backpropagated gradients become extremely small across successive layers, limiting effective parameter updates.
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  Grokking AI Algorithms, Second EditionThe vanishing gradient problem is a training difficulty in which derivatives become extremely small and cause neural-network parameter updates to become ineffective.
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  Deep Learning with PyTorch, Second EditionThe difficulty in training deep networks when gradients become too small as they propagate backward through many layers.
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  Learn Generative AI with PyTorchA training failure mode in deep networks where gradient signals become too small to effectively update early layers.
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  LLMs in ProductionA vanishing gradient is a training condition in which propagated derivatives become increasingly small and impede parameter updates.
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  Build a Large Language Model (From Scratch)A training difficulty in deep neural networks where gradients shrink as they propagate backward, hindering learning in early layers.

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Related concepts

• ReLUaddressed by · ReLU, or rectified linear unit, is an activation function that maps an input x to max(0, x).
• residual connectionaddressed by · A residual connection is a neural-network pathway that carries an earlier representation around one or more transformations to a later computation, where it can be combined with the transformed representation.
• LSTMaddressed by · An LSTM is a recurrent neural network architecture with gated memory mechanisms that control information flow and help retain or update information across sequences.
• ResNetaddressed by · ResNet is a family of deep neural-network architectures built from residual blocks with shortcut connections that facilitate information flow and optimization.
• GRUaddressed by · A gated recurrent unit (GRU) is a recurrent neural-network architecture that uses learned gates to regulate information flow and update a hidden state across sequence steps.
• normalizationaddressed by · Normalization is the process of transforming a value, representation, or data structure to satisfy a specified standard, scale, or structural constraint.

This explainer was generated by Manning's knowledge graph from the text of Data Without Labels. The section links point to the parts of the book it draws on.