Exploding gradient

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

An exploding gradient is a neural-network training failure in which backpropagated gradient values become extremely large across successive layers.

An exploding gradient is a training failure in which gradient components become exceptionally large as signals pass through successive layers that magnify them. The risk increases as networks become deeper or more complex, because backpropagated loss is repeatedly transformed across layers. section 1.3.1

Common responses include:

• Normalize input data and intermediate layer values to keep signals controlled as they move through the network. section 1.3.1
• Use BatchNormalization between layers; it centers feature-map values around zero by subtracting their mean and dividing by their standard deviation. section 8.1.4
• Treat normalization and dropout as added complexity rather than free fixes: they increase computational requirements, and normalization can smooth important data features. section 1.3.1

Figure 1.2 Common problems when growing DL systems

Use the figure to compare exploding gradients with vanishing gradients and other problems that appear as deep-learning systems grow. section 1.3.1

Figure 8.7 The BatchNormalization process

Use the figure to inspect how BatchNormalization calculates feature-map means and variances before passing values to the next layer. section 8.1.4

How Evolutionary Deep Learning: Genetic algorithms and neural networks covers it

How deep it goes

Explains signal magnification across layers and normalization-based mitigation, but stops short of detailed optimization algorithms.

Examples

Uses a deep-network problem figure and a convolutional feature-map calculation to illustrate gradient instability and normalization.

What it assumes

Assumes basic neural-network layers, weights, loss, and backpropagation while defining the failure mode through layered signal behavior.

What you can do after

Diagnose gradient instability and evaluate normalization or dropout as practical architectural responses.

How it connects

Connects exploding gradients with backpropagation, vanishing gradients, normalization, dropout, BatchNormalization, CNNs, and deep networks.

liveBookmanning.com

Read Evolutionary Deep Learning: Genetic algorithms and neural networks on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Transformers in ActionThe exploding gradient problem is a training failure in which gradients become excessively large, causing unstable parameter updates and potentially preventing convergence.
• 

  liveBookmanning.com
  LLMs in ProductionAn exploding gradient is a training condition in which propagated derivatives grow excessively large and destabilize optimization.
• 

  liveBookmanning.com
  Inside Deep Learning: Math, Algorithms, ModelsA training instability where gradient magnitude becomes very large, leading to excessively large parameter updates.
• 

  liveBookmanning.com
  Machine Learning System DesignAn exploding gradient is a training condition in which gradient magnitudes become excessively large and destabilize optimization.

Compare how 5 books explain exploding gradient →

Related concepts

• gradient clippingaddressed by · Gradient clipping is an optimization technique that limits gradient values or their norm before parameter updates to reduce excessively large or unstable updates.
• normalizationaddressed by · Normalization is the process of transforming a value, representation, or data structure to satisfy a specified standard, scale, or structural constraint.
• weight initializationaddressed by · Weight initialization is the process of assigning starting values to a model’s trainable parameters before training or optimization.
• batch normalizationaddressed by · Batch normalization is a neural-network technique that normalizes activations using statistics computed over a mini-batch, with learned scale and shift parameters and typically running statistics for inference.
• recurrent neural networkproduced by · A recurrent neural network is a neural network that processes sequences by updating an internal state across successive inputs, allowing earlier inputs to influence later computations.

This explainer was generated by Manning's knowledge graph from the text of Evolutionary Deep Learning: Genetic algorithms and neural networks. The section links point to the parts of the book it draws on.