LeNet

As explained in Machine Learning Algorithms in Depth

LeNet is a convolutional neural network architecture designed for visual recognition tasks.

LeNet is a convolutional neural network for handwritten-digit classification. Its architecture repeatedly applies convolution, ReLU activation, and max-pooling, increasing feature counts while reducing feature-map dimensions. section 10.2.1

The stack ends by flattening the feature map, passing it through a dense layer, and applying a softmax classifier. This design learns progressively richer visual features before producing digit-class probabilities. section 10.2.1

A practical LeNet workflow includes:

• Load MNIST and reshape images to the required image dimensions. section 10.2.1
• Define training parameters and stack convolutional, ReLU, and max-pooling layers. section 10.2.1
• Compile and train the model with callbacks, record training history, and evaluate it on test data. section 10.2.1
• Inspect loss and accuracy curves, use early stopping when validation loss stops improving, and save predictions. section 10.2.1

Figure 10.4 LeNet architecture for MNIST digits classification

Use the architecture figure to trace the progression from image input through convolution, activation, pooling, and final classification. section 10.2.1

MNIST digit classification

A Keras/TensorFlow implementation trains a simple LeNet-style CNN on MNIST and achieves about 99% test accuracy, with training and validation plots used to inspect learning behavior. section 10.2.1

How Machine Learning Algorithms in Depth covers it

How deep it goes

Explains the layer sequence, training workflow, callbacks, evaluation, and monitoring, but does not derive convolution internals.

Examples

Uses MNIST images, an architecture diagram, training and validation curves, saved predictions, and a Keras/TensorFlow implementation.

What you can do after

Apply it via Keras/TensorFlow to train, evaluate, monitor, and save predictions from an MNIST convolutional classifier.

How it connects

Connects convolutional layers with ReLU, max-pooling, dense layers, softmax classification, MNIST, and later CNN architectures such as ResNet.

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Also explained in

• 

  liveBookmanning.com
  Math and Architectures of Deep LearningA convolutional neural network architecture introduced for digit recognition and used here as the example image classifier.
• 

  liveBookmanning.com
  Distributed Machine Learning PatternsLeNet is a convolutional neural network architecture originally designed for handwritten-digit recognition.

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Related concepts

• convolutional neural networkkind of · A convolutional neural network is a neural-network architecture that applies learned convolutional operations to extract patterns from structured data.
• MNIST datasetuses · MNIST is a labeled dataset of 28-by-28-pixel grayscale images representing handwritten digits from zero through nine.
• classifieraddresses · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• convolutional layerpart of · A convolutional layer is a neural-network layer that applies shared learned filters to local regions of structured input to produce feature representations.
• PyTorchuses · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
• language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Algorithms in Depth. The section links point to the parts of the book it draws on.