LeNet
LeNet is an early convolutional neural-network architecture designed primarily for handwritten-digit and character recognition.
Math and Architectures of Deep Learning
A convolutional neural network architecture introduced for digit recognition and used here as the example image classifier.
- How it is used
- Uses a compact convolutional architecture to classify handwritten MNIST digits.
- How deep it goes
- Explains layer dimensions, pooling, nonlinear activation, and implementation, but stops short of detailed optimization internals.
- Examples
- Uses MNIST digit images, LeNet layer dimensions, PyTorch Lightning modules, training, and inference as concrete examples.
- What you can do after
- Build and train a LeNet digit classifier with PyTorch Lightning, then run inference on a test batch.
Read how Math and Architectures of Deep Learning explains LeNet →
Distributed Machine Learning Patterns
LeNet is a convolutional neural network architecture originally designed for handwritten-digit recognition.
- How it is used
- Uses a compact CNN architecture to classify image-like video frames into selected entities.
- How deep it goes
- Explains the two-part architecture and input processing, but stops short of layer-specific dimensions or training procedures.
- Examples
- Uses handwritten-digit recognition and 28 × 28 video frames classified into ten selected entities.
- What you can do after
- Evaluate/understand a compact CNN architecture for image and frame classification.
Read how Distributed Machine Learning Patterns explains LeNet →
Machine Learning Algorithms in Depth
LeNet is a convolutional neural network architecture designed for visual recognition tasks.
- How it is used
- Classifies handwritten MNIST digits with a convolutional neural network.
- 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.
Read how Machine Learning Algorithms in Depth explains LeNet →
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.
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