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Deep Learning Fundamentals

Import, build, train, tune, visualize, verify, and export deep neural networks

Deep Learning Toolbox™ provides tools for each stage of the deep learning workflow.

Preprocess data for deep network training using command-line functions and interactive apps.
Import pretrained networks from MATLAB^® or external platforms such as TensorFlow™ 2, TensorFlow-Keras, PyTorch^®, and ONNX™.
Build networks using command-line functions or interactively using the Deep Network Designer app.
Select training options and train networks using built-in training functions or custom training loops.
Improve network performance by tuning hyperparameters or running multiple trials using the Experiment Manager app.
Visualize and verify network behavior during and after training.
Export networks to external platforms such as TensorFlow 2 and ONNX.

Frequently Viewed Topics

Deep learning workflow: preprocess data, import or build network, train network, tune network, visualize and verify results, and export network

Featured Examples

Classify Image Using GoogLeNet

Classify an image using the pretrained deep convolutional neural network GoogLeNet.

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Transfer Learning with Deep Network Designer

Interactively fine-tune a pretrained deep learning network to learn a new image classification task.

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Create Simple Deep Learning Neural Network for Classification

Create and train a simple convolutional neural network for deep learning classification. Convolutional neural networks are essential tools for deep learning, and are especially suited for image recognition.

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Train Convolutional Neural Network for Regression

Fit a regression model using convolutional neural networks to predict the angles of rotation of handwritten digits.

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Time Series Forecasting Using Deep Learning

Forecast time series data using a long short-term memory (LSTM) network.

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Train Generative Adversarial Network (GAN)

Train a generative adversarial network to generate images.

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Image Captioning Using Attention

Train a deep learning model for image captioning using attention.

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Create a Deep Learning Experiment for Classification

Train a deep learning network for classification using Experiment Manager.

Since R2020a

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Grad-CAM Reveals the Why Behind Deep Learning Decisions

Use the gradient-weighted class activation mapping (Grad-CAM) technique to understand why a deep learning network makes its classification decisions. Grad-CAM, invented by Selvaraju and coauthors [1], uses the gradient of the classification score with respect to the convolutional features determined by the network in order to understand which parts of the image are most important for classification. This example uses the GoogLeNet pretrained network for images.

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