Deep Learning Fundamentals
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.
Categories
- Preprocess Data for Deep Neural Networks
Manage and preprocess data for deep learning
- Import Deep Neural Networks
Load built-in pretrained networks and import networks from external platforms
- Build Deep Neural Networks
Build networks using command-line functions or interactively using the Deep Network Designer app
- Train Deep Neural Networks
Train networks using built-in training functions or custom training loops
- Tune Deep Neural Networks
Programmatically and interactively tune training options, resume training from a checkpoint, and investigate adversarial examples
- Visualize and Verify Deep Neural Networks
Visualize network behavior, explain predictions, and verify robustness
- Export Deep Neural Networks
Export networks to external deep learning platforms
Featured Examples
Classify Image Using GoogLeNet
Classify an image using the pretrained deep convolutional neural network GoogLeNet.
Prepare Network for Transfer Learning Using Deep Network Designer
Interactively fine-tune a pretrained deep learning network to learn a new image classification task.
Create Simple Deep Learning Neural Network for Classification
Create and train a simple convolutional neural network for deep learning classification.
Train Convolutional Neural Network for Regression
Train a convolutional neural network to predict the angles of rotation of handwritten digits.
Time Series Forecasting Using Deep Learning
Forecast time series data using a long short-term memory (LSTM) network.
Train Generative Adversarial Network (GAN)
Train a generative adversarial network to generate images.
Image Captioning Using Attention
Train a deep learning model for image captioning using attention.
Create a Deep Learning Experiment for Classification
Train a deep learning network for classification using Experiment Manager.
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.
Verify Robustness of Deep Learning Neural Network
Verify the adversarial robustness of a deep learning neural network.
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