Deep Learning

 

MATLAB for Deep Learning

Design, build, and visualize convolutional neural networks

With just a few lines of MATLAB® code, you can build deep learning models without having to be an expert. Explore how MATLAB can help you perform deep learning tasks.

  • MATLAB is fast: Run deployed models up to 7x faster than TensorFlow and up to 4.5x faster than Caffe2.
  • Easily access the latest models, including GoogLeNet, VGG-16, VGG-19AlexNet, and ResNet-50.
  • Use NVIDIA GPUs for your GPU programming: Accelerate training using multiple GPUs, the cloud, or clusters.
  • Use functions and tools to visualize intermediate results and debug deep learning models.
  • Automate ground-truth labeling using apps.
  • Work with models from Caffe and TensorFlow-Keras.

Start Using MATLAB for Deep Learning

Learn how to perform deep learning using MATLAB, a webcam, and a pretrained neural network to identify objects in your surroundings. Watch the video and follow along with the code:


camera = webcam;                           % Connect to camera
nnet = alexnet;                            % Load neural net
while true  
    picture = snapshot(camera);             % Take picture
    picture = imresize(picture,[227,227]); % Resize
    label = classify(nnet, picture);       % Classify 
    image(picture);                        % Show picture
    title(char(label));                    % Show label
    drawnow;   
end

 

Follow along with Joe:

Get a trial of the products you’ll need and download the AlexNet support package.

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Learn to Use MATLAB for Deep Learning in 2 Hours

Get a hands-on introduction to practical deep learning methods for image recognition. Topics include convolutional neural networks, using pre-trained networks, and transfer learning.

Go from basic tasks to more advanced maneuvers by walking through interactive examples and tutorials.

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