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The MATLAB Online Training Suite has been updated in the areas of Deep Learning and traditional Machine Learning! These are great self-paced courses that can get you from zero to hero pretty quickly.
Deep Learning Onramp (Free to everyone!) has been updated to use the dlnetwork workflow, such as the trainnet function, which became the preferred method for creating and training deep networks in R2024a.
Machine Learning Onramp (Free to everyone) has been updated with the following enhancements:
  • Content streamlined to reduce the focus on data processing and feature extraction, and emphasize the machine learning workflow.
  • Course example simplified by using a sample of the original data.
  • Classification Learner used in the course where appropriate.
The rest of the updates are for subscribers to the full Online Training Suite
The Deep Learning Techniques in MATLAB for Image Applications learning path teaches skills to tackle a variety of image applications. It is made up of the following four short courses:
Two more deep learning short courses are also available:
The Machine Learning Techniques in MATLAB learning path helps learners build their traditional machine learning skill set:
Brandon Armstrong
Brandon Armstrong
最後のアクティビティ: 2024 年 1 月 27 日

New deep learning specialization available on Coursera

MathWorks just released three new courses on Coursera liseted below. If you work with image or video data and are wanting to incorporate deep learning techniques into your workflow, this is a great opporutnity. The course creators monitor the discussion forums, so you can ask questions and get feedback on your work. Below are links to the three courses and a quick description of a project you'll complete in each.
  1. Introduction to Computer Vision for Deep Learning. You'll train a classifier to classify images of people signing the American Sign Language alphabet.
  2. Deep Learning for Object Detection. Move from just classification to finding object locations. You'll train a model to find different types of parking available on the MathWorks campus.
  3. Advanced Deep Learning Techniques for Computer Vision. You'll train anomaly detection models for medical images and use AI-assisted labeling auto label images.

Nowadays, many instructors are integrating AI to their courses. In a distance learning setting, the hardware students use to train their models vary. Training time of the deep learning models can be shortened with a pool of GPUs, CPUs or a pool of CPUs and GPUs locally or in the cloud. Accuracy of the results can depend on the hyperparameters used to train the models.

In MATLAB, Experiment Manager (introduced in R2020a) makes it easy to train networks with various hyperparameters and compare the results. Different models can be run in parallel by clicking on “Use Parallel” button in Experiment Manager App. But what if your computer has multiple CPU cores and a GPU? Would you be able to use Experiment Manager with a pool of CPUs and a GPU? The answer is “yes”. For example, my computer has 1 NVIDIA GPU and an 8 core CPU. To use all these computational resources, I typed these lines in my command window in MATLAB:

parpool(9);
spmd
if labindex==1
gpuDevice(1); %select GPU on worker 1
else
gpuDevice([]); %deselect GPU on remaining workers
end

Then, I clicked on “Use Parallel” button in Experiment Manager and used a modified setup function in Experiment Manager to change the execution environment from CPU to GPU:

if isempty(parallel.gpu.GPUDeviceManager.instance.SelectedDevice)
options=trainingOptions(~,"ExecutionEnvironment",'cpu');
else
options=trainingOptions(~,"ExecutionEnvironment",'gpu');
end

Default training options automatically use an NVIDIA GPU if there is one, and specific hardware can be selected using “ExecutionEnvironment” in the trainingOptions.

If you like to learn more about the fundamentals of parallel computing, check out “Parallel Computing Fundamentals” on our documentation and gain some hands-on experience with Parallel Computing through "Parallel Computing Hands-On Workshop" .

Boby S
Boby S
最後のアクティビティ: 2020 年 4 月 13 日

Mouse video tracking and deep learning

Hi I want to track a animal in my recorded video. I tried computer vision toolbox but it is not very accurate for this type of tracking. The recording is from top and the animal runs in a maze. I want to track body and head. The next step is classifying the movements in video using deep learning but again we do not have a trained network.