leana _ smith asked . 2022-09-17
Why should I choose matlab deep learning toolbox
Why should I choose matlab deep learning toolbox over other opensource frameworks like caffe, onnx, pytorch, torch etc?
Hi, I see, the name of the product has been changed from "Neural Network Toolbox" to "Deep learning toolbox". But, I do not see many deep learning research papers implemented in MATLAB. Everyone uses PyTorch, Tensorflow, Caffe etc. Even the popular online courses as well classroom courses at top places like stanford have stopped teaching in MATLAB.
I have been big fan of MATLAB and other mathworks products and mathworks' participation in ONNx appears interesting to me., but seems like, I have no option left apart from moving to other tools. Please let me why I should use MATLAB which is paid, rather than the freely available popular tools like pytorch, tensorflow, caffe etc. I can easily get codes for free there, also good community, documentation everything, in fact those frameworks are very convenient e.g. tensor which is easy to implement in python, but difficult in MATLAB, like this there are many such examples.
I am pretty sure you guys would have an answer to it, since you have a lot of toolboxes revolving around deep learning, also the major customers of Model-based design tools like automotive, defense etc. must need deep learning.
deep learning , deep neural networks , open source
Prashant Kumar answered . 2024-05-18 09:03:19
Thanks for this question, I’d like to provide an update to Sebastian’s answer as a lot has changed in MATLAB since 2018. MATLAB is used by Engineers and Scientists to develop, automate and integrate deep learning models into their domain-specific workflows. It helps them achieve this by providing:
- An open framework that supports interoperability with Python and other open source deep learning frameworks.
- Capabilities that extend beyond modeling to developing end to end applications.
- Integration and Simulation of Deep Learning models into larger domain-specific systems.
- Dedicated support from engineers at MathWorks, developers of MATLAB.
Further information:
The development efforts of MATLAB are aimed at addressing the entire system design workflow for building systems that rely on Deep Learning.
Deep Learning System Design Workflow (scroll left to right)
This workflow is being applied to developing Domain Specific Deep Learning applications in areas such as:
For each of the domain’s mentioned above MATLAB provides specialized tools and functions for data preprocessing and preparation, training interfaces, evaluation tools and reference examples.
Data Preparation:
Having the right data is critical to the success of developing a Deep Learning model but can be a time consuming process. MATLAB provides Apps for automating domain-specific labeling (Signal Labeler, Image Labeler, Video Labeler & Audio Labeler) and functions for pre-processing data, which aim at saving development time.
Modeling:
Users have the choice if they would like to use models developed in MATLAB, pretrained models such as GoogleNet or ResNet-50, or those available in OpenSource Frameworks TensorFlow, PyTorch or ONNX through Framework Interoperability. MATLAB's Deep Learning toolbox provides interactive Apps that automate network design, training and experiment management, allowing users to avoid steps that can be automated or eliminated.
Simulation & Test:
Deep learning models created in MATLAB can be integrated into system-level designs, developed in Simulink, for testing and verification using simulation. System-level simulation models can be used to verify how deep learning models work with the overall design, and test conditions that might be difficult or expensive to test in a physical system. THIS example shows how deep learning can be integrated with a controls model in Simulink, further more AI models can be tested using 3D simulation environments with sensor models as shown in THIS example.
Deployment:
These applications are being deployed to embedded and production systems through automatic code generation. Automatic code generation generates optimized native code for Intel and ARM CPU's, FPGA's and SoC's and NVIDIA GPU's for Deep Networks along with pre-processing and post-processing, eliminating errors of transcription or interpretation.
Examples in Industry and Academia:
MATLAB users in industry and academia have had success using MATLAB deep learning to solve challenging problems such as terrain recognition using hyperspectral data and converting brain signals to word phrases.
To Summarize on why engineers and scientists use MATLAB and MathWorks for Deep Learning:
- MATLAB is focused towards engineering and science workflows
- MATLAB is a platform that covers the entire workflow where users can improve productivity by using interactive apps that expedite analysis and automatically generate reusable code
- Models can be deployed anywhere, from embedded to cloud systems
- MATLAB has interoperability with OpenSource frameworks Tensorflow and PyTorch
- Users have access to support from experienced MathWorks engineers in development, training & consulting.
If you have any questions regarding Deep Learning, please don't hesitate to contact me or any one of our Deep Learning experts at MathWorks via the "Have Questions? Talk to a deep learning expert." form on our Deep Learning solution page.
Not satisfied with the answer ?? ASK NOW
Australia
UK
UAE
Singapore
Canada 
New
Zealand
Malaysia
USA
India
South Africa 
Ireland
Saudi
Arab
Qatar
Kuwait
Hongkong
