In this blog post we are going to show you how to use the newest MATLAB functions to: Import models from TensorFlow and PyTorch into MATLAB Export models from MATLAB to TensorFlow and PyTorch This is a brief blog post that points you to the right functions and other resources for converting deep learning models between MATLAB, PyTorch®, and TensorFlow. Two good resources to get started with are the documentation topics Interoperability Between Deep Learning Toolbox, TensorFlow, PyTorch, and ONNX and Tips on Importing Models from TensorFlow, PyTorch, and ONNX. If you have any questions about ..read more

Deep learning models are trained by using large sets of labeled data. Training a deep learning model can be time-consuming; it can take from hours to days. In this blog post, we will provide a few tips on how to speed up deep learning training with tools, functions, and apps. Deep Learning Toolbox offers more tools than the ones presented in this post, but we will give you enough resources to get started with speeding up the training of deep learning models. To learn more on training acceleration techniques, see Speed Up Deep Neural Network Training.   Transfer Learning Transfer learning ..read more

On March 20th, MATLAB R2024a was released with many updates for Deep Learning Toolbox. Exciting new features for deep learning help engineers create and use explainable, robust, and scalable deep learning models for automated visual inspection, wireless communications, computer vision, and many more applications.   Some of the new Deep Learning Toolbox capabilities are: Simulink co-execution blocks to simulate Python®-based (PyTorch®, TensorFlow, ONNX, and custom) models in a system-wide context. Explainability and verification tools to explain network results and verify the reliability ..read more

  Incremental learning is a machine learning approach that addresses the challenge of adaptively fitting models to new incoming data. The incremental learning approach is particularly useful to engineers that need to model streaming data. Often, engineers and other AI practitioners deploy machine learning to target devices, and incremental learning ensures that the models continue to work as intended if the data changes. In this blog post, we are going to explain what incremental learning is, why it is useful, and how to implement incremental learning with MATLAB tools and Simulink blocks ..read more

The following blog post is from Melda Ulusoy, Technical Marketing Manager at MathWorks. One of artificial intelligence (AI)’s first big successes was solving image classification problems with deep learning. AI has since been used in many other areas, including control systems. In this blog post, we will present an overview of AI for controls, highlight advantages of using MATLAB and Simulink for data-driven control, and provide details on how to register for an upcoming webinar.   AI for Control Algorithms Feedback control algorithms are used in advanced robots, electric motors, batterie ..read more

The following post is from Lucas García, Product Manager for Deep Learning Toolbox.  This is the third post in a 4-post series on Verification and Validation (V&V) for AI. The series began with an overview of V&V’s importance and the W-shaped development process, followed by a practical walkthrough in the second post, detailing the journey from defining AI requirements to training a robust pneumonia detection model. This post is dedicated to learning process verification. We will show you how to ensure that specific verification techniques are in place to guarantee that the pneumo ..read more

How to connect MATLAB to the OpenAI API to boost your NLP tasks. Have you heard of ChatGPT, Generative AI, and large-language models (LLMs)? This is a rhetorical question at this point. But did you know you can combine these transformative technologies with MATLAB? In addition to the MATLAB AI Chat Playground (learn more by reading this blog post), you can now connect MATLAB to the OpenAI Chat Completions API (which powers ChatGPT). In this blog post, we are talking about the technology behind LLMs and how to connect MATLAB to the OpenAI API. We also show you how to perform natural language pr ..read more

Lidar (light detection and ranging) is a remote sensing technology. Lidar sensors emit laser pulses that reflect off objects, allowing them to perceive the structure of their surroundings. The sensors record the reflected light energy to determine the distances to objects to create a 2D or 3D representations of the surroundings. With lidar technology a point cloud is created, that is a collection of data points plotted in 3-D space, where each point represents the X-, Y-, and Z-coordinates of a location on a real-world object’s surface, and the points collectively map the entire surface. Lidar ..read more

We are back from the NeurIPS 2023 conference. It was truly inspiring to engage with the AI community and learn more about the current developments and future of AI. We also enjoyed the opportunity to share how you can use MATLAB to integrate AI into cyber-physical systems. In this blog post, I am going to present some highlights from the MATLAB AI team’s presence at the conference. Figure: Our team at the MathWorks booth Left to right (front): Ashvant Ram Selvam, Lucas García, Brenda Zhuang, Sivylla Paraskevopoulou, Anoush Najarian, Maitreyi Chitale, and Jon Cherr ..read more

The following blog post is from Daniel Prieve, Digital Marketing Manager. Last month, Heather Gorr was interviewed for the TWIML AI Podcast (hosted by Sam Charrington). Heather shared knowledge, which she has gained as a MATLAB Product Manager, on how to prepare and test AI models before deploying the models to edge devices and embedded systems. You can find the podcast on “Deploying Edge and Embedded AI Systems”, here: TWIML’s web site Spotify Apple Podcasts In this blog post, we highlight a few key points from the TWIML podcast on Edge AI. But you will certainly learn a lot more by listen ..read more