This project presents a complete workflow for cone detection in Formula Student Driverless scenarios using deep learning. It demonstrates how to use MATLAB® and Simulink® for data preparation and ...

miniML, a deep learning-based method, facilitates synaptic event analysis with high accuracy and versatility across diverse synaptic preparations, enabling high-throughput investigations of neural ...

Source: MathWorks Building deep learning models from scratch Supervised learning: Modulation classification with built CNN Modulation classification can also be accomplished with the Deep Learning ...

In this paper, we propose a new deep-learningbased framework for the automatic analysis of microorganism images in MATLAB. In fact, this is an effort to improve the accuracy and efficiency of the ...

In recent years, notable advancements in the design and training of deep learning models have led to significant improvements in image recognition performance, particularly on large-scale datasets.

The recently published book Understanding Deep Learning by [Simon J. D. Prince] is notable not only for focusing primarily on the concepts behind Deep Learning — which should make it highly a… ...

This paper proposes a Design Space Exploration for Edge machine learning through the utilization of the novel MathWorks FPGA Deep Learning Processor IP, featured in the HDL Deep Learning toolbox. With ...

This does not work in R2023b. Here's the output: Pretrained yolox_s network (mat file) already exists. Warning: While loading an object of class 'dlnetwork': Invalid ...

Researchers have developed a deep learning model, RECAST, that outperforms traditional methods in predicting earthquake aftershocks, especially with larger datasets. This advancement promises improved ...