Today I want to follow up on my previous post, Defining Your Own Network Layer. There were two reader comments that caught my attention.The first comment, from Eric Shields, points out a key... 더 읽어보기 >>

Note: Post updated 27-Sep-2018 to correct a typo in the implementation of the backward function. One of the new Neural Network Toolbox features of R2017b is the ability to define your own network... 더 읽어보기 >>

Have you ever wondered what your favorite deep learning network is looking at? For example, if a network classifies this image as "French horn," what part of the image matters most for the... 더 읽어보기 >>

This is the second post in the series on using deep learning for automated driving. In the first post I covered object detection (specifically vehicle detection). In this post I will go over how deep... 더 읽어보기 >>

This is a guest post from Avinash Nehemiah, Avi is a product manager for computer vision and automated driving.  I often get questions from friends and colleagues on how automated driving... 더 읽어보기 >>

The MathWorks Neural Networks Toolbox development team has just posted two new items to the MATLAB Central File Exchange. The first is an importer for TensorFlow-Keras models. This... 더 읽어보기 >>

Hi There! While most of the information in this post is correct, this is an original post from 2017 and the world of GPUs changes rapidly. I would encourage you to check out a newer resource on... 더 읽어보기 >>

The R2017b release of MathWorks products shipped just two weeks ago, and it includes many new capabilities for deep learning. Developers on several product teams have been working hard on these... 더 읽어보기 >>

Hello, and welcome to the new MATLAB Central blog on deep learning! In my 24th year of MATLAB and toolbox development and design, I am excited to be tackling this new project.Deep learning refers to... 더 읽어보기 >>