MathWorks shipped our R2018a release last month. As usual (lately, at least), there are many new capabilities related to deep learning. I showed one new capability, visualizing activations in DAG... 더 읽어보기 >>

The R2018a release has been available for almost two week now. One of the new features that caught my eye is that computing layer activations has been extended to GoogLeNet and Inception-v3. Today I... 더 읽어보기 >>

In my 14-Feb-2018 blog post about creating a simple DAG network, reader Daniel Morris wanted to know if there's a less tedious way, compared to adding layers one at a time, to combine two (or more)... 더 읽어보기 >>

Creating a Simple DAG NetworkToday I want to show the basic tools needed to build your own DAG (directed acyclic graph) network for deep learning. I'm going to build this network and train it on our... 더 읽어보기 >>

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 >>