Deep Learning

Understanding and using deep learning networks

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Creating a DAG Network from DAG Parts 4

Posted by Steve Eddins,

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) DAGs into a network. I asked the development team about this. I learned that,... read more >>

Create a Simple DAG Network 6

Posted by Steve Eddins,

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 digits dataset. As the first step, I'll create the main branch, which follows the... read more >>

Defining Your Own Network Layer (Revisited) 1

Posted by Steve Eddins,

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 conclusion from the Clevert, Unterthiner, and Hichreiter paper that I overlooked. I initially focused just on the... read more >>

Defining Your Own Network Layer 9

Posted by Steve Eddins,

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 layer. Today I'll show you how to make an exponential linear unit (ELU) layer.Joe helped me with today's... read more >>

Network Visualization Based on Occlusion Sensitivity

Posted by Steve Eddins,

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 classification? Birju Patel, a developer on the Computer Vision System Toolbox team, helped me with the main... read more >>

Deep Learning for Automated Driving (Part 2) – Lane Detection 8

Posted by Avi Nehemiah,

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 learning is used to find lane boundaries. Lane Detection Lane detection is the identification of the location... read more >>

Deep Learning for Automated Driving (Part 1) – Vehicle Detection 2

Posted by Avi Nehemiah,

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 systems perceive their environment and make “human-like” decisions and how MATLAB is used in these systems. Over the next two blog posts... read more >>

New File Exchange Submissions 1

Posted by Steve Eddins,

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 submission enables you to import a pretrained Keras model and weights and then use the model for prediction or transfer learning. Or,... read more >>

Deep Learning with GPUs and MATLAB 6

Posted by Steve Eddins,

MATLAB users ask us a lot of questions about GPUs, and today I want to answer some of them. I hope you'll come away with a basic sense of how to choose a GPU card to help you with deep learning in MATLAB.I asked Ben Tordoff for help. I first... read more >>

Deep Learning with MATLAB R2017b 5

Posted by Steve Eddins,

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 capabilities, and everybody is excited to see them make it into your hands. Today, I'll give you a little... read more >>

These postings are the author's and don't necessarily represent the opinions of MathWorks.