Artificial neural networks | Deep learning software applications
SqueezeNet is the name of a deep neural network for computer vision that was released in 2016. SqueezeNet was developed by researchers at DeepScale, University of California, Berkeley, and Stanford University. In designing SqueezeNet, the authors' goal was to create a smaller neural network with fewer parameters that can more easily fit into computer memory and can more easily be transmitted over a computer network. (Wikipedia).
What is Global Squeeze? | Head Squeeze
Global Squeeze: Our team of comedians put this week's top stories through the mincer and unveil the real news behind the news. http://www.youtube.com/user/HeadsqueezeTV http://www.youtube.com/subscription_center?add_user=HeadsqueezeTV
From playlist Global Squeeze
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Networking
James May introduces ** Comedy Week ** on Head Squeeze
Especially for Comedy Week, Head Squeeze has compiled a playlist of videos we think are funny - but couldn't help but pick the ones about science too! Watch more YouTube Comedy Week videos here: http://yt.be/comedyweek Watch the whole playlist here: http://www.youtube.com/playlist?list=PL
From playlist COMEDY WEEK on Head Squeeze
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist LinkedIn
Lecture 15 | Efficient Methods and Hardware for Deep Learning
In Lecture 15, guest lecturer Song Han discusses algorithms and specialized hardware that can be used to accelerate training and inference of deep learning workloads. We discuss pruning, weight sharing, quantization, and other techniques for accelerating inference, as well as parallelizati
From playlist Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)
Image Classification on ARM CPU: SqueezeNet on Raspberry Pi
See a demonstration of image classification using deep learning on a Raspberry Pi™ from MATLAB using the Raspberry Pi support package. - Deep Learning Inference for Object Detection on Raspberry Pi: http://bit.ly/2E5I8zp - Raspberry Pi Support from MATLAB: http://bit.ly/2GLCIe2 - Deep L
From playlist Raspberry Pi Tutorials
Squeeze - Cool For Cats (Official Music Video)
The official 'Cool For Cats' music video. NOW REMASTERED IN HD! Taken from the Squeeze album 'Cool For Cats' Listen to the music of Squeeze here: https://ffm.bio/squeezeofficial Visit the Official Website and Store: https://www.squeezeofficial.com Subscribe to the official Squeeze channe
From playlist Music from - Classic Rock London Walking Tour
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Photoshop
An intro to the core protocols of the Internet, including IPv4, TCP, UDP, and HTTP. Part of a larger series teaching programming. See codeschool.org
From playlist The Internet
What is a Bézier curve? Programmers use them everyday for graphic design, animation timing, SVG, and more. #shorts #animation #programming Animated Bézier https://www.jasondavies.com/animated-bezier/
From playlist CS101
In Lecture 9 we discuss some common architectures for convolutional neural networks. We discuss architectures which performed well in the ImageNet challenges, including AlexNet, VGGNet, GoogLeNet, and ResNet, as well as other interesting models. Keywords: AlexNet, VGGNet, GoogLeNet, ResNe
From playlist Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)