Intermediate


	"Generative Sensing: Reliable Recognition from Unreliable Sensor Data," a Presentation from Arizona State University Professor Lina Karam of Arizona State University delivers a Technical Insights presentation at the May 2018 Embedded Vision Summit.
	"The OpenVX Computer Vision and Neural Network Inference Library Standard for Portable, Efficient Code," a Presentation from AMD Radhakrishna Giduthuri of AMD delivers a Technical Insights presentation at the May 2018 Embedded Vision Summit.
	"Deploying CNN-based Vision Solutions on a $3 Microcontroller," a Presentation from Au-Zone Technologies Greg Lytle of Au-Zone Technologies delivers a Technical Insights presentation at the May 2018 Embedded Vision Summit.
	"Deep Understanding of Shopper Behaviors and Interactions Using Computer Vision," a Presentation from the Università Politecnica delle Marche Emanuele Frontoni and Rocco Pietrini deliver a Technical Insights presentation at the May 2018 Embedded Vision Summit.
	"Programming Techniques for Implementing Inference Software Efficiently," a Presentation from Codeplay Software Andrew Richards of Codeplay Software delivers a Technical Insights presentation at the May 2018 Embedded Vision Summit.
	"Introduction to Creating a Vision Solution in the Cloud," a Presentation from GumGum Nishita Sant of GumGum delivers a Fundamentals presentation at the May 2018 Embedded Vision Summit.
	"Solving Vision Tasks Using Deep Learning: An Introduction," a Presentation from Google Pete Warden of Google delivers a Fundamentals presentation at the May 2018 Embedded Vision Summit.
	"The Caffe2 Framework for Mobile and Embedded Deep Learning," a Presentation from Facebook Fei Sun of Facebook delivers a presentation at the March 2018 Vision Industry and Technology Forum.
	Data Sets for Machine Learning Model Training For machine learning to deliver its potential, it requires sufficient high quality training data, plus developer knowledge of how to use it.
	Implementing Vision with Deep Learning in Resource-constrained Designs This article reviews deep learning implementation options such as heterogeneous processing, network quantization, and software optimizations

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Intermediate

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