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culurciello.github.io

Eugenio Culurciello's blog

My views on tech; views that are ideas and comments and like code constantly change and evolve. These views are my own, but feel free to comment and improve them.

http://culurciello.github.io/

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Eugenio Culurciello's blog | culurciello.github.io Reviews

https://culurciello.github.io

My views on tech; views that are ideas and comments and like code constantly change and evolve. These views are my own, but feel free to comment and improve them.

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1

Navigating the unsupervised learning landscape

https://culurciello.github.io/tech/2016/06/10/unsup.html

Navigating the unsupervised learning landscape. Jun 10, 2016. Note: updated on 2016-10-24. Unsupervised learning is the Holy Grail of Deep Learning. The goal of unsupervised learning is to create general systems that can be trained with little data. Very little data. Now, think about how you got trained when you were very little. Yes you got some supervision, but when your parents told you that is a cat they would not tell you cat every split second you were looking at a cat for the rest of your life!

2

Comparing image recognition engines

https://culurciello.github.io/tech/2015/05/17/image-rec.html

Comparing image recognition engines. May 17, 2015. Yesterday a Venture Beat article. Tested a few public image recognition engines on a popular large ImageNet. In order to provide consistent tests, they used images from the ImageNet categories. Only This is because many research and evaluation image recognition out there are trained on the ImageNet. Does not contain a lot of the categories of object we use and see in our everyday life, such as person , man , woman , hand , etc. Here is how Metamind.

3

Training ENet on ImageNet

https://culurciello.github.io/tech/2016/06/20/training-enet.html

Training ENet on ImageNet. Jun 20, 2016. Training a novel network on the ImageNet dataset. Can be tricky. Here are some guidelines to make your model train faster and help you design better models. For a more in-depth analysis and comparison of all the networks record-winners on ImageNet, please see our recent article. One representative figure from this article is here:. This work is based on the model in our recent paper ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

4

Video summarization: why and how?

https://culurciello.github.io/tech/2016/07/09/video-comprehension.html

Video summarization: why and how? Jul 9, 2016. It has been a long time, but the dream is always the same: can I get my computer to watch a video and summarize it for me? The answer right now is: no. Video or story analysis requires to extract features at key-point frames . These features encode the meaning of the scene. Multiple scenes make up a temporal sequence of events, or action performed in the portion of video or story. Please note that frames are artificially created in a video by sampling at fix...

5

Neural Network Architectures

https://culurciello.github.io/tech/2016/06/04/nets.html

Jun 4, 2016. Note: updated on 2016-10-25. Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture. I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning. For a more in-depth analysis and comparison of all the networks reported here, please see our recent article. One representative figure from this article is here:. In 2010 Dan Claudiu C...

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Eugenio Culurciello's blog

Jul 9, 2016. Video summarization: why and how? Jun 20, 2016. Training ENet on ImageNet. Jun 10, 2016. Navigating the unsupervised learning landscape. Jun 8, 2016. How close are Deep Neural Networks to human abilities and performance? Jun 4, 2016. May 17, 2015. Comparing image recognition engines. My views on tech; views that are ideas and comments and like code constantly change and evolve. These views are my own, but feel free to comment and improve them.

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