Deep learning lecture
(tensor flow based..)
part 1.
1. logistic classification
2. stochastic optimization
3. general data practices to train models( data & parameter tuning)
part 2. (we're going to go deeper)
1. Deep networks
2. Regularization (to train even bigger models)
part 3. ( will be a deep dive into image and convolutional models)
1. convolutional networks
part 4. (all about text and sequence in general)
1. embeddings
2. recurrent models
2/22/2016
deep learning study (introduction) #1
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* Introduction - The solution shows panorama image from multi images. The panorama images is processing by real-time stitching algorithm...
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As you can see in the following video, I created a class that stitching n cameras in real time. https://www.youtube.com/user/feelmare/sear...
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Image size of origin is 320*240. Processing time is 30.96 second took. The result of stitching The resul...
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In past, I wrote an articel about YUV 444, 422, 411 introduction and yuv <-> rgb converting example code. refer to this page -> ht...
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refer to code: - x = 0.003 formatted_x = " {:.1e} " . format ( x ) print ( formatted_x ) # Output will be "3.0e-03"...
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Logistic Classifier The logistic classifier is similar to equation of the plane. W is weight vector, X is input vector and y is output...
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The latent SVM tells the learning method used in this paper -> "Discriminatively trained deformable part models". The authors s...
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The code need to install two YouTube downloader package. Those are pytube, youtube_dl. This code try to use one of them because sometime it&...
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fig 1. Left: set 4 points (Left Top, Right Top, Right Bottom, Left Bottom), right:warped image to (0,0) (300,0), (300,300), (0,300) Fi...
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