deep learning study (introduction) #1

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


Popular posts from this blog

(OpenCV Study) Background subtractor MOG, MOG2, GMG example source code (BackgroundSubtractorMOG, BackgroundSubtractorMOG2, BackgroundSubtractorGMG)

OpenCV Stitching example (Stitcher class, Panorama)

Example source code of extract HOG feature from images, save descriptor values to xml file, using opencv (using HOGDescriptor )

Real-time N camera stitching Class.

Optical Flow sample source code using OpenCV

OpenCV Drawing Example, (line, circle, rectangle, ellipse, polyline, fillConvexPoly, putText, drawContours)

Video Stabilization example source code, (using cvFindHomography, cvWarpPerspective functions in openCV)

SICK LMS511 sensor data acquisition interface (source code, C++/MFC)

8 point algorithm (Matlab source code) / The method to get the Fundamental Matrix and the Essential matrix

Image warping (using opencv findHomography, warpPerspective)