This is the curriculum for "Learn Computer Vision" by Siraj Raval on Youtube

This is the Curriculum for this video on Learn Natural Language Processing by Siraj Raval on Youtube. After completing this course, start your own startup, do consulting work, or find a full-time job related to NLP. Remember to believe in your ability to learn. You can learn NLP , you will learn NLP, and if you stick to it, eventually you will master it.

Join the #NLP_curriculum channel in our Slack channel to find one http://wizards.herokuapp.com

• Video Lectures
• Reading Assignments
• Project(s)

• 8 weeks
• 2-3 Hours of Study per Day

• Python, OpenCV, Tensorflow

• Learn Python https://www.edx.org/course/introduction-python-data-science-2
• Statistics http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf
• Probability https://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf
• Calculus http://tutorial.math.lamar.edu/pdf/Calculus_Cheat_Sheet_All.pdf
• Linear Algebra https://www.souravsengupta.com/cds2016/lectures/Savov_Notes.pdf

• Luminance (Brightness, contrast, gamma, histogram equalization)
• Linear Filtering (enhance image - blur & sharpen, edge detect, image countours, convolution)
• Non Linear Filtering (Median, Bilateral Filter, morphology )
• Color processing (B&W, Saturation, White Balance)
• Dithering (Quantization, Ordered Dither, Floyd-Steinberg)
• Blending (Image pyramids)
• Texture Analysis
• Template Matching (find object in an image)

https://www.youtube.com/watch?v=-nt80JUNwlw&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=2 videos 1-5

http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf Sec 3.1.1-2, 3.2 Sec 3.2.3, 4.2 3.3.2-4

• Motion and optical flow Analysis

https://www.udacity.com/course/introduction-to-computer-vision--ud810 Udacity lesson 6 https://www.youtube.com/watch?v=-nt80JUNwlw&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=2 video 8 https://www.youtube.com/watch?v=wC8hXuHsHAQ&list=PLvqB6_mDBCdlnT84LK_NvbOqcXLlOTR8j&index=6&t=0s

http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf Sec 10.5 Sec 8.4 (up until 8.4.1)

• Segmentation and clustering algorithms like watershed, grabcut
• Interactive segmentation
• Hough transform (detect circles, lines)
• Foreground Extraction

https://www.youtube.com/watch?v=ZF-3aORwEc0 https://www.youtube.com/watch?v=3qJej6wgezA

Sec Sec 5.2-5.4 http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf

• Fitting lines and curves
• Robust fitting, RANSAC
• Deformable contours

Videos 6-7 https://www.youtube.com/watch?v=-nt80JUNwlw&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=2

Sec 4.3.2 5.1.1 http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf

• Local invariant feature detection and description
• Image transformations and alignment
• Planar homography
• Epipolar geometry and stereo
• Object instance recognition

• https://www.youtube.com/playlist?list=PLyH-5mHPFffFvCCZcbdWXAb_cTy4ZG3Dj

• http://vision.cs.utexas.edu/376-spring2018/#Tues_May_1 Sections in book

• Stereo Vision, Dense Motion and Tracking;. 3d Objects
• 3D Scene understanding
• 3D Segmentation and Modeling

• https://www.youtube.com/watch?v=-nt80JUNwlw&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=2 video 9
• all videos https://www.coursera.org/learn/stereovision-motion-tracking

1. N. Dalal, Histograms of oriented gradients for human detection

2. G. Csurka et al. (Bag of Visual Words - a brilliant representation of cross field research) Visual categorization with bags of keypoints

3. S Lazebnik, C Schmid, J Ponce, Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories

4. Jegou et al. Aggregating local image descriptors into compact codes.

• Object/scene/activity categorization (semantic segmentation)
• Object detection (Non max suppression , sliding windows, Boundary boxes and anchors, counting)
• YOLO and Darknet, region proposal networks
• Supervised classification algorithms
• Probabilistic models for sequence data
• Visual attributes
• Optical Character Recognition
• Facial Detection

• https://www.youtube.com/watch?v=a-v5_8VGV0A&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=8 10-18
• my video on YOLO

rest of the readings first half recognition http://vision.cs.utexas.edu/376-spring2018/#Tues_May_1

• Active learning
• Dimensionality reduction
• Non-parametric methods and big data
• U-Net
• Tranfer learning
• Avoiding Overfitting
• GANs

• videos 19-20 https://www.youtube.com/watch?v=a-v5_8VGV0A&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=8
• my video on transfer learning
• Lectures 1-16 Stanford https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv

• next half recogniton http://vision.cs.utexas.edu/376-spring2018/#Tues_May_1