Best Deep Learning Post from LinkedIn Group Datasets for Deep Learning (Slide share) http://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv Deep Learning for Video Analysis – part 1 (DeepStream SDK NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)) https://www.linkedin.com/lite/external-redirect?url=http%3A%2F%2Fwww%2Eslideshare%2Enet%2Fpirahansiah%2Fdeep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie&urlHash=F2e8 How to install Digits 5.1 on Ubuntu 14 https://www.linkedin.com/redir/redirect?url=http%3A%2F%2Fwww%2Eslideshare%2Enet%2Fpirahansiah%2Fhow-to-install-digits-51-on-ubuntu-14&urlhash=8V-j&_t=tracking_anet example of using deep learning in opencv 3.1 http://docs.opencv.org/3.1.0/d5/de7/tutorial_dnn_googlenet.html Layers in Deep Learning & Caffe layers (model architecture ) https://www.linkedin.com/lite/external-redirect?url=http%3A%2F%2Fwww%2Eslideshare%2Enet%2Fpirahansiah%2Flayers-in-deep-learningcaffe-layers-model-architecture&urlHash=BsHf Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit, https://www.linkedin.com/lite/external-redirect?url=http%3A%2F%2Fwww%2Eslideshare%2Enet%2Fpirahansiah%2Finstall-compile-setup-setting-opencv-32-visual-c-2015-win-64bit&urlHash=dC7R Autonomous driving vehicles (Computer Vision, Deep Learning) https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1?trk=mp-author-card Compile OpenCV 3.2 with Visual Studio 2017 (C++) on Windows 10 x64 bit and test with TensorFlow https://www.youtube.com/watch?v=mdeP8SdvSJw TensorFlow in OpenCV 3.2 Visual Studio 2017 (C++ deep learning) application https://www.youtube.com/watch?v=QK1kLTfS97c

1. Best of Deep Learning post
2017
https://www.linkedin.com/groups/10320678

2. 1
• Datasets for Deep Learning (Slide share)
• http://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv
• Deep Learning for Video Analysis – part 1 (DeepStream SDK NVIDIA
TensorRT || NVIDIA GPU Inference Engine (GIE))
• https://www.linkedin.com/lite/external-
redirect?url=http%3A%2F%2Fwww%2Eslideshare%2Enet%2Fpirahansiah%2Fdeep-
learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-
inference-engine-gie&urlHash=F2e8
• How to install Digits 5.1 on Ubuntu 14
• https://www.linkedin.com/redir/redirect?url=http%3A%2F%2Fwww%2Eslideshare%
2Enet%2Fpirahansiah%2Fhow-to-install-digits-51-on-ubuntu-14&urlhash=8V-
j&_t=tracking_anet
• example of using deep learning in opencv 3.1
• http://docs.opencv.org/3.1.0/d5/de7/tutorial_dnn_googlenet.html

3. 2
• Layers in Deep Learning & Caffe layers (model architecture )
• https://www.linkedin.com/lite/external-
redirect?url=http%3A%2F%2Fwww%2Eslideshare%2Enet%2Fpirahansiah%2Fl
ayers-in-deep-learningcaffe-layers-model-architecture&urlHash=BsHf
• Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win
64bit,
• https://www.linkedin.com/lite/external-
redirect?url=http%3A%2F%2Fwww%2Eslideshare%2Enet%2Fpirahansiah%2Fi
nstall-compile-setup-setting-opencv-32-visual-c-2015-win-
64bit&urlHash=dC7R

4. 3
• Autonomous driving vehicles (Computer Vision, Deep Learning)
• https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-
vision-deep-farshid-pirahansiah-1?trk=mp-author-card
• Compile OpenCV 3.2 with Visual Studio 2017 (C++) on Windows 10
x64 bit and test with TensorFlow
• https://www.youtube.com/watch?v=mdeP8SdvSJw
• TensorFlow in OpenCV 3.2 Visual Studio 2017 (C++ deep learning)
application
• https://www.youtube.com/watch?v=QK1kLTfS97c

5. 4
• Deep learning is a fast-changing field at the intersection of computer
science and mathematics. It is a relatively new branch of a wider field
called machine learning.
• http://yerevann.com/a-guide-to-deep-learning/
• The Mathematics of Machine Learning
• https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fwww%2Elinke
din%2Ecom%2Fpulse%2Fmathematics-machine-learning-wale-
akinfaderin&urlhash=5Za3&_t=tracking_anet
• Teaching Material Prof. Dr. Laurenz Wiskott
• https://www.ini.rub.de/PEOPLE/wiskott/Teaching/Material/index.html

6. 5
• semanticscholar.org
• https://www.semanticscholar.org/paper/Peak-Signal-to-noise-Ratio-Based-
on-Threshold-
Pirahansiah/9bcef39992f2d336be0ca8dac0dafb9251dfe07d?citingPapersSort
=is-
influential&citingPapersLimit=10&citingPapersOffset=0&citedPapersSort=is-
influential&citedPapersLimit=10&citedPapersOffset=10
• Thousands of people from the TensorFlow community participated in
the first flagship event. Watch the keynote and talks.
• https://events.withgoogle.com/tensorflow-dev-summit/watch-the-
videos/#content

7. 6
• Getting Started with Deep Learning comparison
• http://www.svds.com/getting-started-deep-learning/
• More than 135 publications from DeepMind
• https://deepmind.com/research/publications/
• Before you start coding
• http://skillprogramming.com/images/pictuers/before_you_start_coding.png
• A curated list of the most cited deep learning papers (since 2012)
• https://github.com/terryum/awesome-deep-learning-papers

8. 7
• Deep Learning with Tensorflow – YouTube
• https://www.youtube.com/playlist?list=PL-
XeOa5hMEYxNzHM7YLRjIwE1k3VQpqEh

9. 8
• Deep Learning Machine (DevBox)
Hardware:
CPU: Intel® Core™ i7-5930K Processor (15M Cache, up to 3.70 GHz)
RAM: 64 GB DDR4
GPU: 4 * Geforce GTX 1080 (8 GB)= 32 GB
SSD: 2 * 256 GB
HDD: 2 * 4 TB
MAINBOARD: ASUS X99-E WS/USB 3.1
Software:
Ubuntu 14.04
CUDA 8.0
cuDNN 5.1
Nvida Driver 367.44
Caffe: 0.15.13
Theano:
Torch:
BIDMach: 1.0.3
OpenCV: 2.4.13
Google Tensor Flow: 0.10.0
Nvida DIGITS 4.0

10. 9
• CS294-112 Deep Reinforcement Learning Sp17 – YouTube
• https://www.youtube.com/playlist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX
• NASA's Software Catalog
• https://software.nasa.gov/
• Event Information: Object Recognition: Deep Learning and Machine
Learning for Computer Vision
• https://mathworksevents.webex.com/mw3000/mywebex/default.do?nomenu=true
&siteurl=mathworksevents&service=6&rnd=0.14426561817638295&main_url=https
%3A%2F%2Fmathworksevents.webex.com%2Fec3000%2Feventcenter%2Fevent%2Fe
ventAction.do%3FtheAction%3Ddetail%26%26%26EMK%3D4832534b000000036f71
07ffe1e8a803780e2e8f536188001d51dbdad3552ddc6c9497be9e3e7b8a%26siteurl
%3Dmathworksevents%26confViewID%3D1759295821%26

11. 10
• NVIDIA's new Jetson module with either twice the performance, or
twice the energy efficiency.
• http://www.nvidia.com/object/embedded-systems-dev-kits-modules.html
• Infographic: A Beginner’s Guide to Machine Learning Algorithms
• http://dataconomy.com/2017/03/beginners-guide-machine-learning/
• Bias-Variance Tradeoff in Machine Learning (Machine Learning versus
Curve Fitting)
• http://www.learnopencv.com/bias-variance-tradeoff-in-machine-learning/

12. 11
• Face in Video Evaluation (FIVE)
• https://www.nist.gov/programs-projects/face-video-evaluation-five
• Object Recognition: Deep Learning and Machine Learning for
Computer Vision
• https://www.mathworks.com/videos/object-recognition--deep-learning-and-
machine-learning-for-compu-1482957345023.html
• Google Trends in Computer Vision,Deep Learning,Machine Learning
• https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Ftrends%2Ego
ogle%2Ecom%2Ftrends%2Fexplore%2FTIMESERIES%3Fq%3Dcomputer%2520
vision%2Cdeep%2520learning%2Cmachine%2520learning%26hl%3Den-
US%26sni%3D4&urlhash=Ro2Y&_t=tracking_anet

13. 12
• Deep Learning Tutorials
• http://deeplearning.net/tutorial/
• Stanford University CS224d: Deep Learning for Natural Language
Processing
• http://cs224d.stanford.edu/syllabus.html
• Deep Learning Book Review – YouTube
• https://www.youtube.com/playlist?list=PLldrX-tcWesNk9_zRmIgPUY_uPqHVTPbS
• 5 book to read for programmer
• 1. refactoring improving the design of existing code
2. working effectively with legacy code
3.people ware: productive projects and teams
4. head first design patterns
5. soft skills the software developer's life manual
• https://www.linkedin.com/groups/10320678

14. 13
• Deep Learning Institute Workshop Malaysia
• https://www.eventbrite.com/e/deep-learning-institute-workshop-malaysia-
tickets-32580880290?utm-medium=discovery&utm-campaign=social&utm-
content=attendeeshare&aff=escb&utm-source=cp&utm-term=listing
• Index of Best AI/Machine Learning Resources
• https://hackernoon.com/index-of-best-ai-machine-learning-resources-
71ba0c73e34d#.adf6ggthh
• Update of deep learning self-driving with five videos, competition
"DeepTraffic 2.0",
• http://selfdrivingcars.mit.edu/
• https://www.linkedin.com/groups/10320678

15. 14
• Deep Learning in Python: Getting Started
• http://www.analyticbridge.com/profiles/blogs/a-complete-guide-on-getting-
started-with-deep-learning-in-python

16. 15
• deep learning videos
• How to set up your AWS deep learning server
https://youtu.be/8rjRfW4JM2I
0—Why deep learning; Intro to convolutions
https://youtu.be/ACU-T9L4_lI
1—Recognizing cats and dogs
https://youtu.be/kzt3-FHdAeM
https://youtu.be/Th_ckFbc6bI
2—Convolutional neural networks
https://youtu.be/e3aM6XTekJc
3—Under fitting and over fitting
https://youtu.be/6kwQEBMandw
4—Collaborative filtering, embedding, and more
https://youtu.be/V2h3IOBDvrA
5—Intro to NLP and RNNs
https://youtu.be/qvRL74L81lg
6—Building RNNs
https://youtu.be/ll9y1U0SoVY
7—Exotic CNN architectures; RNN from scratch
https://youtu.be/Q0z-l2KRYFY

17. 16
• Machine Learning by Ng, Andrew
• https://see.stanford.edu/Course/CS229
• https://see.stanford.edu/materials/aimlcs229/MachineLearningAllMaterials.zip
• A Step by Step Backpropagation Example
• https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
• https://www.linkedin.com/groups/10320678
• Two Minute Papers is a series where the most recent and awesome
scientific works are discussed in a simple and enjoyable way, two minutes
at a time. Give it a try!
• https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg

18. 17
• opencv with python
• https://www.linkedin.com/redir/redirect?url=http%3A%2F%2Fude%2Emy%2F
UC-HQO6HEOV&urlhash=yJAJ&_t=tracking_anet
• Uncertainty in Deep Learning
• http://mlg.eng.cam.ac.uk/yarin/blog_2248.html
• A curated list of deep learning resources for computer vision
• https://github.com/kjw0612/awesome-deep-vision

19. 18
• MXNet is a light framework for deep learning
• https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fgithub%2Eco
m%2Fdmlc%2Fmxnet&urlhash=J_Lw&_t=tracking_anet
• BIDMach is a very fast machine learning library
• https://github.com/BIDData/BIDMach
• CNTK version 2.0 Beta 2 (Windows+Linux)
• https://github.com/Microsoft/CNTK/releases

20. 19
• 50+ Data Science and Machine Learning Cheat Sheets
• https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fgithub%2Eco
m%2Fwdv4758h%2Fnotes%2Fblob%2Fmaster%2Fpdf%2Flearning%2FMachin
e%2520Learning%2520Cheat%2520Sheet%2520-
%2520Classical%2520equations%2C%2520diagrams%2520and%2520tricks%2
520in%2520machine%2520learning%2Epdf&urlhash=Sjh_&_t=tracking_anet
• New version of the NVIDIA Deep Learning GPU Training System
(DIGITS) ( version 5)
• https://github.com/NVIDIA/DIGITS/archive/v5.1-dev.zip

21. 20
• How to install OpenCV with python virtual environment.
• https://www.linkedin.com/pulse/installing-opencv-python-virtual-
environment-mac-os-srivastava?trk=prof-post
• OpenCV 3.2
• http://opencv.org/opencv-3-2.html
• Deep Learning course video in waterloo
• https://uwaterloo.ca/data-science/deep-learning
• Practical Deep Learning For Coders
• http://course.fast.ai/

22. 21
• MatConvNet is a MATLAB toolbox implementing Convolutional Neural
Networks (CNNs) for computer vision applications
• http://www.vlfeat.org/matconvnet/
• A CONVOLUTIONAL ENCODER MODEL FOR NEURAL MACHINE
TRANSLATION
• https://arxiv.org/pdf/1611.02344v1.pdf
• compare different solver type in deep learning (Caffe)
• http://cs.stanford.edu/people/karpathy/convnetjs/demo/trainers.html
• SVM Understanding the math
• http://www.svm-tutorial.com/

23. 22
• Probabilistic Programming & Bayesian Methods
• http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-
Methods-for-Hackers/
• The perceptron algorithm explained with python code
• http://ataspinar.com/2016/12/22/the-perceptron/
• Deep Face Recognition
• http://www.robots.ox.ac.uk/~vgg/software/vgg_face/
• Deep Learning for Computer Vision Barcelona
• http://imatge-upc.github.io/telecombcn-2016-dlcv/
• cheat sheet containing many of ANN architectures
• http://www.asimovinstitute.org/neural-network-zoo/

24. 23
• NIPS 2016 Tutorial: Generative Adversarial Networks. Ian Goodfellow.
OpenAI
• https://arxiv.org/pdf/1701.00160v1.pdf
• Caffe Class Diagram
• https://creately.com/diagram/example/irmyfn0l1/Caffe%20Class%20Diagram
• deep learning for building a self-driving car
• http://selfdrivingcars.mit.edu/
• https://www.linkedin.com/groups/10320678
• https://www.linkedin.com/in/pirahansiah/
• cuda cheat sheet
• https://www.cs.ucy.ac.cy/courses/EPL372/Spring2016Files/CUDA_QuickReference.p
df

25. 24
• Collaborative Open Computer Science
• http://www.gitxiv.com/?query=adversarial%20networks&view=top
• Models and Accuracies
• https://cmusatyalab.github.io/openface/models-and-accuracies/
• Tutorial C++
• https://www.tutorialspoint.com/cplusplus/
• Head Pose Estimation using OpenCV and Dlib
• http://www.learnopencv.com/head-pose-estimation-using-opencv-and-dlib/
• Dlib is a modern C++ toolkit containing machine learning algorithms and
tools
• http://openface-api.readthedocs.io/en/latest/_images/dlib-landmark-mean.png
https://github.com/davisking/dlib
• https://www.linkedin.com/in/pirahansiah/

26. 25
• Metrics To Evaluate Machine Learning Algorithms
• http://cs231n.github.io/neural-networks-3/
• Tensors and Dynamic neural networks in Python with strong GPU
acceleration
• http://pytorch.org/

27. Reference
• https://www.linkedin.com/groups/10320678
• https://www.linkedin.com/in/pirahansiah/
• http://www.pirahansiah.com/
• http://www.tiziran.com/