Best of Deep Learning post 2017

https://www.linkedin.com/groups/10320678

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%2Eslid

eshare%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

2• 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

3• Autonomous driving vehicles (Computer Vision, Deep Learning)• https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-visi

on-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

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

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/#co

ntent

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

7• Deep Learning with Tensorflow – YouTube• https://www.youtube.com/playlist?list=PL-XeOa5hMEYxNzHM7YLRjIwE1k3VQ

pqEh

8• Deep Learning Machine (DevBox)

Hardware: CPU: Intel® Core™ i7-5930K Processor (15M Cache, up to 3.70 GHz) RAM: 64 GB DDR4GPU: 4 * Geforce GTX 1080 (8 GB)= 32 GB SSD: 2 * 256 GBHDD: 2 * 4 TBMAINBOARD: ASUS X99-E WS/USB 3.1Software: Ubuntu 14.04CUDA 8.0cuDNN 5.1Nvida Driver 367.44Caffe: 0.15.13Theano: Torch: BIDMach: 1.0.3OpenCV: 2.4.13Google Tensor Flow: 0.10.0Nvida DIGITS 4.0

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?nomen

u=true&siteurl=mathworksevents&service=6&rnd=0.14426561817638295&main_url=https%3A%2F%2Fmathworksevents.webex.com%2Fec3000%2Feventcenter%2Fevent%2FeventAction.do%3FtheAction%3Ddetail%26%26%26EMK%3D4832534b000000036f7107ffe1e8a803780e2e8f536188001d51dbdad3552ddc6c9497be9e3e7b8a%26siteurl%3Dmathworksevents%26confViewID%3D1759295821%26

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/

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%2Egoo

gle%2Ecom%2Ftrends%2Fexplore%2FTIMESERIES%3Fq%3Dcomputer%2520vision%2Cdeep%2520learning%2Cmachine%2520learning%26hl%3Den-US%26sni%3D4&urlhash=Ro2Y&_t=tracking_anet

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 code2. working effectively with legacy code3.people ware: productive projects and teams4. head first design patterns5. soft skills the software developer's life manual

• https://www.linkedin.com/groups/10320678

13• Deep Learning Institute Workshop Malaysia• https://www.eventbrite.com/e/deep-learning-institute-workshop-malaysia-tic

kets-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-71ba0c

73e34d#.adf6ggthh

• Update of deep learning self-driving with five videos, competition "DeepTraffic 2.0", • http://selfdrivingcars.mit.edu/ • https://www.linkedin.com/groups/10320678

14• Deep Learning in Python: Getting Started• http://www.analyticbridge.com/profiles/blogs/a-complete-guide-on-getting-s

tarted-with-deep-learning-in-python

15• deep learning videos • How to set up your AWS deep learning server

https://youtu.be/8rjRfW4JM2I 0—Why deep learning; Intro to convolutionshttps://youtu.be/ACU-T9L4_lI1—Recognizing cats and dogshttps://youtu.be/kzt3-FHdAeMhttps://youtu.be/Th_ckFbc6bI2—Convolutional neural networkshttps://youtu.be/e3aM6XTekJc3—Under fitting and over fittinghttps://youtu.be/6kwQEBMandw4—Collaborative filtering, embedding, and morehttps://youtu.be/V2h3IOBDvrA5—Intro to NLP and RNNshttps://youtu.be/qvRL74L81lg6—Building RNNshttps://youtu.be/ll9y1U0SoVY7—Exotic CNN architectures; RNN from scratchhttps://youtu.be/Q0z-l2KRYFY

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

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

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

19• 50+ Data Science and Machine Learning Cheat Sheets• https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fgithub

%2Ecom%2Fwdv4758h%2Fnotes%2Fblob%2Fmaster%2Fpdf%2Flearning%2FMachine%2520Learning%2520Cheat%2520Sheet%2520-%2520Classical%2520equations%2C%2520diagrams%2520and%2520tricks%2520in%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

20• How to install OpenCV with python virtual environment.• https://www.linkedin.com/pulse/installing-opencv-python-virtual-environme

nt-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/

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/

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/

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.pdf

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/

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/

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