updates

Artificial_Intelligence.md

@@ -24,6 +24,9 @@
 * Edge AI for techies, updated December 11, 2019 https://www.imagimob.com/blog/edge-ai-for-techies
 * Why is edge AI important? https://www.experfy.com/blog/why-is-edge-ai-important
 * Why video games and board games aren’t a good measure of AI intelligence https://www.theverge.com/2019/12/19/21029605/artificial-intelligence-ai-progress-measurement-benchmarks-interview-francois-chollet-google
+* Artificial intelligence predictions for 2020 https://www.infoworld.com/article/3509465/artificial-intelligence-predictions-for-2020.html
+* What just happened in the world of AI? https://www.kdnuggets.com/2019/12/review-what-happened-ai.html
+* AI Augmentation: The Real Future of Artificial Intelligence https://www.forbes.com/sites/cognitiveworld/2019/09/30/ai-augmentation-the-real-future-of-artificial-intelligence/
 
 ## Books/Papers
 * Hands-On Artificial Intelligence for IoT https://www.packtpub.com/big-data-and-business-intelligence/hands-artificial-intelligence-iot

Computer_Vision.md

@@ -19,6 +19,8 @@
 * The Most Intuitive and Easiest Guide for Convolutional Neural Network https://towardsdatascience.com/the-most-intuitive-and-easiest-guide-for-convolutional-neural-network-3607be47480
 * Computer Vision by Andrew Ng — 11 Lessons Learned **Medium** https://link.medium.com/42kNdcdgB2
 * Machine vision that sees things more the way we do is easier for us to understand https://www.technologyreview.com/f/614870/ai-machine-vision-interpretable/
+* Introduction to computer vision https://developer.ibm.com/articles/introduction-computer-vision/
+* How to do everything in Computer Vision **Medium** https://link.medium.com/OQWSqa5sQ2
 
 ## Books/Papers
 * Computer Vision Foundations and Applications  http://vision.stanford.edu/teaching/cs131_fall1718/files/cs131-class-notes.pdf
@@ -37,6 +39,9 @@
 * Pro Processing for Images and Computer Vision with OpenCV http://www.allitebooks.in/pro-processing-images-computer-vision-opencv/
 * 10 CUTTING-EDGE RESEARCH PAPERS IN COMPUTER VISION FROM 2019 https://www.topbots.com/top-ai-vision-research-papers-2019/?utm_campaign=Artificial%2BIntelligence%2BWeekly&utm_medium=web&utm_source=Artificial_Intelligence_Weekly_136
 * Table of Contents – Raspberry Pi for Computer Vision https://www.pyimagesearch.com/2019/04/05/table-of-contents-raspberry-pi-for-computer-vision/
+* [OpenCV CheatSheet](books/opencv_cheatsheet.pdf)
+* Mastering OpenCV4 with Python http://148.228.16.36/CURSOS/IMAGENES/LIBROS/3-Mastering-Opencv4.pdf
+* [opencv-python-tutroals](books/opencv-python-tutroals.pdf)
 
 ## Course/tutorials
 * OpenCV Tutorial: A Guide to Learn OpenCV https://www.pyimagesearch.com/2018/07/19/opencv-tutorial-a-guide-to-learn-opencv/
@@ -54,6 +59,8 @@
 * OpenCV with Python Intro and loading Images tutorial https://pythonprogramming.net/loading-images-python-opencv-tutorial/
 * Become a Computer Vision Expert **by Udacity** https://www.udacity.com/course/computer-vision-nanodegree--nd891
 * How To Train an Object Detection Classifier for Multiple Objects Using TensorFlow (GPU) on Windows 10 https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10
+* Computer Vision and Computer Graphics https://www.tutorialspoint.com/dip/computer_vision_and_graphics.htm
+* Python For Computer vision with OpenCv https://sites.google.com/view/resourcespoint/home
 
 ## Links/URLs
 * Open Source Computer Vision https://docs.opencv.org/4.1.2/
@@ -69,6 +76,10 @@
 * Computer Vision for tracking https://towardsdatascience.com/computer-vision-for-tracking-8220759eee85
 * semantic segmentation https://thegradient.pub/semantic-segmentation/
 * Data Visualization Tutorial https://lnkd.in/fYUCzgC
+* Visual Intelligence Made Easy https://www.customvision.ai/
+* Computer Vision and Visual SLAM vs. AI Agents http://www.computervisionblog.com/2019/11/computer-vision-and-visual-slam-vs-ai.html?m=1
+* Running Deep Learning models in OpenCV https://cv-tricks.com/how-to/running-deep-learning-models-in-opencv/
+    - repo https://github.com/legolas123/cv-tricks.com/tree/master/OpenCV/Running_YOLO
 
 ## Videos
 * How Computer Vision Works https://www.youtube.com/watch?v=OcycT1Jwsns

Data_Science.md

@@ -11,6 +11,12 @@
 
 * resources collection https://github.com/frontbench-open-source/Data-Science-Free
 * [The_Friendly_Data_Science_Handbook](books/The_Friendly_Data_Science_Handbook.pdf)
+* Learning Path: Your Data Science Journey https://www.superdatascience.com/paths
+
+* [Data_Science_Cheatsheet](books/Data_Science_Cheatsheet.pdf)
+
+* Becoming a Self-Taught Data Scientist **Medium** https://towardsdatascience.com/becoming-a-self-taught-data-scientist-5563f546bb7b
+
 
 ### Here's an ultimate data science starter kit:
 1. Foundational Skills

Deep_Learning.md

@@ -25,6 +25,9 @@
 * What’s the Difference Between Deep Learning Training and Inference? https://blogs.nvidia.com/blog/2016/08/22/difference-deep-learning-training-inference-ai/
 * Top 5 Deep Learning Frameworks, their Applications, and Comparisons https://www.analyticsvidhya.com/blog/2019/03/deep-learning-frameworks-comparison/
 * 10 Deep Learning Best Practices to Keep in Mind in 2020 https://nanonets.com/blog/10-best-practices-deep-learning/
+* Meet DiffGrad: New Deep Learning Optimizer that solves Adam’s ‘overshoot’ issue **Medium** https://medium.com/@lessw/meet-diffgrad-new-deep-learning-optimizer-that-solves-adams-overshoot-issue-ec63e28e01b2
+* A 'Brief' History of Neural Nets and Deep Learning http://www.andreykurenkov.com/writing/ai/a-brief-history-of-neural-nets-and-deep-learning/
+* 2019 In-Review and Trends for 2020 – A Technical Overview of Machine Learning and Deep Learning! https://www.analyticsvidhya.com/blog/2019/12/2020-trends-machine-learning-deep-learning/
 
 ## Books/Papers
 * Paper notes in deep learning/machine learning and computer vision https://github.com/Hsuxu/Paper-Notes
@@ -58,6 +61,9 @@
 * Introduction to Deep Learning http://introtodeeplearning.com/#overview
     - http://introtodeeplearning.com/2019/index.html
 * Deep Learning for coders http://course18.fast.ai/index.html
+* Deep Learning and Computer Vision: From Basic Implementation to Efficient Methods **Medium** https://link.medium.com/r5PxHDkDM2
+* NVIDIA DEEP LEARNING INSTITUTE https://www.nvidia.com/en-us/deep-learning-ai/education/
+* Deep Learning - Cognitive Class https://cognitiveclass.ai/learn/deep-learning
 
 ## Links/URLs
 * Deep Learning Glossary by Nvidia [Deep Learning Glossary by Nvidia](books/Deep_Learning_Glossary_by_Nvidia.pdf)
@@ -75,7 +81,7 @@
 * Understanding object detection in deep learning https://blogs.sas.com/content/subconsciousmusings/2018/11/19/understanding-object-detection-in-deep-learning/
 * Deep Reinforcement Learning http://rail.eecs.berkeley.edu/deeprlcourse/?fbclid=IwAR1POtXDvgU1d8TGPwroaSaGLCuUvjnFaD_dgZ6g6xMryMSwy8DBHJ7eOmY
 * Review of Deep Learning Algorithms for Image Semantic Segmentation **Medium** https://medium.com/@arthur_ouaknine/review-of-deep-learning-algorithms-for-image-semantic-segmentation-509a600f7b57
-
+* Cerebras Wafer Scale Engine: Why we need big chips for Deep Learning https://www.cerebras.net/cerebras-wafer-scale-engine-why-we-need-big-chips-for-deep-learning/
 
 
 ## Videos
@@ -88,3 +94,4 @@
 * Machine Learning & Deep Learning Specialization with Python, Scikit-Learn and TensforFlow **Playlist** https://www.youtube.com/playlist?list=PLFhNzVKP1pVrNU8cTL_t-8YzPLF8i8PaS
 * Heroes of Deep Learning: Andrew Ng interviews Geoffrey Hinton https://www.youtube.com/watch?v=-eyhCTvrEtE&feature=youtu.be
 * Yoshua Bengio | From System 1 Deep Learning to System 2 Deep Learning | NeurIPS 2019 https://www.youtube.com/watch?v=FtUbMG3rlFs&feature=youtu.be
+* Deep Learning Lectures **Playlist** https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf

Machine_Learning.md

@@ -24,6 +24,10 @@
 * Bias in Machine Learning: How Facial Recognition Models Show Signs of Racism, Sexism and Ageism **Medium** https://towardsdatascience.com/bias-in-machine-learning-how-facial-recognition-models-show-signs-of-racism-sexism-and-ageism-32549e2c972d
 * Why Data Normalization is necessary for Machine Learning models **Medium** https://medium.com/@urvashilluniya/why-data-normalization-is-necessary-for-machine-learning-models-681b65a05029
 * Finally, machine learning interprets gene regulation clearly https://m.phys.org/news/2019-12-machine-gene.html
+* Everything you need to know about Neural Networks and Backpropagation **Medium** https://towardsdatascience.com/everything-you-need-to-know-about-neural-networks-and-backpropagation-machine-learning-made-easy-e5285bc2be3a
+* Software Developers: You’re Learning Machine Learning Upside Down **Medium** https://towardsdatascience.com/software-developers-youre-learning-machine-learning-upside-down-3867dc140862
+* The Machine Learning Behind Google’s New Recorder App https://analyticsindiamag.com/machine-learning-google-recorder-pixel-mobile-ai/
+* PhD or programming? Fast paths into aligning AI as a machine learning engineer https://80000hours.org/podcast/episodes/olsson-and-ziegler-ml-engineering-and-safety/
 
 ## Books/Papers
 * Papers with code https://paperswithcode.com/
@@ -43,6 +47,8 @@
 * Hands-On Machine Learning with Scikit-Learn and TensorFlow https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/
 * Latest papers on machine learning https://arxiv.org/list/cs.LG/recent
 * Monetizing Machine Learning http://www.allitebooks.in/monetizing-machine-learning/
+* Humble Book Bundle: Python & Machine Learning by Packt https://www.humblebundle.com/books/python-machine-learning-packt-books
+[McGrawHill - Machine Learning - Tom Mitchell](books/McGrawHill-Machine-Learning-Tom Mitchell.pdf)
 
 
 ## Course/tutorials
@@ -57,6 +63,7 @@
 * Open Machine Learning Course **Medium** https://link.medium.com/FOJo11mbQX
 * Applied Machine Learning Course https://www.appliedaicourse.com/
 * Introduction to Machine Learning Course **by Udacity** https://www.udacity.com/course/intro-to-machine-learning--ud120
+* Machine Learning A-Z (Python & R in Data Science Course) https://www.udemy.com/course/machinelearning/
 
 ## Links/URLs
 * Machine Learning From Scratch https://github.com/eriklindernoren/ML-From-Scratch
@@ -85,6 +92,8 @@
 * Machine Learning for Problem Solving https://lnkd.in/f5aUbBM
 * Machine learning edge devices: benchmark report https://tryolabs.com/blog/machine-learning-on-edge-devices-benchmark-report/
 * Core ML Models https://developer.apple.com/machine-learning/models/
+* Foundational Skills https://end-to-end-machine-learning.teachable.com/p/000-foundational-skills
+* Machine Learning Repository https://archive.ics.uci.edu/ml/index.php
 
 ## Videos
 * Machine Learning - CS50 Podcast, Ep. 6 https://www.youtube.com/watch?v=mEdIQbOL8dY

Math.md

@@ -12,12 +12,14 @@
 ## Articles
 * Demystifying the math and implementation of Convolutions: Part I. https://praisethemoon.org/demystifying-the-math-and-implementation-of-convolutions-part-i/
 * Mathematics for Data Science https://towardsdatascience.com/mathematics-for-data-science-e53939ee8306?gi=9af3c9aa4682
+* Understanding the Mathematics behind Gradient Descent **Medium** https://link.medium.com/BBI6A2XpM2
 
 ## Books/Papers
 * Mathematics for machine learning https://mml-book.github.io/book/mml-book.pdf
 * Probability Cheatsheet[Probability Cheatsheet](books/probability_cheatsheet.pdf)
 * Statistics Cheatsheet[Statistics Cheatsheet](books/Statistics_cheat_sheet.pdf)
 * A beginners guide to the mathematics of neural network.[book](books/math_neural_networks.pdf)
+* Introduction to Applied Linear Algebra https://web.stanford.edu/~boyd/vmls/vmls.pdf
 
 ## Course/tutorials
 * Mathematics for Machine Learning Specialization https://www.coursera.org/specializations/mathematics-machine-learning

OpenVINO.md

@@ -33,6 +33,10 @@
 * AI Courses https://software.intel.com/en-us/ai/courses
 * Computer Vision with the Intel® Distribution of OpenVINO™ Toolkit https://software.intel.com/es-es/iot/computer-vision-with-open-vino-toolkit
 * Object recognition with Intel® Distribution of OpenVINO™ toolkit **Medium** https://medium.com/intel-software-innovators/object-recognition-with-intel-distribution-of-openvino-toolkit-475647574fb7
+* [OpenVINO_inferencing code example](books/OpenVINO_inferencing_code_example.pdf)
+* Converting TensorFlow* Object Detection API Models https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html
+* Converting a Caffe* Model https://docs.openvinotoolkit.org/2018_R5/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe.html
+* Converting a ONNX* Model https://docs.openvinotoolkit.org/2018_R5/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX.html
 
 ## Links/URLs
 * OpenVINO toolkit https://software.intel.com/en-us/openvino-toolkit
@@ -74,3 +78,6 @@
 * (37) OpenVINO™ toolkit - RaspberryPI + Movidius NCS  https://www.youtube.com/watch?v=PNmH_ugW6Zw
 * (01) OpenVINO™ toolkit - What is OpenVINO? https://www.youtube.com/watch?v=kY9nZbX1DWM
 * Intel Webinar: Accelerate Deep Learning Inference using OpenVINO Toolkit https://www.youtube.com/watch?v=FOgg9_zCC9g&feature=youtu.be
+* Intel® Distribution of OpenVINO™ Toolkit
+    - [part 1](others/OpenVINO_Toolkit_part1.mp4)
+    - [part 2](others/OpenVINO_Toolkit_part2.mp4)

Others.md

@@ -23,6 +23,12 @@
 * The Benefits and Potential of Edge Computing https://www.vxchnge.com/blog/the-5-best-benefits-of-edge-computing
 * 2020 Predictions For AI, DL, And ML https://www.forbes.com/sites/evansparks/2019/12/19/2020-predictions-for-ai-dl-and-ml/
 * Generative Adversarial Networks (GANs) for Beginners: Generating Images of Distracted Drivers **Medium** https://towardsdatascience.com/generative-adversarial-networks-gans-for-beginners-82f26753335e
+* ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations https://ai.googleblog.com/2019/12/albert-lite-bert-for-self-supervised.html
+* Twelve Million Phones, One Dataset, Zero Privacy https://www.nytimes.com/interactive/2019/12/19/opinion/location-tracking-cell-phone.html
+* Five things we learned at Oslo’s innovation festival https://apolitical.co/solution_article/five-things-we-learned-at-oslos-innovation-festival/
+* 20 Data Trends for 2020 https://storybydata.com/datacated-weekly/20-data-trends-for-2020/
+* Give Me Jeans not Shoes: How BERT Helps Us Deliver What Clients Want https://multithreaded.stitchfix.com/blog/2019/07/15/give-me-jeans/
+* Edge Computing — The New Frontier of the Web **Medium** https://link.medium.com/106uvj1pQ2
 
 
 ## Books/Papers
@@ -46,7 +52,11 @@
 * CAUSALITY http://bayes.cs.ucla.edu/BOOK-2K/index.html
 * Wi-Fi 6 and Private LTE/5G Technology and Business Models in Industrial IoT [file](books/iot-5g-wp.pdf)
 * KNN Algorithm Simulation Based on Quantum Information [file](books/KNNAlgorithmSimulationBasedonQuantumInformation.pdf)
-* [The Evolution of Imitation and Mirror Neurons in Adaptive Agents](emn_borens_cogsysres_r.pdf)
+* [The Evolution of Imitation and Mirror Neurons in Adaptive Agents](books/emn_borens_cogsysres_r.pdf)
+* [Quantum Teleportation in High Dimensions](books/Quantum_Teleportation_in_High_Dimensions.pdf)
+* Show and Tell: A Neural Image Caption Generator https://arxiv.org/pdf/1411.4555.pdf
+* Show, Attend and Tell: Neural Image Caption Generation with Visual Attention https://arxiv.org/pdf/1502.03044.pdf
+* [Linux Command Line Cheat Sheet](books/Linux_Command_Line_CheatSheet.pdf)
 
 
 ## Course/tutorials
@@ -62,6 +72,10 @@
 * Linux Command Line Basics https://www.udacity.com/course/linux-command-line-basics--ud595
 * Shell Workshop https://www.udacity.com/course/shell-workshop--ud206
 * Catalog | Next XYZ (ML - DL - Python - ... etc) https://next.tech/xyz/catalog
+* A quick guide to using FFmpeg to convert media files https://opensource.com/article/17/6/ffmpeg-convert-media-file-formats
+* 9 Free Courses by Harvard, MIT, IBM, Google, and Microsoft **Medium** https://medium.com/@PurpleGreenLemon/9-courses-to-learn-for-free-d7951a959f82
+* A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials https://github.com/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials
+* Coding Blocks courses https://cb.lk/join/MAN0QQ
 
 ## Links/URLs
 * Shell scripting https://explainshell.com/
@@ -115,6 +129,10 @@
 * Downloading Datasets into Google Drive via Google Colab **Medium**  https://towardsdatascience.com/downloading-datasets-into-google-drive-via-google-colab-bcb1b30b0166
 * Create a blog using Jekyll and GitHub pages with Docker https://yamatokataoka.github.io/create-a-blog-using-jekyll-and-github-pages-with-docker/
 * Optimization Model https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/optimization-model
+* Moral Machine http://moralmachine.mit.edu/
+* Computing Receptive Fields of Convolutional Neural Networks https://distill.pub/2019/computing-receptive-fields/
+* Quantitative Economics with Python https://python.quantecon.org/index_toc.html
+* SCC++: Predicting the programming language of questions and snippets of Stack Overflow https://www.sciencedirect.com/science/article/pii/S0164121219302791
 
 ## Videos
 * NEURAL NETWORKS! - CS50 Live, EP. 53 (pre-release) **2+ hours** https://www.youtube.com/watch?v=hQhPogn1dpM
@@ -142,3 +160,8 @@
     - Download: https://orange.biolab.si/
 * Artificial Curiosity https://www.youtube.com/watch?v=aom4RMOHezc&list=PL2-dafEMk2A663T80wzbIE4YDldoTaLDH
 * Neuroscience vs Philosophy | Full Debate | Margaret Boden, Barry Smith, Steven Rose, Roger Bolton https://www.youtube.com/watch?v=x_ypquYcNl4
+* word embeddings
+    - RNN W2L05 :  Learning word embeddings https://www.youtube.com/watch?v=xtPXjvwCt64
+    - Word Embeddings https://www.youtube.com/watch?v=5PL0TmQhItY
+    - What Are Word Embeddings for Text? https://machinelearningmastery.com/what-are-word-embeddings/
+    - RNN W2L01 :  Word Representation https://www.youtube.com/watch?v=hjx-zwVdfjc