Updating links under Details, Timeseries, model interpretability, dat…

…a and cloud
UmaGunturi · Jun 19, 2020 · 0b9ea1e · 0b9ea1e
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diff --git a/cloud-devops-infra/README.md b/cloud-devops-infra/README.md
@@ -83,6 +83,7 @@ reproducible research
     - [Intel® AI Developer Webinar Series](https://software.seek.intel.com/AIWebinarSeries?registration_source=IDZ) | [All webinars listing](https://intelvs.on24.com/vshow/IntelWebinarEvents/#content/2033414)
     - The PlaidML Tensor Compiler - [webinar](https://event.on24.com/eventRegistration/console/EventConsoleApollo.jsp?&eventid=2026509&sessionid=1&username=&partnerref=&format=fhaudio&mobile=false&flashsupportedmobiledevice=false&helpcenter=false&key=B27628973F7FA8B9758983E373E36ED1&text_language_id=en&playerwidth=1000&playerheight=700&overwritelobby=y&eventuserid=246511746&contenttype=A&mediametricsessionid=207230377&mediametricid=2857349&usercd=246511746&mode=launch)
     - nGraph - Unlocking next-generation performance with deep learning compilers: [webinar](https://intelvs.on24.com/vshow/IntelWebinarEvents/#content/2033414) | [slides](https://event.on24.com/event/20/33/41/2/rt/1/documents/resourceList1565185524584/s_ngraphwebinar1565185512750.pdf) | [homepage](https://www.ngraph.ai/) | [github](https://github.com/NervanaSystems/ngraph)
+    - Intel Debug memory & threading bugs: [Webinar slides](https://event.on24.com/event/22/68/22/4/rt/1/documents/resourceList1588698180846/s_webinarslides1588698178133.pdf) | [Intel inspector](https://software.intel.com/inspector) | [](software.intel.com/en-us/inspector/choose-download?cid=em&source=elo&campid=iags_WW_iagstd2_EN_2020_5.6%20Debug%20Thread%20Webinar_C-MKA-16350_T-MKA-17876&content=iags_WW_iagstd2_EMRW_EN_2020_5.6%20Debug%20Thread%20WebinarRM1_C-MKA-16350_T-MKA-17876&elq_cid=2921631&em_id=57114&elqrid=fe81e70c81c3419bb69dec833cdd0fa4&elqcampid=37341&erpm_id=5550945#inspector) | [Inspector Docs](https://software.intel.com/inspector/documentation/featured-documentation) | [Intel® Parallel Studio XE](https://software.intel.com/en-us/parallel-studio-xe/choose-download?cid=em&source=elo&campid=iags_WW_iagstd2_EN_2020_5.6%20Debug%20Thread%20Webinar_C-MKA-16350_T-MKA-17876&content=iags_WW_iagstd2_EMRW_EN_2020_5.6%20Debug%20Thread%20WebinarRM1_C-MKA-16350_T-MKA-17876&elq_cid=2921631&em_id=57114&elqrid=fe81e70c81c3419bb69dec833cdd0fa4&elqcampid=37341&erpm_id=5550945) | [Intel® System Studio](https://software.intel.com/en-us/system-studio/choose-download?cid=em&source=elo&campid=iags_WW_iagstd2_EN_2020_5.6%20Debug%20Thread%20Webinar_C-MKA-16350_T-MKA-17876&content=iags_WW_iagstd2_EMRW_EN_2020_5.6%20Debug%20Thread%20WebinarRM1_C-MKA-16350_T-MKA-17876&elq_cid=2921631&em_id=57114&elqrid=fe81e70c81c3419bb69dec833cdd0fa4&elqcampid=37341&erpm_id=5550945)
     - Intel Analysers/Profilers:
       - [Webinar slides: offload your code to GPU (part 1)](https://event.on24.com/event/23/51/32/1/rt/1/documents/resourceList1590781996922/s_webinarslides1590781995277.pdf)
       - [oneAPI Toolkits](https://software.intel.com/content/www/us/en/develop/tools/oneapi.html#oneapi-toolkits)

diff --git a/data/data-exploratory-analysis.md b/data/data-exploratory-analysis.md
@@ -63,6 +63,9 @@ aka *_Exploratory Data Analysis_*
 - [10 Clustering Algorithms With Python](https://machinelearningmastery.com/clustering-algorithms-with-python/)
 - [Finding organic clusters in complex data-networks](https://www.datasciencecentral.com/profiles/blogs/finding-organic-clusters-in-complex-data-networks) [LinkedIn Post](https://www.linkedin.com/posts/data-science-central_finding-organic-clusters-in-complex-data-networks-activity-6650907272413274112-p-H7)
 - [How to Develop an Encoder-Decoder Model for Sequence-to-Sequence Prediction in Keras](https://machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/)
+- [10 Clustering Algorithms With Python](https://machinelearningmastery.com/clustering-algorithms-with-python/)
+- [An Introduction to Clustering and different methods of clustering](https://www.linkedin.com/posts/data-science-central_an-introduction-to-clustering-and-different-activity-6657823846013419520-3o5Y)
+- [Scale-Invariant Clustering and Regression](https://www.linkedin.com/posts/data-science-central_scale-invariant-clustering-and-regression-activity-6657477059243229184-1ibv)
 
 ### Outliers
 

diff --git a/data/databases.md b/data/databases.md
@@ -13,6 +13,8 @@
 - [MemGraph](https://memgraph.com/) - Open source projects from the world's fastest graph database. | [GitHub](https://github.com/memgraph)
 - [TigerGraph](https://www.tigergraph.com/) - TigerGraph is the fastest and most scalable graph database analytics platform—and the only native parallel graph database. Previously GraphSQL | [GitHub](https://github.com/tigergraph)
 - [Graph Database via MathWorks interfaces](https://uk.mathworks.com/help/database/graph-database.html) - Explore, manage, store, and analyze graph data in Neo4j® database using MATLAB® interface to Neo4j or Database Toolbox™ Interface for Neo4j Bolt Protocol | [Database Toolbox](https://uk.mathworks.com/products/database.html)
+- [COMP 766 - Graph Representation Learning](https://cs.mcgill.ca/~wlh/comp766)
+- [𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗮𝗻𝗱 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜 (Good read & my paper of the day)](https://www.linkedin.com/posts/philipvollet_knowledge-graph-embeddings-and-explainable-activity-6662231581974908928-vtKT)
 
 ### Redis modules
 - [neural-redis](https://github.com/antirez/neural-redis) - Online trainable neural networks as Redis data types

diff --git a/data/model-analysis-interpretation-explainability.md b/data/model-analysis-interpretation-explainability.md
@@ -5,14 +5,18 @@
 - [Yellowbrick](https://www.scikit-yb.org/en/latest/#yellowbrick-machine-learning-visualization) - is a suite of visual diagnostic tools called “Visualizers” that extend the Scikit-Learn API to allow human steering of the model selection process
 - [Shap](https://github.com/slundberg/shap) - A unified approach to explain the output of any machine learning model
 - [LIME](https://github.com/marcotcr/lime)
+- Seldon's Model explainability: [Alibi](https://www.seldon.io/tech/products/alibi/) | [Github](https://github.com/SeldonIO/alibi)
+- QuantumBlacksLabs' CasualNex: [Post 1](https://www.mckinsey.com/about-us/new-at-mckinsey-blog/causalnex-our-new-open-source-library-leverages-cause-and-effect-relationships-in-data) | [Post 2](https://medium.com/quantumblack/introducing-causalnex-driving-models-which-respect-cause-and-effect-a561545f0a5e) | [GitHub](https://github.com/quantumblacklabs/causalnex)
 - [4 Python Libraries For Getting Better Model Interpretability](https://www.analyticsindiamag.com/4-python-libraries-for-getting-better-model-interpretability/)
 - [Integrated Gradients: Axiomatic Attribution for Deep Networks](https://github.com/ankurtaly/Integrated-Gradients) | [Paper](https://arxiv.org/abs/1703.01365)
 - [Resources on GitHub on interpretability](https://github.com/topics/interpretability)
 - [Awesome Machine Learning Interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability) - A Curated, but Probably Biased and Incomplete, List of Awesome Machine Learning Interpretability Resources
 - [Seldon's opensource library for MachineLearning model inspection and interpretation](https://github.com/SeldonIO/alibi)
 - [Elifive](https://eli5.readthedocs.io/en/latest/overview.html) | [GitHub](https://github.com/TeamHG-Memex/eli5)
 - [Shap](https://github.com/slundberg/shap) [1](https://blog.dominodatalab.com/shap-lime-python-libraries-part-1-great-explainers-pros-cons/) [2](https://blog.dominodatalab.com/shap-lime-python-libraries-part-2-using-shap-lime/)
-
+- [Demystifying Black-Box Models with SHAP Value Analysis](https://www.linkedin.com/posts/vincentg_demystifying-black-box-models-with-shap-value-activity-6657041692224483328-ixRo)
+
+
 ## Articles, blog posts, papers, notebooks, books, presentations
 
 - [DataRobot: Model Interpretability - What is Model Interpretability in Machine Learning?](https://www.datarobot.com/wiki/interpretability/)

diff --git a/details/java-jvm.md b/details/java-jvm.md
@@ -42,7 +42,7 @@ MLPMNist using DL4J: [![MLPMNist using DL4J](https://img.shields.io/docker/pulls
   - [AI apps demand DevOps infrastructure automation](https://searchitoperations.techtarget.com/news/252433627/AI-apps-demand-DevOps-infrastructure-automation) ([Tweet](https://twitter.com/java/status/974284331702108160))
   - [Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics](https://www.datasciencecentral.com/profiles/blogs/difference-between-machine-learning-data-science-ai-deep-learning) ([Tweet](https://twitter.com/java/status/947129400083890176))
   - [You Could Become an AI Master Before You Know It. Here’s How.](https://www.technologyreview.com/s/608921/you-could-become-an-ai-master-before-you-know-it-heres-how/) ([Tweet](https://twitter.com/java/status/920620763262148609))
-  - [Why Java Will Dominate the Future of Machine Learning, AI, and Big Data](https://www.deepnetts.com/blog/why-java-will-dominate-the-future-of-machine-learning-ai-and-big-data.html) ([Tweet](https://twitter.com/java/status/1065642563468709889))
+  - [Why Java Will Dominate the Future of Machine Learning, AI, and Big Data](https://www.deepnetts.com/blog/why-java-will-dominate-the-future-of-machine-learning-ai-and-big-data.html) ([Tweet](https://twitter.com/java/status/1065642563468709889)) | [About DeepNetts](https://www.deepnetts.com/product.html) | [Download DeepNetts](https://deepnetts.com:10172/download/faces/user/signUpAndDownload.xhtml)
   - [Using Java for Artificial Intelligence](https://skymind.ai/wiki/java-ai) ([Tweet](https://twitter.com/java/status/1037728632142213120))
   - [Want A Bigger Bang From AI? Embed It Into Your Apps](https://www.forbes.com/sites/oracle/2018/11/27/want-a-bigger-bang-from-ai-embed-it-into-your-apps/#1c07cc7a4e2d) ([Tweet](https://twitter.com/java/status/1068779734337708032))
   - [A Beginner's Guide to Automated Machine Learning & AI](https://t.co/s7lvKY9Y7z) ([Tweet](https://twitter.com/java/status/1051844141808664576))

diff --git a/details/julia-python-and-r.md b/details/julia-python-and-r.md
@@ -68,6 +68,10 @@
   - [Neural Module Networks for Reasoning over Text: [Paper]](https://arxiv.org/abs/1912.04971) | [Code](https://nitishgupta.github.io/nmn-drop/) [LinkedIn Post](https://www.linkedin.com/posts/montrealai_neuralnetworks-reasoning-symbolicai-activity-6630879104814116864-Opyq)
   - [”A Beginner's Guide to the Mathematics of Neural Networks”](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.161.3556&rep=rep1&type=pdf) [LinkedIn Post](https://www.linkedin.com/posts/montrealai_artificialintelligence-deeplearning-neuralnetworks-activity-6637386735531683840-9RGJ)
   - [Machine Learning: Data Insights for Model Building](https://towardsdatascience.com/machine-learning-data-insights-for-model-building-b6bdea0ac092) [LinkedIn Post](https://www.linkedin.com/posts/towards-data-science_machine-learning-data-insights-for-model-activity-6644068456784367616-Ylyl)
+  - [Representation of NN with different variants](https://www.linkedin.com/posts/ashishpatel2604_datascience-deeplearning-machinelearning-activity-6676025346330112000-c2YB)
+  - [Neural Networks are Function Approximation Algorithms](https://machinelearningmastery.com/neural-networks-are-function-approximators/)
+  - [Troubleshooting Deep NNs](http://josh-tobin.com/assets/pdf/troubleshooting-deep-neural-networks-01-19.pdf)
+  - [Understand the Impact of Learning Rate on Neural Network Performance](https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/)
 
   ## Generative Adversarial Network (GAN)
 
@@ -82,10 +86,11 @@
 
   - [Introduction to Genetic Algorithms & their application in data science](https://media.licdn.com/dms/document/C511FAQE99ZtwL7srQA/feedshare-document-pdf-analyzed/0?e=1570906800&v=beta&t=OiKdTPzo2ozpBzVl0N_5JPvzAoqsS2iie9cHNK4BXCg) [deadlink]
   - Hands-On Genetic Algorithms with Python book:
-    - Book: [Amazon](https://www.amazon.com/Hands-Genetic-Algorithms-Python-intelligence-ebook/dp/B0842372RQ/) | [Packt Publishing](https://www.packtpub.com/data/hands-on-genetic-algorithms-with-python)
+    - Book: [Amazon](https://www.amazon.com/Hands-Genetic-Algorithms-Python-intelligence-ebook/dp/B0842372RQ/) | [Packt Publishing](https://www.packtpub.com/data/hands-on-genetic-algorithms-with-python) | [Free chapter 7](https://static.packt-cdn.com/downloads/9781838557744_ColorImages.pdf)
     - [Static images in each chapter](https://static.packt-cdn.com/downloads/9781838557744_ColorImages.pdf)
     - [Video playlist over each chapter](https://www.youtube.com/playlist?list=PLeLcvrwLe186eQR5Y-T_kOZEjADAwTScy)
     - [Code from the book](https://github.com/PacktPublishing/Hands-On-Genetic-Algorithms-with-Python)
+    - [Enhancing Machine Learning Models using Genetic Algorithms by Eyal W.](https://www.youtube.com/watch?v=ubit7SU5BJQ)
   - [Genetic Algorithm: The Nature of Code playlist](https://www.youtube.com/watch?v=9zfeTw-uFCw&list=PLRqwX-V7Uu6bJM3VgzjNV5YxVxUwzALHV)
     - [Session 2 - Genetic Algorithms - Intelligence and Learning The Coding Train](https://www.youtube.com/watch?v=c8gZguZWYik&list=PLRqwX-V7Uu6bw4n02JP28QDuUdNi3EXxJ)
     - [Coding Challenge #35.4: Traveling Salesperson with Genetic Algorithm](https://www.youtube.com/watch?v=M3KTWnTrU_c)
@@ -192,7 +197,10 @@
 
   ### Testing
 
-  See [Machine Learning > Testing](./julia-python-and-r/machine-learning.md#testing) 
+  - [A machine learning testing framework for sklearn and pandas. The goal is to help folks assess whether things have changed over time](https://github.com/EricSchles/drifter_ml)
+  - [Testing Machine Learning Models with Eric Schles](https://www.youtube.com/watch?v=bZtdnFVAfbs)
+
+  Also see [Machine Learning > Testing](./julia-python-and-r/machine-learning.md#testing) 
 
   ### Deep Learning
 

diff --git a/details/julia-python-and-r/machine-learning.md b/details/julia-python-and-r/machine-learning.md
@@ -122,6 +122,13 @@
 - [Two New Free Books on Machine Learning](https://www.linkedin.com/posts/data-science-central_two-new-free-books-on-machine-learning-activity-6651587789072715776-09QA)
 - [The Hundred-Page Machine Learning Book by Andriy Burkov](https://www.linkedin.com/posts/andriyburkov_chapter-10-other-forms-of-learning-activity-6653853295150452736-Bm5d)
 
+## Uncertainty
+
+- [GitHub topic: uncertainties](https://github.com/topics/uncertainties)
+- [Charles's talk on Probability, Uncertainty: The Surprising Utility of Surprise](https://docs.google.com/presentation/d/1PVonxRuMns5TjNXLReg6C3mFlVyWylBZfCgJZNFENoA/edit?usp=sharing)
+- [Aggregated resources on the topic "uncertainty"](https://www.kaggle.com/c/m5-forecasting-uncertainty/discussion/155085)
+
+
 # Contributing
 
 Contributions are very welcome, please share back with the wider community (and get credited for it)!

diff --git a/details/maths-stats-probability.md b/details/maths-stats-probability.md
@@ -136,6 +136,12 @@
 - [An introduction to (and puns on) Bayesian neural networks](https://engineering.papercup.com/posts/bayesian-neural-nets/)
 - [Bayesian Inference Algorithms: MCMC and VI](https://towardsdatascience.com/bayesian-inference-algorithms-mcmc-and-vi-a8dad51ad5f5)
 - [A chat w/ @twiecki about #BayesianHierarchicalModels! What are they? How to build them? And why you should be very careful when building them](https://twitter.com/alex_andorra/status/1238004298245644288)
+- Bayesian Hyperparameter Optimization - A Primer: [Blog](https://www.wandb.com/articles/bayesian-hyperparameter-optimization-a-primer) | [Notebook](https://colab.research.google.com/drive/1SNP7ioYd3LHj2HmNDJIG2N7rPh1IiDbm?authuser=1#scrollTo=nkCUrzzIeLSM)
+- [How to Implement Bayesian Optimization from Scratch in Python](https://machinelearningmastery.com/what-is-bayesian-optimization/)
+- [Develop an Intuition for Bayes Theorem With Worked Examples](https://machinelearningmastery.com/intuition-for-bayes-theorem-with-worked-examples/)
+- [Bayesian Stats 101 for Data Scientists](https://www.linkedin.com/posts/towards-data-science_bayesian-stats-101-for-data-scientists-activity-6655949045678387202-qlgP)
+- [New Marketing Insight from Unsupervised Bayesian Belief Networks](https://www.linkedin.com/posts/vincentg_new-marketing-insight-from-unsupervised-bayesian-activity-6657419179529949184-nyP4)
+
 
 # Contributing
 

diff --git a/time-series_anomaly-detection/time-series.md b/time-series_anomaly-detection/time-series.md
@@ -174,6 +174,10 @@
 - [(Video) Igor Gotlibovych: Deep Learning and Time Series Forecasting for Smarter Energy | PyData London 2019](https://www.youtube.com/watch?v=p6mKFs6HVlg (40m))
 - [Selecting Forecasting Methods in Data Science](https://www.linkedin.com/posts/vincentg_selecting-forecasting-methods-in-data-science-activity-6637418894577459200-NXel)
 - Multi-step Time Series Forecasting with Machine Learning for Electricity Usage: [Part 1](https://machinelearningmastery.com/multi-step-time-series-forecasting-with-machine-learning-models-for-household-electricity-consumption/) [Part 2](https://www.linkedin.com/posts/jasonbrownlee_multi-step-time-series-forecasting-with-machine-activity-6640791155732807680-IdDZ)
+- [Probabilistic Forecasting: Learning Uncertainty](https://www.datasciencecentral.com/profiles/blogs/probabilistic-forecasting-learning-uncertainty)
+- [Three Approaches to Predicting Uncertainty](https://www.kaggle.com/c/m5-forecasting-uncertainty/discussion/133613)
+- [Probabilistic Forecasting: Learning Uncertainty](https://www.datasciencecentral.com/profiles/blogs/probabilistic-forecasting-learning-uncertainty)
+- [Aggregated resources on the topic "uncertainty"](https://www.kaggle.com/c/m5-forecasting-uncertainty/discussion/155085)
 
 ## Forecasting using Prophet