Marked a number of links as deadlinks, also added replacements for a …

…few links
yml-blog · Nov 30, 2019 · b9f921a · b9f921a
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diff --git a/ML-on-code-programming-source-code.md b/ML-on-code-programming-source-code.md
@@ -1,6 +1,6 @@
 # ML on Code/Programm/Source Code
 
-- [Talk: Learning to Type by Liam Atkinson](https://lara.epfl.ch/~kuncak/Learning_to_Type_S1360006.mp4) at the [ml4p.org]() conference in 2018
+- [Talk: Learning to Type by Liam Atkinson](https://lara.epfl.ch/~kuncak/Learning_to_Type_S1360006.mp4) at the [ml4p.org]() conference in 2018 [deadlink]
 - [Awesome ML on Source Code](https://github.com/src-d/awesome-machine-learning-on-source-code)
 - [Machine Learning on Go Code](https://medium.com/sourcedtech/machine-learning-on-go-code-829e85e2d2c6)
 - [ML on Source Code](https://github.com/topics/machine-learning-on-source-code)

diff --git a/cloud-devops-infra/README.md b/cloud-devops-infra/README.md
@@ -23,7 +23,7 @@
   - [Cray Computers](https://www.cray.com/ai) | [Artificial Intelligence](https://www.cray.com/solutions/artificial-intelligence) | [Accel AI](https://www.cray.com/solutions/artificial-intelligence/cray-accel-ai) | [Cryp-em](https://www.cray.com/solutions/cryo-em) | [Autonomous Vehicles](https://www.cray.com/solutions/autonomous-vehicles) | [Geospatial AI](https://www.cray.com/solutions/geospatial-ai)
   - [GraphCore's IPU](README.md#ipu)
   - [Lambda Labs](https://lambdalabs.com/)
-  - NGD Systems: [Technology](https://www.ngdsystems.com/technology) | [Solutions](https://www.ngdsystems.com/solutions) - High Compute Storage, Scalable Computational Storage [deadlink] | [NGD Systems: Ensuring AI Advancement with Intelligent Storage](https://www.insightssuccess.com/ngd-systems-ensuring-ai-advancement-with-intelligent-storage/)
+  - NGD Systems: [Technology](https://www.ngdsystems.com/technology) [deadlink] | [Solutions](https://www.ngdsystems.com/solutions) - High Compute Storage, Scalable Computational Storage [deadlink] | [NGD Systems: Ensuring AI Advancement with Intelligent Storage](https://www.insightssuccess.com/ngd-systems-ensuring-ai-advancement-with-intelligent-storage/)
 
 ## Grid computing / Super computing
 
@@ -93,7 +93,7 @@
   - [All tutorials](https://cloud.google.com/tpu/docs/tutorials)
   - Command-line interface
       - https://cloud.google.com/sdk/gcloud/reference/compute/tpus/
-      - https://cloud.google.com/tpu/docs/custom-setup
+      - https://cloud.google.com/tpu/docs/setup-gcp-account
   - [Cloud TPU tools](https://cloud.google.com/tpu/docs/cloud-tpu-tools)
   - [Performance Guide](https://cloud.google.com/tpu/docs/performance-guide)
   - [TPU Estimator API](https://cloud.google.com/tpu/docs/using-estimator-api)

diff --git a/courses.md b/courses.md
@@ -99,7 +99,8 @@
     - [Slides](https://storage.googleapis.com/wandb/Bloomberg%20Class%201.pdf)
     - [Building and Debugging CNNs](https://wb-ml.slack.com/files/UN2SL6G7Q/FNE9193U0/bloomberg_class_2.pdf)
     - [Introduction to ML](https://wb-ml.slack.com/files/UN2SL6G7Q/FNE3Q7NN7/bloomberg_class_3.pdf)
-- [Course material by Students of AI (Imperial College, London)](https://github.com/Students-for-AI/The-Academy-of-AI)
+- [Course material by Students of AI (Imperial College, London)](https://github.com/Imperial-College-Data-Science-Society/)
+[previous github link](https://github.com/Students-for-AI/The-Academy-of-AI), [alternative forked repo](https://github.com/DurhamAI/The-Academy-of-AI)
 - [Comprehensive list of machine learning videos by Yaz](https://github.com/yazdotai/machine-learning-video-courses)
 - [3Blue1Brown](https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw)
     - https://www.youtube.com/watch?v=aircAruvnKk 

diff --git a/data/databases.md b/data/databases.md
@@ -2,7 +2,7 @@
 
 - [Grakn and Graql](http://grakn.ai/) - not just graphs, or graph database but knowledge graphs | [Docs | Quick start](https://dev.grakn.ai/docs/general/quickstart) | [GitHub](https://github.com/graknlabs/grakn) | [Videos](https://www.youtube.com/channel/UCtZKw0RFof3x23KqGtW3yDA) | [Blogs](https://blog.grakn.ai/) | [Discuss](https://discuss.grakn.ai/) | [Slack](https://grakn.ai/slack)
     + See [example](../examples/data/databases/graph/grakn/README.md) in the `../examples/data/databases/graph/grakn` folder
-- [Redis Graph](https://oss.redislabs.com/redisgraph/) | [Blogs](https://blog.grakn.ai/?gi=d6874fc57ebb) | [Videos](https://www.youtube.com/channel/UCtZKw0RFof3x23KqGtW3yDA) | [Skillsmatter: how redis enterprise made redis highly available, scalable, durable and cloudnative](https://skillsmatter.com/skillscasts/11886-how-redis-enterprise-made-redis-highly-available-scalable-durable-and-cloudnative)
+- [Redis Graph](https://oss.redislabs.com/redisgraph/) | [Blogs](https://blog.grakn.ai/?gi=d6874fc57ebb) | [Videos](https://www.youtube.com/channel/UCtZKw0RFof3x23KqGtW3yDA) | [Skillsmatter: how redis enterprise made redis highly available, scalable, durable and cloudnative](https://skillsmatter.com/skillscasts/11886-how-redis-enterprise-made-redis-highly-available-scalable-durable-and-cloudnative) [deadlink]
 - [Neo4j](https://neo4j.com/)
 - [Gun: A realtime, decentralized, offline-first, mutable graph database engine](https://github.com/amark/gun)
 - [Cayley: An open-source graph database](https://github.com/cayleygraph/cayley)

diff --git a/details/java-jvm.md b/details/java-jvm.md
@@ -113,7 +113,7 @@ MLPMNist using DL4J: [![MLPMNist using DL4J](https://img.shields.io/docker/pulls
 - [Java ML Dev Videos on YouTube: Naive Bayes w/ JAVA - Tutorial 01](https://www.youtube.com/watch?v=mrP4CyW4tKA&frags=pl%2Cwn)
 - Java ML Framework: [1](https://github.com/datumbox/datumbox-framework) [2](http://blog.datumbox.com/developing-a-naive-bayes-text-classifier-in-java/)
 - [Weka 3: Machine Learning Software in Java](https://www.cs.waikato.ac.nz/ml/weka)
-- [Smile - Statistical Machine Intelligence and Learning Engine](https://haifengl.github.io/smile)
+- [Smile - Statistical Machine Intelligence and Learning Engine](https://haifengl.github.io/)
 - [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
 - Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: [Slides](http://daiwz.net/org/slides/ABL-meetup.html) | [Video](https://www.youtube.com/watch?v=ETHrFxiFIUM) | [GitHub](https://github.com/AbductiveLearning/ABL-HED)
 - [Overview of AI Libraries in Java](https://www.baeldung.com/java-ai)

diff --git a/details/javascript.md b/details/javascript.md
@@ -5,7 +5,7 @@
    - Tensorflow Playground [1](https://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.87744&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false) | [2](http://playground.tensorflow.org/#activation=sigmoid&regularization=L2&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=5,3&seed=0.84062&showTestData=false&discretize=true&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&showTestData_hide=true&learningRate_hide=true&regularizationRate_hide=true&percTrainData_hide=true&numHiddenLayers_hide=false&discretize_hide=true&activation_hide=true&problem_hide=true&noise_hide=true&regularization_hide=true&dataset_hide=true&batchSize_hide=true&playButton_hide=false) | [3](http://playground.tensorflow.org/#activation=linear&regularization=L2&batchSize=10&dataset=gauss&regDataset=reg-plane&learningRate=0.0001&regularizationRate=0&noise=0&networkShape=&seed=0.27124&showTestData=false&discretize=true&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&showTestData_hide=true&learningRate_hide=true&regularizationRate_hide=true&percTrainData_hide=true&numHiddenLayers_hide=true&discretize_hide=true&activation_hide=true&problem_hide=true&noise_hide=true&regularization_hide=true&dataset_hide=true&batchSize_hide=true&playButton_hide=false) | [4](http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=spiral&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.29208&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false)
    - [Bring deep learning into the browser &mdash; In Browser AI](https://inbrowser.ai/)
    - [Run Keras models in the browser, with GPU support using WebGL](https://github.com/transcranial/keras-js)
-   - [Tensorflow.js: bringing ML into the browser](https://skillsmatter.com/skillscasts/11876-tensorflow-js-bringing-ml-into-the-browser)
+   - [Tensorflow.js: bringing ML into the browser](https://skillsmatter.com/skillscasts/11876-tensorflow-js-bringing-ml-into-the-browser) [deadlink]
    - See [this link](https://github.com/josephmisiti/awesome-machine-learning#javascript) for more JavaScript related ML links
 
 

diff --git a/details/julia-python-and-r.md b/details/julia-python-and-r.md
@@ -30,7 +30,7 @@
    - [Python for Computational Science and Engineering (book)](http://www.southampton.ac.uk/~fangohr/teaching/python/book.html)
    - [PyCon 2015 Scikit-learn Tutorial](https://github.com/jakevdp/sklearn_pycon2015)
    - [Book: Automate the boring stuff](https://automatetheboringstuff.com/)
-   - [Book: How to Think Like a Computer Scientist: Interactive Edition](http://interactivepython.org/courselib/static/thinkcspy/index.html) | [How to Think Like a Computer Scientist - non-Interactive Edition](http://openbookproject.net/thinkcs/python/english2e/)
+   - [Book: How to Think Like a Computer Scientist: Interactive Edition](http://interactivepython.org/courselib/static/thinkcspy/index.html) | [How to Think Like a Computer Scientist - non-Interactive Edition](http://openbookproject.net/thinkcs/python/english2e/) [deadlink]
    - Python Project (Classification):
      - Part A: https://www.youtube.com/watch?v=p0snNMCbvN4&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=8
      - Part B: https://www.youtube.com/watch?v=j4IgXflsZtg&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=9

diff --git a/details/maths-stats-probability.md b/details/maths-stats-probability.md
@@ -37,7 +37,7 @@
 - [Visualization in Bayesian workflow](https://arxiv.org/abs/1709.01449)
 - [Suite of probabilitic programming language repos from Improbable.io](https://github.com/improbable-research)
 - [Chris Fonnesbeck’s presentation: PyMC's Big Adventure - Lessons Learned from the Development of Open-source Software for Probabilistic Programming](https://gitpitch.com/fonnesbeck/neurips_2018_talk#/) | [Chris Fonnesbeck](https://twitter.com/fonnesbeck)
-- [Skillsmatter: Precision Medicine With Mechanistic, Bayesian Models](https://skillsmatter.com/skillscasts/12129-bayesian-mixer-london-june)
+- [Skillsmatter: Precision Medicine With Mechanistic, Bayesian Models](https://skillsmatter.com/skillscasts/12129-bayesian-mixer-london-june) [deadlink]
 - [Colin Carroll’s presentation: Tidy and beautiful - Visualizing Bayesian models with xarray and ArviZ](https://colcarroll.github.io/arviz_pydata_nyc/#/) | [Colin Carroll](https://twitter.com/colindcarroll)
 - [Amortized Monte Carlo Integration](https://www.youtube.com/watch?v=-oHCqLFLTAI) by [Tom Rainforth](http://www.robots.ox.ac.uk/~twgr/)
 - Books

diff --git a/details/visualisation.md b/details/visualisation.md
@@ -1,6 +1,6 @@
 # Visualisation
 
-  - [Vega](https://skillsmatter.com/skillscasts/12257-vega-a-grammar-of-interactive-graphics) 
+  - [Vega](https://skillsmatter.com/skillscasts/12257-vega-a-grammar-of-interactive-graphics) [deadlink]
   - [Interactive Machine Learning, Deep Learning and Statistics websites](https://p.migdal.pl/interactive-machine-learning-list/)
   - [Data Visualisation by Kaggle.com](https://www.kaggle.com/learn/data-visualization)
   - [How the BBC Visual and Data Journalism team works with graphics in R](https://medium.com/bbc-visual-and-data-journalism/how-the-bbc-visual-and-data-journalism-team-works-with-graphics-in-r-ed0b35693535)
@@ -20,7 +20,7 @@ data visualisation. Magic from spreadsheets. Next-level storytelling. Embed on y
     - [Infographics and presentation tools](https://www.fireplusalgebra.com/infographics-and-presentation-tools)
     - [Interactive data visualisation tools and libraries](https://www.fireplusalgebra.com/infographics-and-presentation-tools)
     - [Photography, illustration and icon resources](https://www.fireplusalgebra.com/image-and-icon-resources)
-  - [Telling Human Stories With Data (skillscast)](https://skillsmatter.com/skillscasts/14252-telling-human-stories-with-data) by [Alan Rutter](https://www.fireplusalgebra.com/about)
+  - [Telling Human Stories With Data (skillscast)](https://skillsmatter.com/skillscasts/14252-telling-human-stories-with-data) [deadlink] by [Alan Rutter](https://www.fireplusalgebra.com/about)
   - Resources kindly recommended by [Alan Rutter](https://www.fireplusalgebra.com/about)
     - [The Data Visualisation Catalogue](https://datavizcatalogue.com/)
     - [Flowing Data](https://flowingdata.com/) | [About Nathan Yau](https://flowingdata.com/about-nathan/)

diff --git a/examples/nlp-java-jvm/README.md b/examples/nlp-java-jvm/README.md
@@ -2,7 +2,7 @@
 
 ---
 
-This area in the repo is a result of the blog post [Exploring NLP concepts using Apache OpenNLP](https://medium.com/@neomatrix369//exploring-nlp-concepts-using-apache-opennlp-4d59c3cac8) | [Original post](https://blog.valohai.com/exploring-nlp-concepts-using-apache-opennlp-1?from=3oxenia9mtr6). Please refer to the post before considering using this repo to understand better on how to use the different aspects of it.
+This area in the repo is a result of the blog post [Exploring NLP concepts using Apache OpenNLP](https://medium.com/@neomatrix369/exploring-nlp-concepts-using-apache-opennlp-4d59c3cac8) | [Original post](https://blog.valohai.com/exploring-nlp-concepts-using-apache-opennlp-1?from=3oxenia9mtr6). Please refer to the post before considering using this repo to understand better on how to use the different aspects of it.
 
 ### Please find the working project at [Valohai's GitHub](https://github.com/valohai/) org, see repo [nlp-java-jvm-example project on GitHub](https://github.com/valohai/nlp-java-jvm-example)
 

diff --git a/natural-language-processing/library-framework-models-tools-services.md b/natural-language-processing/library-framework-models-tools-services.md
@@ -8,7 +8,7 @@
 - [Facebook's Pythia](https://code.fb.com/ai-research/pythia/) | [github](https://github.com/facebookresearch/pythia) | [Medium](https://medium.com/syncedreview/facebook-open-sources-pythia-for-vision-and-language-multimodal-ai-models-be480644b538)
 - [Flair by Zolando Research](https://www.analyticsvidhya.com/blog/2019/02/flair-nlp-library-python/) | [github](https://github.com/zalandoresearch/flair) | [Research paper](https://drive.google.com/file/d/17yVpFA7MmXaQFTe-HDpZuqw9fJlmzg56/view)
 - [Microsoft NLP](https://github.com/microsoft/nlp)
-- [Smile - Statistical Machine Intelligence and Learning Engine](https://haifengl.github.io/smile)
+- [Smile - Statistical Machine Intelligence and Learning Engine](https://haifengl.github.io/)
 - [Standford NLP Group](https://nlp.stanford.edu/)
 - [Google’s Bert](https://github.com/google-research/bert) | [TensorFlow code and pre-trained models for BERT](https://github.com/google-research/bert)
 - [H2O Driverless AI](http://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/nlp.html) 

diff --git a/presentations/data/02-devoxx-uk-2019/README.md b/presentations/data/02-devoxx-uk-2019/README.md
@@ -15,7 +15,7 @@ See [YouTube video on the Devoxx UK channel (~51 mins)](https://www.youtube.com/
 
 ### Abstract
 
-See [Devoxx UK 2019 - talk abstract](https://devoxxuk19.confinabox.com/talk/VEM-8021/Do_we_know_our_data_as_good_as_we_know_our_tools)
+See [Devoxx UK 2019 - talk abstract](https://devoxxuk19.confinabox.com/talk/VEM-8021/Do_we_know_our_data_as_good_as_we_know_our_tools) | [cached version of Talk Abstract](https://webcache.googleusercontent.com/search?q=cache:ggqgEPqxb0MJ:https://devoxxuk19.confinabox.com/talk/VEM-8021/Do_we_know_our_data,_as_good_as_we_know_our_tools%253F+&cd=1&hl=en&ct=clnk&gl=uk)
 
 For many of us who are developer turning data scientist, we are always concerned about how to build a model, train it, etc... And yes, we want the best accuracy (close to 99%).
 
@@ -36,4 +36,14 @@ We will cover some of these techniques:
     cleaning - deal with missing/ambiguous values, outliers, generating synthetic data, resampling
     preparation - using statistical and physics functions, dimensionality reduction, feature selection, resampling
 
-And using different kinds of plots relevant at different stages.
+And using different kinds of plots relevant at different stages.
+
+### Code
+
+- [Simple Data generation code](../../../notebooks/jupyter/data/data-generation)
+
+### Notebooks
+
+- [Notebooks used during the talk](../../../notebooks/jupyter/data/)
+- Also see towards the bottom of [Notebooks](../../../notebooks/README.md)
+
-Original file line number
+Diff line change
@@ Expand Up / @@ -2,7 +2,7 @@ @@
     ---
-    This area in the repo is a result of the blog post [Exploring NLP concepts using Apache OpenNLP](https://medium.com/@neomatrix369//exploring-nlp-concepts-using-apache-opennlp-4d59c3cac8) | [Original post](https://blog.valohai.com/exploring-nlp-concepts-using-apache-opennlp-1?from=3oxenia9mtr6). Please refer to the post before considering using this repo to understand better on how to use the different aspects of it.
+    This area in the repo is a result of the blog post [Exploring NLP concepts using Apache OpenNLP](https://medium.com/@neomatrix369/exploring-nlp-concepts-using-apache-opennlp-4d59c3cac8) | [Original post](https://blog.valohai.com/exploring-nlp-concepts-using-apache-opennlp-1?from=3oxenia9mtr6). Please refer to the post before considering using this repo to understand better on how to use the different aspects of it.
     ### Please find the working project at [Valohai's GitHub](https://github.com/valohai/) org, see repo [nlp-java-jvm-example project on GitHub](https://github.com/valohai/nlp-java-jvm-example)
@@ Expand Down @@