updates

Author: nocomplexity

MLlearningresources.md

@@ -21,132 +21,139 @@ notebook, but most tutorials offer notebooks ready to use directly.
 
 -   A Course in Machine Learning, <http://ciml.info/>
 
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 -   AutoML: Methods, Systems, Challenges,
     <https://www.ml4aad.org/wp-content/uploads/2019/05/AutoML_Book.pdf>
 
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 -   Building Safe A.I., A Tutorial for Encrypted Deep Learning,
     <https://iamtrask.github.io/2017/03/17/safe-ai/>
 
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 -   Collection of Interactive Machine Learning Examples,
     <https://aihub.cloud.google.com/s?category=notebook>
 
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 -   Cryptography and Machine Learning, Mixing both for
     privacy-preserving machine learning, <https://mortendahl.github.io/>
 
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 -   Dive into Deep Learning, An interactive deep learning book with
     code, math, and discussions, <https://d2l.ai/>
 
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 -   Explainable Deep Learning: A Field Guide for the Uninitiated. Great
     learning guide for new and starting researchers in the Deep neural
     network (DNN) field. <https://arxiv.org/pdf/2004.14545.pdf>
 
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+- Fairness and machine learning, Limitations and Opportunities by Solon Barocas, Moritz Hardt, Arvind Narayanan, https://fairmlbook.org/index.html
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+ +++
 
 -   Foundations of Machine Learning, Understand the Concepts, Techniques
     and Mathematical Frameworks Used by Experts in Machine Learning,
     <https://bloomberg.github.io/foml/#home>
 
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 -   Interpretable Machine Learning, A Guide for Making Black Box Models
     Explainable,Christoph Molnar,
     <https://christophm.github.io/interpretable-ml-book/>
 
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 -   Machine Learning Crash Course with TensorFlow APIs,
     <https://developers.google.com/machine-learning/crash-course/> This
     is a great course published by Google\'s. It is advertised as a \'A
     self-study guide for aspiring machine learning practitioners\'
 
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 -   Machine Learning Guides, Simple step-by-step walkthroughs to solve
     common machine learning problems using best practices ,
     <https://developers.google.com/machine-learning/guides/>
 
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 -   Machines that Learn in the Wild - Machine learning capabilities,
     limitations and implications,
     <https://media.nesta.org.uk/documents/machines_that_learn_in_the_wild.pdf>
 
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 -   Mathematics for Machine Learning, <https://mml-book.github.io/>
     Examples and tutorials for this book are placed on:
     <https://github.com/mml-book/mml-book.github.io>
 
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 -   Mathematics for Machine Learning, Garrett Thomas. Introductory class
     in machine learning from UC Berkeley(course CS 189/289A). See
     <https://gwthomas.github.io/docs/math4ml.pdf>
 
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 -   Practical Deep Learning for Coders v3,
     <https://course.fast.ai/index.html>
 
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 -   Python Machine Learning course,
     <https://machine-learning-course.readthedocs.io/en/latest/index.html>
 
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 -   Privacy Preserving Deep Learning with PyTorch & PySyft, Tutorial
     with Jupyter notebooks based on PySyft library,
     <https://github.com/OpenMined/PySyft/tree/master/examples/tutorials>
 
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 -   Rules of Machine Learning: Best Practices for ML Engineering, cc-by
     licensed ML course developed by Google,
     <https://developers.google.com/machine-learning/guides/rules-of-ml>
 
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 -   Scikit-learn User Guide,
     <https://scikit-learn.org/stable/user_guide.html>
 
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 -   scikit-learn Tutorials,
     <https://scikit-learn.org/stable/tutorial/index.html>
 
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 -   Seeing Theory, A visual introduction to probability and statistics.
     Interactive learning book that visualizes the fundamental
     statistical concepts, <https://seeing-theory.brown.edu/>
 
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 -   Spinning Up in Deep RL, become a skilled practitioner in deep
     reinforcement learning,
     <https://spinningup.openai.com/en/latest/index.html>
 
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 -   The Elements of AI, learn the basics of AI,
     <https://www.elementsofai.com/>
 
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 -   TensorFlow, Keras and deep learning, without a PhD,
     <https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#0>
 
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NLPlearningresources.md

@@ -12,20 +12,13 @@ licenses are included. So all references are open access resources.
 
 -  Natural Language Processing with Python, <http://www.nltk.org/book/>   
 
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 -  Advanced NLP with spaCY, <https://course.spacy.io/>    
 
 
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 -  NLP concepts with spaCy (notebook), <https://gist.github.com/nocomplexity/b7c4c0aa5a0b53f4f5ff1c4784084be6>   
 
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_toc.yml

@@ -1,39 +1,34 @@
-- file: abstract
-
-- part: Core Concepts
+format: jb-book
+root: abstract
+parts:
+- caption: Core Concepts
   chapters:
   - file: preface
   - file: introduction
   - file: whyossml
   - file: whatisml
   - file: ml-business-use
   - file: architecture
-  - file: risks   
+  - file: risks
   - file: nlp
-  - file: ml-challenges 
-
-- part: FOSS ML Software
+  - file: ml-challenges
+- caption: FOSS ML Software
   chapters:
   - file: catalogue
     sections:
-      - file: mlframeworks
-      - file: mlcomputervision
-      - file: mltools
-      - file: mlhosting
-      - file: nlpframeworks
-
-- part: Open References
+    - file: mlframeworks
+    - file: mlcomputervision
+    - file: mltools
+    - file: mlhosting
+    - file: nlpframeworks
+- caption: Open References
   chapters:
   - file: MLlearningresources
   - file: NLPlearningresources
-
-- part: About
+- caption: About
   chapters:
   - file: help
   - file: about
   - file: license
   - url: https://www.amazon.com/Free-Machine-Learning-Maikel-Mardjan/dp/B0863S9LQ5/ref=sr_1_2?qid=1585488090&refinements=p_27%3AMaikel+Mardjan&s=books&sr=1-2&text=Maikel+Mardjan
-    title: 'Support this project:Buy a hardcopy version!'
-
-
-
+    title: Support this project:Buy a hardcopy version!