updates for 2021 version

Author: nocomplexity

MLlearningresources.md

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+ML Learning resources
+=====================
+
+Learning machine learning does not have to be very expensive or time
+consuming. Great learning material for machine learning is licensed
+under a Creative Commons license. For starters but also people who are
+already more familiar with the key concepts.
+
+This section presents an opinionated list of great machine learning
+learning resources. A lot of garbage is produced on the internet and
+even paid courses are often not that good. But most material released
+under an open license is of excellent quality. This list consist of very
+readable references and some great hands-on courses.
+
+Only resources that are real open, so resources published using a
+Creative Commons license (cc-by mostly) or other types of real open
+licensed material is included.
+
+Most learning resources include hands-on tutorials. So be ready to use a
+notebook, but most tutorials offer notebooks ready to use directly.
+
+-   A Course in Machine Learning, <http://ciml.info/>
+
+| 
+
+-   AutoML: Methods, Systems, Challenges,
+    <https://www.ml4aad.org/wp-content/uploads/2019/05/AutoML_Book.pdf>
+
+| 
+
+-   Building Safe A.I., A Tutorial for Encrypted Deep Learning,
+    <https://iamtrask.github.io/2017/03/17/safe-ai/>
+
+| 
+
+-   Collection of Interactive Machine Learning Examples,
+    <https://aihub.cloud.google.com/s?category=notebook>
+
+| 
+
+-   Cryptography and Machine Learning, Mixing both for
+    privacy-preserving machine learning, <https://mortendahl.github.io/>
+
+| 
+
+-   Dive into Deep Learning, An interactive deep learning book with
+    code, math, and discussions, <https://d2l.ai/>
+
+| 
+
+-   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>
+
+| 
+
+-   Foundations of Machine Learning, Understand the Concepts, Techniques
+    and Mathematical Frameworks Used by Experts in Machine Learning,
+    <https://bloomberg.github.io/foml/#home>
+
+| 
+
+-   Interpretable Machine Learning, A Guide for Making Black Box Models
+    Explainable,Christoph Molnar,
+    <https://christophm.github.io/interpretable-ml-book/>
+
+| 
+
+-   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\'
+
+| 
+
+-   Machine Learning Guides, Simple step-by-step walkthroughs to solve
+    common machine learning problems using best practices ,
+    <https://developers.google.com/machine-learning/guides/>
+
+| 
+
+-   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>
+
+| 
+
+-   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>
+
+| 
+
+-   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>
+
+| 
+
+-   Practical Deep Learning for Coders v3,
+    <https://course.fast.ai/index.html>
+
+| 
+
+-   Python Machine Learning course,
+    <https://machine-learning-course.readthedocs.io/en/latest/index.html>
+
+| 
+
+-   Privacy Preserving Deep Learning with PyTorch & PySyft, Tutorial
+    with Jupyter notebooks based on PySyft library,
+    <https://github.com/OpenMined/PySyft/tree/master/examples/tutorials>
+
+| 
+
+-   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>
+
+| 
+
+-   Scikit-learn User Guide,
+    <https://scikit-learn.org/stable/user_guide.html>
+
+| 
+
+-   scikit-learn Tutorials,
+    <https://scikit-learn.org/stable/tutorial/index.html>
+
+| 
+
+-   Seeing Theory, A visual introduction to probability and statistics.
+    Interactive learning book that visualizes the fundamental
+    statistical concepts, <https://seeing-theory.brown.edu/>
+
+| 
+
+-   Spinning Up in Deep RL, become a skilled practitioner in deep
+    reinforcement learning,
+    <https://spinningup.openai.com/en/latest/index.html>
+
+| 
+
+-   The Elements of AI, learn the basics of AI,
+    <https://www.elementsofai.com/>
+
+| 
+
+-   TensorFlow, Keras and deep learning, without a PhD,
+    <https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#0>
+
+| 

NLPlearningresources.md

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+NLP Learning resources
+======================
+
+There is a large overlap between machine learning and current NLP
+technology. But is makes sense to outline specific NLP resources
+separate. This to make searching for good open NLP resources easier.
+
+So this section is an opinionated list of great NLP learning resources.
+Of course also only resources that are open, so only resources published
+using a Creative Commons license (cc-by mostly) or other real open
+licenses are included. So all references are open access resources.
+
+-   Natural Language Processing with Python, <http://www.nltk.org/book/>
+
+| 
+
+-   Advanced NLP with spaCY, <https://course.spacy.io/>
+
+| 
+
+-   NLP concepts with spaCy (notebook)
+    <https://gist.github.com/nocomplexity/b7c4c0aa5a0b53f4f5ff1c4784084be6>
+
+| 

about.md

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+About
+=====
+
+This publication to fight for real Free and Open Machine learning is
+initially started and created by Maikel Mardjan. Maikel is a hands-on
+practical business IT architect and loves to make simple designs for
+complex IT systems. Maikel has more than 25 years of relevant experience
+on various IT roles in famous (international) companies. Maikel holds
+both a Master (Msc) Business Studies of University of Groningen
+(<https://www.rug.nl/>) and a Master degree (Msc) Electrical
+Engineering, of Delft University of Technology
+(<https://www.tudelft.nl/en/>). Maikel is TOGAF 9 Certified and CISSP
+(Certified Information Systems Security Professional) certified. Maikel
+is also an OWASP member (<https://owasp.org/>) and supporter.
+
+Check <https://nocomplexity.com> for more information about Maikel.
+
+Machine learning is a complex technology. So we need simple and Free and
+Open solutions to create applications so that will solve complex
+problems we humans face. To trust machine learning applications there is
+simply no other option than using fully transparent technologies. So
+Free and Open in the spirit of the Free Software Foundation
+(<https://fsf.org>).
+
+If you or your company is committed to openness make sure to support the
+BM-Support.org Foundation. Supporting this foundation is free! Check
+<https://www.bm-support.org/join/>
+
+This publication would never have reached version 1.0 without your help.
+So I gratefully thank all people who devote time and knowledge to give
+input to this publication. Will we continue this FOSS machine learning
+journey so machine learning technology will stay Free and Open so
+everyone can benefit.

abstract.md

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+# Free and Open Machine Learning
+
+
+## Abstract
+
+
+This publication is created to promote and advocate the use of FOSS
+machine learning for real practical business use cases. Machine learning
+is a fascinating technology. Free and Open machine learning should be
+the norm for business innovation. So simple to use for complex problems.
+Freedom to control machine learning technology is not self-evident. Free
+and Open Machine Learning puts you in full control.
+
+This publication empowers everyone to make a head start using the
+powerful machine learning technology in a Free, Open and Simple way.
+
+```{note}
+This is a living document. A stable version of this publication (version
+1.0) is available as hard copy. You can order it at Amazon, click
+[here](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)
+to order. So **support** this project and buy a hard copy!
+```
+
+
+Machine learning is an exciting and powerful technology. The continuous
+use and growth of machine learning technology opens new opportunities.
+It also enables solving complex problems in a simple way. Problems that
+are impossible to solve by using traditional software technologies. This
+great machine learning technology should available for everyone. This
+means that everyone should be able to learn, play and create great
+applications using machine learning technology. But also reuse existing
+machine learning solutions, inspect solutions and improve solutions of
+others. Without borders or strings attached.
+
+The key focus of this publication in on Free and Open Machine Learning
+technologies. This to remove barriers for learning, playing, using and
+reusing machine learning technologies for real practical use cases for
+everyone.
+
+Of course you can use or switch to cloud company solutions to deploy
+your machine learning driven application in production. But besides
+vendor lock-in, crucial aspects like safety, privacy and security for
+machine learning applications are only possible when using fully
+transparent Free and Open machine learning building blocks and
+solutions.
+
+This document describes an open machine learning architecture. Including
+key aspects that are involved for real business use. This means e.g.
+that we focus on FOSS machine learning software and open datasets.
+
+Since the majority of humans are not a graduated mathematician, we skip
+deep mathematical background concepts of machine learning algorithms in
+this publication. Good books with lots of mathematical background
+information on how machine learning works are available for more than 70
+years. There are plenty excellent free and open publications available
+if you want to learn everything about the inner working of the
+mathematical algorithms that power the current exciting machine learning
+applications. In the learning resources section in this publication you
+can find a list of good references. All references in this publication
+are publications available under a creative commons license (cc-by).
+
+This publication has a core focus on outlining how Free and Open machine
+learning can be used for real business use cases. This is done by
+describing:
+
+-   Key machine learning concepts. The focus is on concepts that are
+    needed in order to use solid FOSS machine learning frameworks and
+    datasets when creating a machine learning powered application.
+-   An open reference architecture for creating and maintaining a
+    machine learning solution architecture and IT landscape.
+-   Presenting useful and most used FOSS machine learning building
+    blocks. Most Open Solution Building Blocks for machine learning are
+    FOSS based. The most used solutions have a healthy ecosystem of
+    (open) tools and service companies that enables you to create your
+    machine learning application as fast as possible.
+-   Key quality aspects for engineering and maintaining your machine
+    learning application.
+-   Important safety, privacy and security aspects to prevent disasters.
+-   Ethical issues (like bias) and guidelines for handling these issues
+    in a transparent way.
+
+No pieces of program code or mathematical formulas is presented in this
+publication. The emphasis is on machine learning concept and applying
+machine learning technology for real business use cases. No programming
+knowledge is needed to enjoy and learn machine learning.
+
+This publication is created to give you a head start with using Free and
+Open machine learning technology to solve your business problems.
+Without any strings attached, so the focus is on Free and Open
+transparent machine learning technologies and solutions only!
+
+```{warning}
+This document is a living document! Collaboration is fun, so
+**Help Us** by contributing ! Some more background information of the project can be found in [the readme on github.com](https://github.com/nocomplexity/FreeAndOpenMachineLearning).
+And do not forget to join the [ROI movement!](https://www.bm-support.org/projects/)
+```
+
+![image](/images/coverimage.png)
+