updates

Author: nocomplexity

MLlearningresources.md

@@ -19,59 +19,10 @@ 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/>
 
 +++
 
-- Adversarial Robustness - Theory and Practice.This tutorial seeks to provide a broad, hands-on introduction to this topic of adversarial robustness in deep learning. The goal is combine both a mathematical presentation and illustrative code examples that highlight some of the key methods and challenges in this setting. With this goal in mind, the tutorial is provided as a static web site, but all the sections are also downloadable as Jupyter Notebooks. Check the course [Adversarial Robustness - Theory and Practice](https://adversarial-ml-tutorial.org/)
 
-+++
-
--   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>
-
-+++
-- Fairness and machine learning, Limitations and Opportunities by Solon Barocas, Moritz Hardt, Arvind Narayanan, https://fairmlbook.org/index.html
-
- +++
-
--   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

_toc.yml

@@ -24,6 +24,7 @@ parts:
 - caption: Courses
   chapters:
   - file: mlcourses
+  - file: ml-pdfbooks
 - caption: Open References
   chapters:
   - file: MLlearningresources

ml-pdfbooks.md

@@ -0,0 +1,26 @@
+# ML Books 
+
+This section presents an opinionated list of great machine learning
+learning resources available in PDF format. 
+
+Of course all [open](http://opendefinition.org/od/2.1/en/).
+
+
+```{admonition} AutoML: Methods, Systems, Challenges.
+:class: tip, dropdown
+
+Check this [Springer Book](https://www.ml4aad.org/wp-content/uploads/2019/05/AutoML_Book.pdf).
+
+```
+
+
+```{admonition} Explainable Deep Learning: A Field Guide for the Uninitiated.
+:class: tip, dropdown
+Great    learning guide for new and starting researchers in the Deep neural    network (DNN) field. 
+
+Check this [Guide at ArXiV](https://arxiv.org/pdf/2004.14545.pdf).
+
+```
+
+
+    

mlcourses.md

@@ -1,12 +1,49 @@
 # ML courses
 
+Everyone in the world should have access to high-quality machine learning resources. This to empower Free and Open Machine Learning. 
+
+This list of [open](http://opendefinition.org/od/2.1/en/) (Creative Commons licensed ) machine learning training resources contains resources for starters who never want to do ‘hands-on’. Openness for knowledge sharing means no user registration to read or play with the material is required.
+
+Never stop learning.
+
 
 
 ::::{grid} 3
 :class-container: text-center
 :gutter: 3
 
 
+:::{grid-item-card} 
+:link: https://d2l.ai/
+{octicon}`book;1em;caption-text` **Dive into Deep Learning**
+^^^
+```{image} https://d2l.ai/_images/front.png
+:height: 100px
+```
+Interactive deep learning book with code, math, and discussions. Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow.
+
+Adopted at 400 universities from 60 countries
+
++++
+[Check this Course Book »](https://d2l.ai/)
+:::
+
+
+:::{grid-item-card} 
+:link: https://christophm.github.io/interpretable-ml-book/
+{octicon}`book;1em;caption-text` **Interpretable Machine Learning**
+^^^
+```{image} https://christophm.github.io/interpretable-ml-book/images/cutout.png
+:height: 100px
+```
+A Guide for Making Black Box Models Explainable
+
++++
+[Check this Course Book »](https://christophm.github.io/interpretable-ml-book/)
+:::
+
+
+
 :::{grid-item-card} 
 :link: https://www.tomasbeuzen.com/deep-learning-with-pytorch/README.html
 {octicon}`book;1em;caption-text` **Deep Learning with PyTorch**
@@ -48,14 +85,6 @@ A course designed for people with some coding experience, who want to learn how
 [Check this course »](https://course.fast.ai/)
 :::
 
-::::
-
-% another row
-
-::::{grid} 3
-:class-container: text-center
-:gutter: 3
-
 
 :::{grid-item-card}
 :link: https://mlc.ai/index.html
@@ -71,4 +100,92 @@ Learn the key abstractions to represent machine learning programs, automatic opt
 [Check this course »](https://mlc.ai/index.html)
 :::
 
+:::{grid-item-card}
+:link: https://bronwojtek.github.io/neuralnets-in-raw-python/docs/index.html
+{octicon}`book;1em;caption-text` **Explaining neural networks in raw Python:lectures in Jupiter**
+^^^
+```{image} https://bronwojtek.github.io/neuralnets-in-raw-python/_static/koh.png
+:height: 100px
+```
+The goal of this course is to teach some basics of the omnipresent neural networks with Python.
+
+*The text is brief so you can complete the course in a few afternoons!*
+
++++
+[Check this course »](https://bronwojtek.github.io/neuralnets-in-raw-python/docs/index.html)
+:::
+
+
+:::{grid-item-card}
+:link: https://bait509-ubc.github.io/BAIT509/intro.html
+{octicon}`book;1em;caption-text` **BAIT509 - Business Applications of Machine Learning**
+^^^
+```{image} https://bait509-ubc.github.io/BAIT509/_static/bait_logo.png
+:height: 100px
+```
+Introduction to machine learning concepts, such as model training, model testing, generalization error and overfitting.
+
+*Course given at University of British Columbia*
+
++++
+[Check this course »](https://bait509-ubc.github.io/BAIT509/intro.html)
+:::
+
+
+:::{grid-item-card}
+{octicon}`book;1em;caption-text` **DEEP LEARNING(with PyTorch)**
+^^^
+```{image} https://atcold.github.io/pytorch-Deep-Learning/images/week11/11-3/figure1.png
+:height: 100px
+```
+Supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.
+
+*Course from NYU CENTER FOR DATA SCIENCE, advanced course*
+
+[Repository (with notebooks)](https://github.com/Atcold/pytorch-Deep-Learning)
+
++++
+[Check this course »](https://atcold.github.io/pytorch-Deep-Learning/)
+:::
+
+:::{grid-item-card}
+:link: https://dafriedman97.github.io/mlbook/content/introduction.html
+{octicon}`book;1em;caption-text` **Machine Learning from Scratch**
+^^^
+```{image} https://dafriedman97.github.io/mlbook/_images/logo_light.png
+:height: 100px
+```
+This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers.
+
++++
+[Check this course »](https://dafriedman97.github.io/mlbook/content/introduction.html)
+:::
+
+:::{grid-item-card}
+:link: https://fairmlbook.org/index.html
+{octicon}`book;1em;caption-text` **Fairness and machine learning, Limitations and Opportunities**
+^^^
+```{image} https://fairmlbook.org/assets/causal-collider.svg
+:height: 100px
+```
+If machine learning is our way into studying institutional decision making, fairness is the moral lens through which we examine those decisions.
+
++++
+[Read this course book »](https://fairmlbook.org/index.html)
+:::
+
+
+:::{grid-item-card}
+:link: https://bloomberg.github.io/foml/#home
+{octicon}`book;1em;caption-text` **Foundations of Machine Learning**
+^^^
+```{image} https://bloomberg.github.io/foml/images/mlbanner.jpg
+:height: 100px
+```
+Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning
++++
+[Read this course book »](https://bloomberg.github.io/foml/#home)
+:::
+
+
 ::::