Facebook’s Field Guide to Machine Learning video series
#guide #facebook
https://research.fb.com/the-facebook-field-guide-to-machine-learning-video-series/
#guide #facebook
https://research.fb.com/the-facebook-field-guide-to-machine-learning-video-series/
Series of articles «Machine Learning Kaggle Competition» in three parts:
1. Part I: https://towardsdatascience.com/machine-learning-kaggle-competition-part-one-getting-started-32fb9ff47426
2. Part II: https://towardsdatascience.com/machine-learning-kaggle-competition-part-two-improving-e5b4d61ab4b8
3. Part III: https://towardsdatascience.com/machine-learning-kaggle-competition-part-three-optimization-db04ea415507
#kaggle #guide
1. Part I: https://towardsdatascience.com/machine-learning-kaggle-competition-part-one-getting-started-32fb9ff47426
2. Part II: https://towardsdatascience.com/machine-learning-kaggle-competition-part-two-improving-e5b4d61ab4b8
3. Part III: https://towardsdatascience.com/machine-learning-kaggle-competition-part-three-optimization-db04ea415507
#kaggle #guide
Medium
Machine Learning Kaggle Competition Part One: Getting Started
Learning the Kaggle Environment and an Introductory Notebook
A Guide for Making Black Box Models Explainable.
One of the biggest challenges is to make ML models interpretable (explainable to human, preferably, non-expert). It matters not only in terms of credit scoring, to exclude possibility of racism or any other bias or news promotion and display (Cambridge Analytica case), but even in terms of debug and further progress in model training.
Link: https://christophm.github.io/interpretable-ml-book/
#guide #interpretablelearning #IL
One of the biggest challenges is to make ML models interpretable (explainable to human, preferably, non-expert). It matters not only in terms of credit scoring, to exclude possibility of racism or any other bias or news promotion and display (Cambridge Analytica case), but even in terms of debug and further progress in model training.
Link: https://christophm.github.io/interpretable-ml-book/
#guide #interpretablelearning #IL
christophm.github.io
Interpretable Machine Learning
Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners to make machine learning decisions interpretable.
Forwarded from Archived GitHub
Testing Python Applications with Pytest.
Pytest is a testing framework and test runner for Python. In this guide we will have a look at the most useful and common configuration and usage, including several pytest plugins and external libraries. Although Python comes with a unittest module in the standard library and there are other Python test frameworks like nose2 or Ward, pytest remains my favourite. The beauty of using simple functions instead of class hierarchies, one simple
#guide #testing #python #pytest
Pytest is a testing framework and test runner for Python. In this guide we will have a look at the most useful and common configuration and usage, including several pytest plugins and external libraries. Although Python comes with a unittest module in the standard library and there are other Python test frameworks like nose2 or Ward, pytest remains my favourite. The beauty of using simple functions instead of class hierarchies, one simple
assert instead of many different assert functions, built-in parametrized testing, a nice system of fixtures and the number of available plugins makes it a pleasure to use.#guide #testing #python #pytest