Personal reading list of non-fiction books

Books I am currently reading A Promised Land by Barack Obama Link to book Books I have read The Alchemy of Air by Thomas Hager (Link to book) Superintelligence: Paths, Dangers, Strategies by Nick Bostrom (Link to book) Beethoven, A Life by Jan Caeyers (Link to book) Deep Work: Rules for Focused Success in a Distracted World by Cal Newport (Link to book)

February 14, 2021 Read
Deep Work

I recently finished reading the book Deep Work by Cal Newport. For years, I have been interested in the question of how we can improve our learning and work habits. There is a ton of research on this topic and many great resources (like, for example, the podcast The Learning Scientists). I don’t want to use this blog post to talk about all the different things I have learned and tried in the past years.

December 18, 2020 Read
Mocking in Python

Topics: Mocking in Python Today, I want to talk about mocking. I became interested in this topic a few months back when I started to work in a data engineering project at work. In this project we have a lot of tests that relies on mocking to test code with external dependencies. If you don’t like reading long blog posts, consider listening to one of the podcast episodes I did on this topic: there is one at Talk Python to Me and the other at Test and Code.

October 11, 2020 Read
Variational Inference

Introduction Variational inference is an important topic that is widely used in machine learning. For example, it’s the basis for variational autoencoders. Also Bayesian learning often makes use variational of inference. To understand what variational inference is, how it works and why it’s useful we will go through each point step by step. What are latent variables? A latent variable is the opposite of an observed variable. This means that a latent variable is not directly observed but inferred from other variables which are observed.

February 23, 2019 Read
Kullback-Leibler Divergence

One of the points on my long ‘stuff-you-have-to-look-at’ list is the Kullback-Leibler divergence. I finally took the time to take a detailed look at this topic. Definition The KL-divergence is a measure of how similar (or different) two probablity distributions are. When having a discrete probability distribution $P$ and another probability distribution $Q$ the KL-divergence for a set of points $X$ is defined as: $$D_{KL}(P ,|| ,Q) = \sum_{x \in X} P(x) \log \big( \frac{P(x)}{Q(x)} \big)$$

February 2, 2019 Read
An introduction to Docker

Topics: Docker I have been wanting to learn more about Docker for months. However, when starting to read Dockers get started page I quickly had to find out that I’m lacking knowledge in too many other concepts. Since my background isn’t computer science I often run across this problem. Luckily, this allows me to constantly learn new things! After writing a post on virtual machines and another about containers this one is finally about Docker!

November 10, 2018 Read
An introduction to containers

Topics: Containers I have been wanting to learn more about Docker for months. However, when starting to read Dockers get started page I quickly had to find out that I’m lacking knowledge in too many other concepts. Since my background isn’t computer science I often run across this problem. Luckily, this allows me to constantly learn new things! With the goal of eventually reaching the topic of Docker, the first post introduced virtual machines.

October 7, 2018 Read
An introduction to virtual machines

Topics: Virtual machines I have been wanting to learn more about Docker for months. However, when starting to read Dockers get started page I quickly had to find out that I’m lacking knowledge in too many other concepts. Since my background isn’t computer science I often run across this problem. Luckily, this allows me to constantly learn new things! With the goal of eventually reaching the topic of Docker, this post will introduce virtual machines, while the next one will be about containers.

September 29, 2018 Read
Wrap up

Topics: Wrap up During the past 50 days we studied several highly useful Python concepts using a Magical Universe. Overall, we looked at the following concepts: General Python concepts: Object oriented programming, how to use classes, how inheritance works, etc. The differences between class, instance and static methods How to convert objects to strings How to properly name variables and functions The ‘It’s easier to ask for forgiveness than permission’ EAFP principle The functioning of if __name__ == '__name__' Testing code with pytest Advanced Python concepts: How to use function annotations What decorators are and how we can use them, e.

September 16, 2018 Read
Config files

Topics: Using config files What are config files As a last topic I would like to take a look at the usage of config files. First of all we have to establish what a config file is. The Wikipedia definition definition is precise and easy to understand: configuration files (or config files) are files used to configure the parameters and initial settings for some computer programs. In our case we can use a config file to organize and speed up the process of creating members of our magical universe.

September 10, 2018 Read
collections.defaultdict

Topics: collections.defaultdict Updated 2020-10-06 The collections module The collections module in Python contains several useful classes. One of them is especially helpful for our Magical Universe: collections.defaultdict. When defining our CastleKilmereMember class we specified self.traits to be an empty dictionary. New positive and negative traits can be added to a person using the add_trait() function. We can check whether a person possesses a certain trait using the exhibits_trait() function. The relevant parts of the class look as follows:

September 7, 2018 Read
functools.wraps - avoiding losing metdata when applying decorators

Topics: functools.wraps We have discussed decorators on day 5, day 6 and day 20 already. Therefore, we know that decorators allow us to extend or modify the behavior of a function without permanently modifying the function itself. In our example we modified the says() function of our CastleKilmereMember class to be whispering instead of talking normally: class CastleKilmereMember: """ Creates a member of the Castle Kilmere School of Magic """ def __init__(self, name: str, birthyear: int, sex: str): self.

September 6, 2018 Read