I don’t know how CPUs work so I simulated one in code
HN
But How Do It Know? is the breakthrough book that distills Computer Science down to the simple basic principles that all computers have in common,
and presents the big picture step by step in plain English so that anyone can understand this ingenious, yet basically simple invention called a
computer. bol.com 18,99
Formal probability theory is a rich and complex field of mathematics with a reputation for being confusing if not outright impenetrable. Much of that intimidation, however, is due not to the abstract mathematics but rather how they are employed in practice. In particular, many introductions to probability theory sloppily confound the abstract mathematics with their practical implementations, convoluting what we can calculate in the theory with how we perform those calculations. To make matters even worse, probability theory is used to model a variety of subtlely different systems, which then burdens the already confused mathematics with the distinct and often conflicting philosphical connonations of those applications.
The course — Data 8X (Foundations of Data Science) — covers everything from testing hypotheses, applying statistical inferences, visualizing distributions and drawing conclusions, all while coding in Python and using real-world data sets. One lesson might take economic data from different countries over the years to track global economic growth. The next might use a data set of cell samples to create a classification algorithm that can diagnose breast cancer. (Learn more from a video on the Berkeley data science website.)
This field is referred to as geospatial analysis. Geospatial analysis applies statistical analysis to data that has geographical or geometrical components. In this tutorial, we’ll use Python to learn the basics of acquiring geospatial data, handling it, and visualizing it. More specifically, we’ll do some interactive visualizations of the United States!
https://www.twilio.com/blog/2017/08/geospatial-analysis-python-geojson-geopandas.html
HN comments:
If you read data science articles, you may have already stumbled upon FiveThirtyEight’s content. Naturally, you were impressed by their awesome visualizations. You wanted to make your own awesome visualizations and so asked Quora and Reddit how to do it. You received some answers, but they were rather vague. You still can’t get the graphs done yourself.
About this course: The last three or four decades have seen a remarkable evolution in the institutions that comprise the modern monetary system. The financial crisis of 2007-2009 is a wakeup call that we need a similar evolution in the analytical apparatus and theories that we use to understand that system. Produced and sponsored by the Institute for New Economic Thinking, this course is an attempt to begin the process of new economic thinking by reviving and updating some forgotten traditions in monetary thought that have become newly relevant.
https://www.coursera.org/learn/money-banking
More:
For a quick video intro I recommend this video from 3Blue1Brown (“youtube mathematician”) https://www.youtube.com/watch?v=bBC-nXj3Ng4
IMHO the best intro to Bitcoin and cryptocurrencies in general.
Between a full time job and a toddler at home, I spend my spare time learning about the ideas in cognitive science & AI. Once in a while a great paper/video/course comes out and you’re instantly hooked.
Andrew Ng’s new deeplearning.ai course is like that Shane Carruth or Rajnikanth movie that one yearns for!
Naturally, as soon as the course was released on coursera, I registered and spent the past 4 evenings binge watching the lectures, working through quizzes and programming assignments.
Machine learning (Coursera)
Deep learning specialization (Coursera)