• There’s more to data than distributions
  Distribution shift is a fundamental challenge in machine learning, and whenever we attempt to deploy machine learning in the real world without considering the way in which that real world environment can change (whether its changes in technology (e.g., software vendors)), changes in population and setting (e.g., new demographics), or changes in behavior (e.g., new reimbursement incentives), then we fail to properly consider the ways in which the data can change or shift between train and test environments. If not considered, the model will inevitably fail....

• What I learned from the Data Council 2022 data science conference
  I attended the data science/data engineering conference Data Council 2022 this week. This is my first time attending this conference. One thing special about this conference is that there are a lot of startups and investors. There are several interesting themes we noticed...

• σ-driven project management: when is the optimal time to give up?
  Hi! It's your friendly project management theorician. You might remember me from blog posts such as "Why software projects take longer than you think", which is a blog post I wrote a long time ago positing that software projects completion time follow a log-normal distribution...My thesis for this blog post is that σ is an inherent property of the type of risk you have in your project portfolio, and that different values for σ warrants very different types of project management...

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• Exploring Neural Networks Visually in the Browser
  What I wanted for neural networks: A constrained, simplified environment for building basic network topologies and experimenting live to see visually how different layer counts, sizes, activation functions, hyperparameters, etc. impact their functionality and performance...With this goal in mind, I created a browser-based tool for building, training, visualizing, and experimenting with neural networks. Since it runs on the web, I've embedded it directly in this post...

• Improving forecasting by learning quantile functions
  Learning the complete quantile function, which maps probabilities to variable values, rather than building separate models for each quantile level, enables better optimization of resource trade-offs...

• Can Computers Learn Common Sense?
  A.I. researchers are making progress on a long-term goal: giving their programs the kind of knowledge we take for granted....

• Tech Apprenticeships are opening everywhere! [Twitter Thread]
  Tech Apprenticeships are opening everywhere! They are opportunities to pivot to a tech career whether you have a degree or not......Apprenticeships pay YOU to learn the skills while you work and gain mentorship from others...🥳...Here are some paying upwards of $3-6K per month.🧵...

• Efficiently Initializing Reinforcement Learning With Prior Policies
  A key challenge in Reinforcement Learning is learning policies from scratch in environments with complex tasks. Read how a meta-algorithm, Jump Start Reinforcement Learning, uses prior policies to create a learning curriculum that improves performance...

• Andrew Ng: Unbiggen AI
  Google Brain co-founder and Landing AI founder Andrew Ng has become an evangelist for what he calls the data-centric AI movement. “Collecting more data often helps," he says. "But if you try to collect more data for everything, that can be a very expensive activity.”...

• Javis.jl - Julia Animations and Visualizations [Video]
  Javis.jl is a general purpose animation library which builds on top of the Luxor.jl graphics library...It fills a gap in the Julia ecosystem by providing functionality to create object based animations to communicate complex ideas through simple means. Furthermore, Javis provides the flexibility for users to extend Javis’s visualizations to a variety of applications. Users are already expressing complicated ideas through winsome domain specific visuals such as planetary motion or brain mapping....

• Everything gets a package? Yes, everything gets a package.
  I recently read Ethan Rosenthal's Python Data Science setup, and I loved every line of it. His blog post encapsulates some of the best of the ideas that I wrote about in my Data Science Bootstrap knowledge base. In this blog post, I'd like to share my own data science setup and some new tooling that I've been working on to make it easy to achieve the same goals as I have...

• Mushrooms communicate with each other using up to 50 ‘words’, scientist claims
  Professor theorises electrical impulses sent by mycological organisms could be similar to human language...

• Institute for AI & Fundamental Interactions Colloquium Series: A path towards human-level intelligence [Video]
  Yann LeCun, VP and Chief AI Scientist, Meta talks about “A path towards human-level intelligence”...

• Best of the visualisation web...December 2021
  Since 2010 I have compiled and published monthly collections of links to some of the best, most interesting, or thought-provoking data visualisation-related content I come across. These collections are not always published immediately after the month in question has ended, but I try to do so as soon as my workload permits!...Here's a collection of some of the best content I encountered during December 2021...

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• How to Make Your Pandas Code Run Faster - Two Pandas Tricks I Wish I’d Known Earlier
  Most of us data scientists, come to use Pandas library at some point of our work. This post is going to present 2 tricks that will make your pandas code run faster. The first is for removing None values, and the second regards extracting a set of values from a certain column...

• Complete Step-by-step Particle Swarm Optimization Algorithm from Scratch
  And its implementation for solving a nonlinear control theory problem...

• K-Medoid Clustering (PAM) Algorithm in Python
  A step-by-step tutorial—with a solved example...

• Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
  Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems...
  
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...

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P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian