Google Brain researchers published this amazing paper, with accompanying GIF where they show the true power of AutoML.
AutoML stands for automated machine learning, and basically refers to an algorithm autonomously building the best machine learning model for a given problem.
This task of selecting the best ML model is difficult as it is. There are many different ML algorithms to choose from, and each of these has many different settings ([hyper]parameters) you can change to optimalize the model’s predictions.
For instance, let’s look at one specific ML algorithm: the neural network. Not only can we try out millions of different neural network architectures (ways in which the nodes and lyers of a network are connected), but each of these we can test with different loss functions, learning rates, dropout rates, et cetera. And this is only one algorithm!
In their new paper, the Google Brain scholars display how they managed to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. Using evolutionary principles, they have developed an AutoML framework that tailors its own algorithms and architectures to best fit the data and problem at hand.
This is AI research at its finest, and the results are truly remarkable!
Both in science and business, we often experience difficulties collecting enough data to test our hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs.
Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the questions we’re really interested in.
This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample studies. Each chapter illustrates statistical methods that allow researchers and analysts to apply the optimal statistical model for their research question when the sample is too small.
This book will enable anyone working with data to test their hypotheses even when the statistical model required for answering their questions are too complex for the sample sizes they can collect. The covered statistical models range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R.
I came across this opinionated though informed commentary by Vinay Prasad on the recent Nature article where Google’s machine learning experts trained models to predict whether scans of patients’ breasts (mammogram’s) show cancerous cells or not.
Vinay Prasad [official bio] is a practicing hematologist-oncologist and Associate Professor of Medicine at Oregon Health and Science University. So he knows what he’s talking about.
He argues that “cancer screening is the LAST thing you should pick FIRST to work on with AI”. Which is an interesting statement in and of itself.
Regardless of my personal opinion on the topic, I found the paper, Vinay’s commentary, and the broader discussion on twitter very interesting and educational to read. I feel it shows how important it is to know the context in which you are applying machine learning. What tremendous value it provides to have domain experts in the same team as the data and machine learning experts.
The “world wide web” hosts millions of datasets, on nearly any topic you can think of. Google’s Dataset Search has indexed almost 25 million of these datasets, giving you a single entry point to search for datasets online. After a year of testing, Dataset Search is now officially out of beta.
After alpha testing, Dataset Search now includes filter based on the types of dataset that you want (e.g., tables, images, text), on whether the dataset is open source/access. For dataset on geographic area’s, you can see the map. The quality of dataset’s descriptions has improved greatly, and the tool now has a mobile version.
This 13-page guide by the American Society of Plant Biologists was recommended by some, but I personally don’t find it as useful as the other advices here. Nevertheless, for the laymen, it does include a nice visualization of the anatomy of scientific papers:
This course gives you easy access to the invaluable learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines. We’ll learn about the how the brain uses two very different learning modes and how it encapsulates (“chunks”) information. We’ll also cover illusions of learning, memory techniques, dealing with procrastination, and best practices shown by research to be most effective in helping you master tough subjects.
Last week I cohosted a professional learning course on data visualization at JADS. My fellow host was prof. Jack van Wijk, and together we organized an amazing workshop and poster event. Jack gave two lectures on data visualization theory and resources, and mentioned among others treevis.net, a resource I was unfamiliar with up until then.
treevis.net is a lot like the dataviz project in the sense that it is an extensive overview of different types of data visualizations. treevis is unique, however, in the sense that it is focused on specifically visualizations of hierarchical data: multi-level or nested data structures.
Hans-Jörg Schulz — professor of Computer Science at Aarhus University in Denmark — maintains the treevis repo. At the moment of writing, he has compiled over 300 different types of hierachical data visualizations and displays them on this website.
As an added bonus, the repo is interactive as there are several ways to filter and look for the visualization type that best fits your data and needs.
Most resources come with added links to the original authors and the original papers they were first published in, so this is truly a great resources for those interested in doing a deep dive into data visualization. Do have a look yourself!
The repository consists of tools for multiple languages (R, Python, Matlab, Java) and resources in the form of:
Books & Academic Papers
Online Courses and Videos
Algorithms and Applications
Open-source and Commercial Libraries/Toolkits
Key Conferences & Journals
Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify outlying objects that are deviant from the general data distribution. Outlier detection has been proven critical in many fields, such as credit card fraud analytics, network intrusion detection, and mechanical unit defect detection.