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Data Science Weekly Newsletter
September 2, 2021
Intelligent carpet gives insight into human poses
The sentient Magic Carpet from Aladdin might have a new competitor. While it can’t fly or speak, a new tactile sensing carpet from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) can estimate human poses without using cameras, in a step towards improving self-powered personalized healthcare, smart homes, and gaming...
Google’s New AI Photo Upscaling Tech is Jaw-Dropping
Photo enhancing in movies and TV shows is often ridiculed for being unbelievable, but research in real photo enhancing is actually creeping more and more into the realm of science fiction. Just take a look at Google’s latest AI photo upscaling tech...
The 7 Reasons Most Machine Learning Funds Fail Marcos Lopez de Prado [Video]
This talk, titled The 7 Reasons Most Machine Learning Funds Fail, looks at the particularly high rate of failure in financial machine learning. The few managers who succeed amass a large number of assets, deliver consistently exceptional performance to their investors. However, that is a rare outcome. This presentation will go over the 7 critical mistakes underlying most financial machine learning failures based off of Marcos López de Prado’s experiences and observations...
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What is the secret formula for MLOps success?
Putting one foot before the other on the way to uncover the secret I spoke with more than 100 ML professionals to learn about their MLOps journeys. My expedition is far from over but I saw three ‘signposts’ along the way that helped me better understand what the formula for MLOps success looks like. I hope you find them helpful on your journey too...
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing their relative performance on a large suite of tasks. Most published results on deep RL benchmarks compare point estimates of aggregate performance such as mean and median scores across tasks, ignoring the statistical uncertainty implied by the use of a finite number of training runs. Beginning with the Arcade Learning Environment (ALE), the shift towards computationally-demanding benchmarks has led to the practice of evaluating only a small number of runs per task, exacerbating the statistical uncertainty in point estimates. In this paper, we argue that reliable evaluation in the few run deep RL regime cannot ignore the uncertainty in results without running the risk of slowing down progress in the field...
Multiplying Matrices Without Multiplying
Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning. Consequently, there has been significant work on efficiently approximating matrix multiplies. We introduce a learning-based algorithm for this task that greatly outperforms existing methods. Experiments using hundreds of matrices from diverse domains show that it often runs 100× faster than exact matrix products and 10× faster than current approximate methods...
Music Composition with Deep Learning: A Review
Generating a complex work of art such as a musical composition requires exhibiting true creativity that depends on a variety of factors that are related to the hierarchy of musical language. Music generation have been faced with Algorithmic methods and recently, with Deep Learning models that are being used in other fields such as Computer Vision. In this paper we want to put into context the existing relationships between AI-based music composition models and human musical composition and creativity processes...
11 Short Videos About AI Ethics
I made a playlist of 11 short videos (most are 6-13 mins long) on Ethics in Machine Learning. This is from my ethics lecture in Practical Deep Learning for Coders v4. I thought these short videos would be easier to watch, share, or skip around...
A guide to Knowledge Graphs
A consolidation of notes that briefly but gently introduces Knowledge graphs and shines a light on several practical aspects...
TransformX Conference: Driving AI from Experimentation to Reality
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Training & Resources
Dask and pandas: There’s No Such Thing as Too Much Data
Do you love pandas, but hate when you reach the limits of your memory or compute resources? Dask gives you the chance to use the pandas API with distributed data and computing. In this article, you’ll learn how it really works, how to use it yourself, and why it’s worth the switch...
CLabel is a terminal-based cluster labeling tool that allows you to explore text data interactively and label clusters based on reviewing that data...