peter frick

[peter frick @peterlfrick]

ML performance is a subset of ML business value. Encoding business goals into the objective function of models boosts overall impact @MLconf #mlconf

[peter frick @peterlfrick]

Interesting explanation of variational autoencoders as a generalization of LDA, and how that’s used to scale recommendations @MLconf #mlconf

[Cecile Janssens @cecilejanssens]

The area under the ROC curve (AUC) is so frequently criticized and misunderstood that I often wonder whether I am the metric’s only fan. Let me explain why how I see and value the AUC.

(thread)

[Tai-Danae Bradley @math3ma]

A nice fact I like: Every matrix corresponds to a graph, and so familiar things (e.g. matrix multiplication) have nice pictures! Another nice fact: joint probability distributions *also* correspond to graphs. They have telling pictures, too. New blog post! https://t.co/EP67IhGAr0

[peter frick @peterlfrick]

@Damienrj @_alexrhee @peclevenger Anyone else reading this?

[Sebastian Ruder @seb_ruder]

A comprehensive overview and break-down of 14 NLP research paper highlights from 2018

[Nathan Yau @flowingdata]

Goodbye, mid-range shot. https://t.co/MGr0Amvjz5

[Allen Downey @AllenDowney]

A recent paper in NEJM shows that children born in August are 30% more likely to be diagnosed with ADHD than children born in September, probably because they start school younger.

I reanalyzed the data using Bayesian logistic regression:
https://t.co/fok41Hdgpj

[peter frick @peterlfrick]

@rctatman Imagine predicting what I’m like based on a sibling. He is a model of me. If you don’t learn about him to predict me, you are excluding useful info (bias) But if you learn everything about him and apply it to me thats using too much info since we are different ppl (variance)

[peter frick @peterlfrick]

Congrats Parker!

[Travis Oliphant @teoliphant]

Slides for my talk @py_bay about AI/ML and Python are located here: https://t.co/Zn6pjXplXB #PyBay2018 #MachineLearning #Python @NumFOCUS @TensorFlow @PyTorch @ChainerOfficial @ApacheMXNet

[peter frick @peterlfrick]

Clicked for the title, stayed for the memes

[Kaggle @kaggle]

RT @afshinea 🔥Set of illustrated Machine Learning cheat sheets from Stanford's CS 229 class:
Deep Learning: https://t.co/BNQ7FpUum4
Supervised Learning: https://t.co/5bhzGHlUCl
Unsupervised Learning: https://t.co/ZaD94OQNNN
Tips and tricks: https://t.co/SciByei6a1

[Machine Learning Mastery @TeachTheMachine]

How to Create an ARIMA Model for Time Series Forecasting in Python https://t.co/9CX4NBs7P1

[Real Python @realpython]

One of the top questions we've been getting from new Python coders is "how should I structure my Python projects?"

With this tutorial, we want to give you a dependable Python application layout reference guide that you can refer to: https://t.co/qW5fHc5hma

[peter frick @peterlfrick]

how to train a neural network for text classification using keras https://t.co/YQt0e2w4fF

[Radim Řehůřek @RadimRehurek]

"Advances in Semantic Textual Similarity": @GoogleAI's new post on universal text embeddings: https://t.co/Xk2vyZTndj

Pre-trained 512 dim #Tensorflow model: https://t.co/6FOngCGXHm (already available in @scaletext_ai)

[Radim Řehůřek @RadimRehurek]

"Context is Everything: Finding Meaning Statistically in Semantic Spaces." A replacement of tf-idf that is actually better than tf-idf (finally?!)
https://t.co/mvFYZmt47h

[🔎Julia Evans🔍 @b0rk]

virtual memory

[Daren Willman @darenw]

Just for fun... Here's how an outfield of all Buxton would compare to an outfield of all Pujols based on 2017 sprint speed. Each colored band is a second of hang time. outer band is 6 seconds, inner band 3.