Sign Up Now!

Sign up and get personalized intelligence briefing delivered daily.


Sign Up

Articles related to "machine"


Scaling Pandas: Comparing Dask, Ray, Modin, Vaex, and Rapids

  • Dask (as a lower-level scheduler) and Ray overlap quite a bit in their goal of making it easier to execute Python code in parallel across clusters of machines.
  • Ultimately, Dask is more focused on letting you scale your code to compute clusters, while Vaex makes it easier to work with large datasets on a single machine.
  • Modin should be your first port of call if you’re looking for a quick way to speed up existing Pandas code, while Vaex is more likely to be interesting for new projects or specific use cases (especially visualizing large datasets on a single machine).
  • The RAPIDS project as a whole aims to be much broader than Vaex, letting you do machine learning end-to-end without the data leaving your GPU.

save | comments | report | share on


Labeling Data with Pandas

  • Data labeling is the process of assigning informative tags to subsets of data.
  • Data containing x-ray images of cancerous and healthy lungs along with their respective tags is an example of labeled data.
  • Upon obtaining a labeled data set, machine learning models can be trained on the labeled data and used to predict on new unlabeled examples.
  • In this post, we will discuss the process of generating meaningful labels using the python Pandas library.
  • The data is now appropriately labeled for training a ternary classification model.
  • To summarize, in this post we discussed how to use Pandas for labeling data.
  • First, we considered the task of assigning binary labels to wine data that indicates whether a wine is above 10% alcohol by volume.
  • We then took a look at assigning ternary labels that indicate the level of fixed acidity in the wines.

save | comments | report | share on


Labeling Data with Pandas

  • Data labeling is the process of assigning informative tags to subsets of data.
  • Data containing x-ray images of cancerous and healthy lungs along with their respective tags is an example of labeled data.
  • Upon obtaining a labeled data set, machine learning models can be trained on the labeled data and used to predict on new unlabeled examples.
  • In this post, we will discuss the process of generating meaningful labels using the python Pandas library.
  • The data is now appropriately labeled for training a ternary classification model.
  • To summarize, in this post we discussed how to use Pandas for labeling data.
  • First, we considered the task of assigning binary labels to wine data that indicates whether a wine is above 10% alcohol by volume.
  • We then took a look at assigning ternary labels that indicate the level of fixed acidity in the wines.

save | comments | report | share on


Labeling Data with Pandas

  • Data labeling is the process of assigning informative tags to subsets of data.
  • Data containing x-ray images of cancerous and healthy lungs along with their respective tags is an example of labeled data.
  • Upon obtaining a labeled data set, machine learning models can be trained on the labeled data and used to predict on new unlabeled examples.
  • In this post, we will discuss the process of generating meaningful labels using the python Pandas library.
  • The data is now appropriately labeled for training a ternary classification model.
  • To summarize, in this post we discussed how to use Pandas for labeling data.
  • First, we considered the task of assigning binary labels to wine data that indicates whether a wine is above 10% alcohol by volume.
  • We then took a look at assigning ternary labels that indicate the level of fixed acidity in the wines.

save | comments | report | share on


NASCAR's Jimmie Johnson tests positive for Covid-19, will miss Sunday's race

  • Johnson tested positive Friday afternoon after learning that his wife, Chandra, had tested positive, according to a news release.
  • In Johnson's absence, Justin Allgaier will drive the No. 48 Chevrolet Camaro ZL1 1LE for Hendrick Motorsports on Sunday at the Big Machine Hand Sanitizer 500 at Indianapolis Motor Speedway.
  • The news comes weeks after NASCAR restarted its season on May 17.
  • Other American sports have slowly followed, despite rapidly rising coronavirus cases in the US.
  • Earlier this week, six players on the MLS team FC Dallas tested positive for Covid-19.

save | comments | report | share on


On Moving from Statistics to Machine Learning, the Final Stage of Grief

  • The main difference between machine learning and statistics is what I’d call “β-hat versus y-hat.” (I’ve also heard it described as inference versus prediction.) Basically, academia cares a lot about what the estimated parameters look like (β-hat), and machine learning cares more about being able to estimate a dependent variable given some inputs (y-hat).
  • I like showing ridge regression as an example of machine learning because it’s very similar to OLS, but is totally and unabashedly modified for predictive purposes, instead of inferential purposes.
  • In the latter design, you have to come up with a way to estimate the parameter that measures the extent to which customers would have behaved differently if they had or hadn’t received the coupon booklet.
  • Coupon booklets are not a list of things you predict that customers will buy next, it’s a list of things you think you can profitably entice people to buy through price discrimination, i.e. an inference problem.

save | comments | report | share on


TPOT: Automated Machine Learning in Python

  • You know, the process of finding an optimal algorithm and its hyperparameters.
  • The ideal reader is someone familiar with the Python programming language and with the general flow of a machine learning project.
  • You don’t have to be an expert, or even work in the field, but it’s expected that you’re familiar with the most common algorithms and how to use them.
  • Genetic Programming (GP) is a type of Evolutionary Algorithm (EA), a subset of machine learning.
  • That’s where genetic programming really shines, because it is inspired by the Darwinian process of Natural Selection, and they are used to generate solutions to optimization in computer science.
  • Sounds like a lot, definitely, but TPOT doesn’t require us to be experts in genetic programming.
  • Feel free to just copy it over, as this isn’t the article on data preparation.

save | comments | report | share on


TPOT: Automated Machine Learning in Python

  • You know, the process of finding an optimal algorithm and its hyperparameters.
  • The ideal reader is someone familiar with the Python programming language and with the general flow of a machine learning project.
  • You don’t have to be an expert, or even work in the field, but it’s expected that you’re familiar with the most common algorithms and how to use them.
  • Genetic Programming (GP) is a type of Evolutionary Algorithm (EA), a subset of machine learning.
  • That’s where genetic programming really shines, because it is inspired by the Darwinian process of Natural Selection, and they are used to generate solutions to optimization in computer science.
  • Sounds like a lot, definitely, but TPOT doesn’t require us to be experts in genetic programming.
  • Feel free to just copy it over, as this isn’t the article on data preparation.

save | comments | report | share on


TPOT: Automated Machine Learning in Python

  • You know, the process of finding an optimal algorithm and its hyperparameters.
  • The ideal reader is someone familiar with the Python programming language and with the general flow of a machine learning project.
  • You don’t have to be an expert, or even work in the field, but it’s expected that you’re familiar with the most common algorithms and how to use them.
  • Genetic Programming (GP) is a type of Evolutionary Algorithm (EA), a subset of machine learning.
  • That’s where genetic programming really shines, because it is inspired by the Darwinian process of Natural Selection, and they are used to generate solutions to optimization in computer science.
  • Sounds like a lot, definitely, but TPOT doesn’t require us to be experts in genetic programming.
  • Feel free to just copy it over, as this isn’t the article on data preparation.

save | comments | report | share on


Pre-infusion for Espresso: Visual Cues for Better Espresso

  • Typically, I would do a 10 second pre-infusion, push until the filter was covered, let it bloom for 5 seconds, then extract for however long it took.
  • I don’t recall why, but I think it was because my pre-infusion had extended just enough to where the filter was already covered when I started extraction, so a bloom seemed irrelevant.
  • Using the videos I collect for each shot, I found the time in the video when I could see coffee coming out of each filter hole to record as the TCF, and then I looked for the line on the cup reaching 10ml to record for the T10.
  • From this initial dataset, there is some correlation to Pre-infusion (PI), but for TCF on its own, there doesn’t seem to be much of a trend for either EY or taste (Final Score).

save | comments | report | share on