New Machine Learning Cheat Sheet by Emily Barry

Machine Learning Cheat Sheet

  • This blog about machine learning was written by Emily Barry.
  • Emily is a Data Scientist in San Francisco, California.
  • The more she learns about machine learning algorithms, the more challenging it is to keep these subjects organized in her brain to recall at a later time.
  • This is by no means a comprehensive guide to machine learning, but rather a study in the basics for herself and the likely small overlap of people who like machine learning and love emoji as much as she do.
  • For more articles about machine learning, click here.

This blog about machine learning was written by Emily Barry. Emily is a Data Scientist in San Francisco, California. She really loves emoji. Another thing she…
Continue reading “New Machine Learning Cheat Sheet by Emily Barry”

30 Free Courses: Neural Networks, Machine Learning, Algorithms, AI

Free Courses: Neural Networks, Machine Learning, Algorithms, AI #abdsc

  • The 78-video playlist above comes from a course called Neural Networks for Machine Learning, taught by Geoffrey Hinton, a computer science professor at the University of Toronto.
  • The videos were created for a larger course taught on Coursera, which gets re-offered on a fairly regularly basis.
  • Neural Networks for Machine Learning will teach you about “artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc.” The courses emphasizes ” both the basic algorithms and the practical tricks needed to get them to work well.”
  • You can find the video playlist on YouTube.
  • For more free courses about computer science, click here.

The 78-video playlist above comes from a course called Neural Networks for Machine Learning, taught by Geoffrey Hinton, a computer science professor at the Uni…
Continue reading “30 Free Courses: Neural Networks, Machine Learning, Algorithms, AI”

Free Deep Learning Textbook

Free #DeepLearning Textbook:  #abdsc #BigData #DataScience #MachineLearning #AI

  • The online version of the book is now complete and will remain available online for free.
  • For more information about this 700+ pages free book and its authors, click here.
  • The picture below represents a selection of (non-free) deep learning books: you can check them here.
  • For other free data science books, click here.
  • To access the free deep learning textbook, scroll down to the contents section, below the picture.

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in partic…
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Python for Big Data in One Picture

Python for Big Data in One Picture  #DataScience #IoT #MachineLearning #BigData

  • This picture originally posted here covers the following topics:

    To zoom in, view picture in the original article, or click on picture.

  • The original article also provides a detailed listing of all the 100+ entities listed in the picture, broken down in categories and sub-categories, some items belonging to multiple categories.
  • Anyone interested in creating a clickable link for each of these entities?
  • For instance, entity 1.1 (in the original article) is numpy, while 4.1 is matplotlib.

This picture originally posted here covers the following topics:

Basic stack
Newer packages
Integrated platforms
Visualization
Data formats
MapReduce
Glue
GPU…
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New Machine Learning Cheat Sheet by Emily Barry

Machine Learning Cheat Sheet

  • This blog about machine learning was written by Emily Barry.
  • Emily is a Data Scientist in San Francisco, California.
  • The more she learns about machine learning algorithms, the more challenging it is to keep these subjects organized in her brain to recall at a later time.
  • This is by no means a comprehensive guide to machine learning, but rather a study in the basics for herself and the likely small overlap of people who like machine learning and love emoji as much as she do.
  • For more articles about machine learning, click here.

This blog about machine learning was written by Emily Barry. Emily is a Data Scientist in San Francisco, California. She really loves emoji. Another thing she…
Continue reading “New Machine Learning Cheat Sheet by Emily Barry”

As many as 48 million accounts on Twitter are actually bots, study finds

As many as 48 million accounts on #Twitter are actually #Bots, study finds #AI

  • In February, Twitter announced it had 319 million monthly active users worldwide, or just slightly under the number of every person in the United States.But of those 319 million, as many as 48 million aren’t actually real, according to a study conducted by researchers from the University of Southern California: They’re just software programs, designed to do everything a normal person on Twitter would do, including following other accounts and liking and retweeting certain messages.Those accounts, called “bots,” can range from accounts dedicated to alerting their followers about emergencies to political advocates intended to boost the numbers of a programmer’s preferred candidate.
  • “Many bot accounts are extremely beneficial, like those that automatically alert people of natural disasters … or from customer service points of view,” a Twitter spokesperson told CNBC.However, there are also plenty of fake accounts.
  • Because a person’s number of Twitter followers is often seen as indicative of how popular and powerful that person is, there are services that allow people to buy followers, and quite often those services use bots as part of their service.Twitter has acknowledged the existence of bots in the past and has attempted to crack down on them, suspending accounts they believe are not human.
  • However, as CNBC reports, the USC study goes far beyond what Twitter itself has claimed about the number of bots on its platform.
  • In February, Twitter estimated in a SEC filing that up to 8.5 percent of its users were not human, while the USC study’s authors say even its estimate of 15 percent is “conservative.”

The study, from researchers at the University of Southern California, found that anywhere between nine and 15 percent of active users on the social media site are automated software that can like, retweet and follow just like accounts managed by humans.
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Book: Evaluating Machine Learning Models

Book: Evaluating #MachineLearning Models #abdsc

  • If you’re new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming.
  • With this O’Reilly report, machine-learning expert Alice Zheng takes you through the model evaluation basics.
  • In this overview, Zheng first introduces the machine-learning workflow, and then dives into evaluation metrics and model selection.
  • With this report, you will:

    Alice is a technical leader in the field of Machine Learning.

  • Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University.

Data science today is a lot like the Wild West: there’s endless opportunity and excitement, but also a lot of chaos and confusion. If you’re new to data scien…
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