Data Science As A Career Change – My Story as a Video Interview

MT @_data_mania: Video interview~
#DataScience as #career change
#WomenInTech #AI

  • If you’re like most people who work with data on a regular basis, you’re probably hearing about data science as a career change option and wondering “Is data science right for me?
  • Although I can’t answer those lingering questions for you – I can tell you my experience, as a person who approached data science as a career change.
  • In this exclusive premier interview for LinkedIn Learning, I discuss how I transitioned myself from an Environmental Engineer to a Data Scientist.
  • There’s a lot covered in this lively 30-minute session; And if you’re considering data science as a career change, watching it should help you get a better idea what to expect, and hopefully a little inspiration to ignite your passion.
  • If you liked this video and want to learn more about how to make the transition into data science as a career change, then be sure to check out my LinkedIn Learning / Lyndas training courses here.

If you’re like most people who work with data on a regular basis, you’re probably hearing about data science as a career change option and wondering…
Continue reading “Data Science As A Career Change – My Story as a Video Interview”

Book: Machine Learning Algorithms From Scratch

Book: #MachineLearning Algorithms From Scratch

  • From First Principles With Pure Python and

    Use them on Real-World Datasets

    You must understand algorithms to get good at machine learning.

  • In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
  • I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones.
  • (yes I have written tons of code that runs operationally)

    I get a lot of satisfaction helping developers get started and get really good at machine learning.

  • I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.

Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets

$37 USD
You must understand algorithms t…
Continue reading “Book: Machine Learning Algorithms From Scratch”

Apple Machine Learning Journal

  • However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly.
  • An alternative to labelling huge amounts of data is to use synthetic images from a simulator.
  • This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images.
  • We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.
  • Read the article View the article “Improving the Realism of Synthetic Images”

Most successful examples of neural nets today are trained with supervision. However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly. An alternative to labelling huge amounts of data is to use synthetic images from a simulator. This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images. To help close this performance gap, we’ve developed a method for refining synthetic images to make them look more realistic. We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.
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AI can now predict whether or not humans will think your photo is awesome

#AI can now predict whether or not humans will think your photo is awesome

  • The Aesthetics tool, still in beta testing, allows users to upload a photo and get an auto-generated list of tags, as well as a percentage rate on the “chance that this image is awesome.”
  • According to developers, the neural network was trained to view an image much in the same way a human photo editor would, looking at factors such as color, sharpness, and subject.
  • As early users report, the system seems to be fairly good at recognizing factors like whether or not the image is sharp and if the composition is interesting, but it is certainly far from a pair of human eyes.
  • While the results of just how “awesome” a photo is may not be accurate for every image, the auto-tagging tool could prove useful, generating a list of keywords from object recognition as well as less concrete terms, like love, happiness, and teamwork.
  • Clicking on a keyword will bring up an Everypixel search for other images with that same tag, or users can copy and paste the list of keywords.

Can a computer judge art? A new neural network program will rank photos by their probability of being awesome.
Continue reading “AI can now predict whether or not humans will think your photo is awesome”

Try The Everypixel Tool To See What A Computer Thinks Of Your Best Shot

#AI can now predict whether or not humans will think your photo is awesome

  • The Aesthetics tool, still in beta testing, allows users to upload a photo and get an auto-generated list of tags, as well as a percentage rate on the “chance that this image is awesome.”
  • According to developers, the neural network was trained to view an image much in the same way a human photo editor would, looking at factors such as color, sharpness, and subject.
  • As early users report, the system seems to be fairly good at recognizing factors like whether or not the image is sharp and if the composition is interesting, but it is certainly far from a pair of human eyes.
  • While the results of just how “awesome” a photo is may not be accurate for every image, the auto-tagging tool could prove useful, generating a list of keywords from object recognition as well as less concrete terms, like love, happiness, and teamwork.
  • Clicking on a keyword will bring up an Everypixel search for other images with that same tag, or users can copy and paste the list of keywords.

Can a computer judge art? A new neural network program will rank photos by their probability of being awesome.
Continue reading “Try The Everypixel Tool To See What A Computer Thinks Of Your Best Shot”

Book: Machine Learning Algorithms From Scratch

Book: #MachineLearning Algorithms From Scratch #abdsc

  • From First Principles With Pure Python and

    Use them on Real-World Datasets

    You must understand algorithms to get good at machine learning.

  • In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
  • I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones.
  • (yes I have written tons of code that runs operationally)

    I get a lot of satisfaction helping developers get started and get really good at machine learning.

  • I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.

Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets

$37 USD
You must understand algorithms t…
Continue reading “Book: Machine Learning Algorithms From Scratch”

Artificial Intelligence: Removing The Human From Fintech

Artificial intelligence: Removing the human from fintech

  • News announced this week also suggests that artificial intelligence will become a central part of anything a technology organisation will do in the future.
  • Chief technology officer Paul Daugherty highlighted that “AI is poised to transform business in ways we’ve not seen since the impact of computer technology in the late 20th century.”
  • The UK government’s AI research will be led by Benevolent Tech’s CEO Jérôme Pesenti who explored how despite the “negative hype” around artificial intelligence, it has the ability to create jobs and transform industries.
  • This seems a little too optimistic to me as perhaps, AI will create new jobs, but it will remove people from careers that they know how to do and it will take time, years or decades in fact, for those people to learn new skills and then take on the jobs that artificial intelligence has ‘created’.
  • On the other hand, the millennial generation seem to welcome and encourage new technology – cellphone apps are a perfect example of how quickly new systems can enter the marketplace, so it could be said that this is the area in which AI could potentially blossom.

As I’m sure many in the technology industry have thought today, there should have been a way to avoid the Oscars Envelopegate. But, is artificial intelligence the answer to all of our human error problems?
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