Data Science in Python: Pandas Cheat Sheet

#DataScience in #Python — Pandas Cheat Sheet:  #abdsc #BigData #MachineLearning by @DataCamp

  • This cheat sheet, along with explanations, was first published on DataCamp.
  • To view other cheat sheets (Python, R, Machine Learning, Probability, Visualizations, Deel Learning, Data Science, and so on) click here.
  • To view a better version of the cheat sheet and read the explanations, click here.

This cheat sheet, along with explanations, was first published on DataCamp. Click on the picture to zoom in. To view other cheat sheets (Python, R, Machine Lea…
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The Infinite Drum Machine

The Infinite Drum Machine: Thousands of everyday sounds, organized using machine learning

  • This experiment uses machine learning to organize thousands of everyday sounds.
  • The computer wasn’t given any descriptions or tags – only the audio.
  • Using a technique called t-SNE, the computer placed similar sounds closer together.
  • You can use the map to explore neighborhoods of similar sounds and even make beats using the drum sequencer.

AI Experiments is a showcase for simple experiments that let anyone play with artificial intelligence and machine learning in hands-on ways, through pictures, drawings, language, music, and more.
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This is why AI shouldn’t design inspirational posters

And this is why AI shouldn't design inspirational posters...

  • Inspirational posters have their place.
  • An AI dubbed InspiroBot, brought to our attention by IFL Science, puts together some of the most bizarre (and thus delightful) inspirational posters around.
  • The dog’s cute, but this isn’t great advice either.
  • This bot obviously doesn’t know many LARPers, or hang around at Renaissance Faires.
  • The bot’s posters fall in between Commander Data trying to offer advice and a mistranslated book of quaint sayings.

The results are kind of like if Commander Data from Star Trek tried to be your motivational therapist.
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Machine Learning’s Mediocre Gains

Hedge funds using AI suffer the same shortcomings as their more traditional peers

  • Hedge funds using vast amounts of data, computing power, and machine-learning techniques to make money are drawing investors’ attention.
  • But their brief track records show they suffer the same shortcomings as their more traditional peers.
  • The Eurekahedge AI Hedge Fund Index, which tracks 12 of these money pools, has outperformed hedge fund peers since 2013 but failed to beat the SP 500 Index.

Like hedge funds, AI strategies have struggled to beat the stock market

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Google creates a neural network that can carry out EIGHT different tasks at once in a step towards making AI behave more like a human

Google creates a #neuralnetwork that can carry out EIGHT tasks at once  #AI

  • But now, for the first time, Google has created a system that can do eight tasks at once, including image and speech recognition and language translation.
  • The inspiration for the MultiModel comes from how the brain transforms sensory input from modalities such as sound, vision and taste, and transforms them into a single shared representation and back out as language or actions.
  • ‘It can detect objects in images, provide captions, recognise speech, translate between four pairs of languages, and do grammatical constituency parsing at the same time’, the researchers, led by Lukasz Kaiser wrote in their blog.
  • ‘When designing MultiModel it became clear that certain elements from each domain of research (vision, language and audio) were integral to the model’s success in related tasks’, the researchers wrote.
  • ‘To our surprise, this happens even if the tasks come from different domains that would appear to have little in common, e.g., an image recognition task can improve performance on a language task’, the researchers said.

The MultiModel system has been created by researchers from Mountain View, California-based Google Brain. The system  can be taught to do eight tasks at once.
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GitHub

Our implementation of graph auto-encoders (in TensorFlow) is now available on GitHub:

  • This is a TensorFlow implementation of the (Variational) Graph Auto-Encoder model as described in our paper:

    Graph Auto-Encoders (GAEs) are end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs.

  • GAEs have successfully been used for:

    GAEs are based on Graph Convolutional Networks (GCNs), a recent class of models for end-to-end (semi-)supervised learning on graphs:

    A high-level introduction is given in our blog post:

    In order to use your own data, you have to provide

    Have a look at the function in for an example.

  • In this example, we load citation network data (Cora, Citeseer or Pubmed).
  • The original datasets can be found here: and here (in a different format): can specify a dataset as follows:

    You can choose between the following models:

    Please cite our paper if you use this code in your own work:

gae – Implementation of Graph Auto-Encoders in TensorFlow
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10 Super Scary Developments In Artificial Intelligence – Viral FreQ

  • That is, unfortunately, a very real fear these days, as mankind is making more and more advances in the science and technology behind AI components and robotics.
  • All it takes is for one robot to grow p*ssed off before they decide to form an uprising against humanity.
  • Lying and cheating is actually a normal human behavior.
  • But robots are only just learning to do this all on their own.
  • Researchers from the Georgia Institute of Technology developed a series of robots capable of cheating and deception towards others.

Stephen Hawking, Bill Gates, and even Elon Musk are considered some of the most brilliant men on the planet, but they are all absolutely terrified of artificial intelligence taking over the world one day. That is, unfortunately, a very real fear these days, as mankind is making more and more advances in the science and technology behind AI components and robotics. All it takes is for one robot to grow p*ssed off before they decide to form an uprising against humanity. We should all be terrified of that happening. Here are ten truly scary developments in AI.
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