Messing around with OpenAI Gym – craftworkz

Ever heard of @OpenAI? We did some research...  #ai #openai #ArtificialIntelligence

  • OpenAI Gym is a cool platform for anybody involved with reinforcement learning algorithms.
  • To be clear, OpenAI Gym doesn’t power any algorithms itself, leaving it up to more specialised packages like TensorFlow or Theano.
  • The platform will allow you to test your algorithms in a variety of different environments without having to go through the hassle of making the right inputs available to your algorithm.
  • Data scientist at Craftworkz designing chatbots and developing robotics applications
  • That’s right, you can test the performance of your reinforcement learning algorithms on a variety of different atari games and what’s more, you can automatically upload the performance of your algorithms and compare them to other people’s approaches.

So while I was looking around for interesting Python-based AI projects I came across OpenAI Gym, backed by mister Elon Musk himself. This application aims to provide the ultimate sandbox environment…
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Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning

Machine learning use cases #AI:

1. Supervised
2. Unsupervised
3. Reinforcement #DF16

  • You start by showing a block and then placing the block in the block box; similarly you pick up a soft toy and then place it in the toy box.
  • Unsupervised learning is a somewhat harder form of machine learning.
  • Very quickly after a few iterations the child learns which toys need to go into which box – this is called Reinforcement Learning .
  • Just as the three year old learns the difference between a ‘block’ and a ‘soft toy’, the supervised machine learning system learns which email is ‘spam’ and which is ‘not spam’.
  • For example, a supervised machine learning system that can learn which emails are ‘spam’ and which are ‘not spam’ will have its input data tagged with this classification to help the machine learning system learn the characteristics or parameters of the ‘spam’ email and distinguish it from those of ‘not spam’ emails.

Where business and experience meet emerging technology.
Continue reading “Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning”

Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning

Machine learning use cases #AI:

1. Supervised
2. Unsupervised
3. Reinforcement

  • You start by showing a block and then placing the block in the block box; similarly you pick up a soft toy and then place it in the toy box.
  • Unsupervised learning is a somewhat harder form of machine learning.
  • Very quickly after a few iterations the child learns which toys need to go into which box – this is called Reinforcement Learning .
  • Just as the three year old learns the difference between a ‘block’ and a ‘soft toy’, the supervised machine learning system learns which email is ‘spam’ and which is ‘not spam’.
  • For example, a supervised machine learning system that can learn which emails are ‘spam’ and which are ‘not spam’ will have its input data tagged with this classification to help the machine learning system learn the characteristics or parameters of the ‘spam’ email and distinguish it from those of ‘not spam’ emails.

Where business and experience meet emerging technology.
Continue reading “Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning”

Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning

Machine learning use cases #AI

  • You start by showing a block and then placing the block in the block box; similarly you pick up a soft toy and then place it in the toy box.
  • Unsupervised learning is a somewhat harder form of machine learning.
  • For example, a supervised machine learning system that can learn which emails are â spamâ and which are â not spamâ will have its input data tagged with this classification to help the machine learning system learn the characteristics or parameters of the â spamâ email and distinguish it from those of â not spamâ emails.
  • Control theoretic techniques and Markov decision processes are types of reinforcement learning.
  • Now instead of telling the child which toy to put in which box, you reward the child with a â big hugâ when it makes the right choice and make a â sad faceâ when it makes the wrong action (e.g., block in a soft toy box or soft toy in the block box).

In this blog we explore different types of machine learning.
Continue reading “Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning”