Demystifying machine learning part 2: Supervised, unsupervised, and reinforcement learning

#machinelearning use cases:

1. Supervised
2. Unsupervised
3. Reinforcement

  • It is a type of machine learning, where one guides the system by tagging the output.
  • 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.
  • 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’.
  • Now instead of telling the child which toy to put in which box, you reward the child with a ‘big hug’ when it makes the right choice and make a ‘sad face’ when it makes the wrong action (e.g., block in a soft toy box or soft toy in the block box).
  • Based on your problem domain and the availability of data do you know which type of machine learning system you want to build?

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 #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”

Demystifying Machine Learning Part 4: Image and Video Applications

Demystifying #MachineLearning Part 4: Image and Video Applications from @AnandSRao:  #AI

  • Deep learning requires large volumes of data in order for a system to learn the features and should not be attempted where data is sparse.
  • , deep learning is suitable only for certain classes of problems and cannot be seen as a panacea to solve all problems.
  • In the previous post in our Machine Learning series, we dived into the inner workings of deep learning .
  • Previous post: Sportifying STEM through Robotics to Stimulate Learning
  • Demystifying Machine Learning Part 4: Image and Video Applications

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How are companies using deep learning to drive business goals? Improved image and video recognition, audio recognition, and language understanding.


Demystifying Machine Learning Part 4: Image and Video Applications