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Ajit jaokar's Blog – February 2021 Archive (4)

The Bayesian vs frequentist approaches: implications for machine learning – Part two

This blog is the second part in a series. The first part is The Bayesian vs frequentist approaches: implications for machine learning – Part One

In part one, we summarized that:

There are three key points to…

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Added by ajit jaokar on February 28, 2021 at 11:43am — No Comments

The Bayesian vs frequentist approaches: Implications for machine learning – Part One

Background

The arguments / discussions between the Bayesian vs frequentist approaches in statistics are long running. I am interested in how these approaches impact machine learning. Often, books on machine learning combine the two approaches, or in some cases, take only one approach. This does not help from a learning…

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Added by ajit jaokar on February 21, 2021 at 12:18pm — No Comments

Probabilistic Machine Learning book – a great free reference for maths of machine learning



At the #universityofoxford I focus a lot on the mathematics aspect of AI

 

I recommend eight books for the mathematics of AI

 

 

  1. The Nature Of Statistical Learning Theory By Vladimir Vapnik.
  2. Pattern Classification By Richard O Duda
  3. Machine Learning: An Algorithmic Perspective, Second…
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Added by ajit jaokar on February 14, 2021 at 1:41pm — No Comments

What distributions do we need to use Deep Learning?

 

I was asked this question: “What distributions do we need to use Deep Learning?”

 

This is a question with a multi-faceted answer.

 

The direct answer is that algorithms which are traditionally referred to as deep learning (MLP, CNN, LSTM) do not need to know the distribution in advance since they…

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Added by ajit jaokar on February 7, 2021 at 1:26pm — No Comments

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