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Posted on February 28, 2021 at 11:43am 0 Comments 1 Like
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…
ContinuePosted on February 21, 2021 at 12:18pm 0 Comments 1 Like
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…
ContinuePosted on February 14, 2021 at 1:41pm 0 Comments 1 Like
At the #universityofoxford I focus a lot on the mathematics aspect of AI
I recommend eight books for the mathematics of AI
Posted on February 7, 2021 at 1:26pm 0 Comments 1 Like
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…
ContinuePosted 1 March 2021
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