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hpr2955 :: Machine Learning / Data Analysis Basics

We talk about different machine learning techniques

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Hosted by Daniel Persson on 2019-11-29 is flagged as Clean and is released under a CC-BY-SA license.
Tags: machine learning, basics, theory.
Listen in ogg, spx, or mp3 format. | Comments (2)

In this episode, I talk about different techniques that we can use to predict the outcome of some question depending on input features.

The different techniques I will go through are the ZeroR and OneR that will create a baseline for the rest of the methods.

Next up, we have the Naive Bayes classifier that is simple but powerful for some applications.

Nearest neighbor and Decision trees are next up that requires more training but is very efficient when you infer results.

Multi-layer perceptron (MLP) is the first technique that is close to the ones we usually see in Machine Learning frameworks used today. But it is just a precursor to Convolutional Neural Network (CNN) because of the size requirements. MLPs have the same size for all the hidden layers, which makes it unfeasible for larger networks.

CNNs, on the other hand, uses subsampling that will shrink the layer maps to reduce the size of the network without reducing the accuracy of the predictions.


Comments

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Comment #1 posted on 2019-11-29T20:34:17Z by b-yeezi

Great first episode

Welcome to the HPR Host Crew! This was a great first episode. I look forward to you next one.

Comment #2 posted on 2019-12-06T10:23:42Z by gerryk

great! clear and informational

Hi Daniel...
Many thanks for a great epsiode. I have been dabbling in numerical analysis in Python for a few weeks. I think this is an area I would like to explore next.
Will check out your YT for sure.
Regards
Gerry

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