A Strategy to Understand Machine Learning and Deep Learning

Below, Ajit Jaokar puts together an Excellent introductory newsletter post (here one of many, join his newsletter for much more)     To get all the text and all illustrations, and commentary by him and others,  click through to Linkedin below.  Note this is maths-based, but relatively non technical.  Anyone can get the gist.  

NEWSLETTER ON LINKEDIN  (introduction)

Artificial Intelligence  By Ajit Jaokar

Open this article on LinkedIn to see what people are saying about this topic.     Open on LinkedIn

Artificial Intelligence #13: An easy maths-based strategy to understand machine learning and deep learning

Welcome to Artificial Intelligence #13

For this episode, I was originally going to post on a different theme, but I got quite a few comments on a post I made about maths on LinkedIn.

Because a few people found that post useful, I thought of expanding it a bit more on my approach of teaching AI using a maths based approach

I use a similar approach in my teaching #artificialintelligence at the #universityofoxford  

Previously, I discussed about the significance of maths in learning AI.

So, to recap, there are mainly four things you need to understand machine learning and deep learning

·      Probability theory

·      Statistics

·      Linear Algebra

·      Optimization

So, in this post, I am going to show you a simple approach to understand machine learning deep learning based on maths knowledge that most of you already know (as a student in year 12 / A levels if you took a maths/ science-based degree)

Here is a chain of thought I use

The idea is you start with simple concepts and gradually add to them using familiar maths

Considering the limits of this article, I will illustrate a small number of steps – but even these can be hopefully useful to you.   ...... "