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Author: Genre: Length: Novelette

Free on 28th - 30th Oct 17
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What is a MEMORYLESS predictive model?

Markov models are a powerful predictive technique used to model stochastic systems using time-series data. They are centered around the fundamental property of “memorylessness”, stating that the outcome of a problem depends only on the current state of the system – historical data must be ignored.

This model construction may sound overly simplistic. After all, if you have historical data why not use it to develop more complete and well-informed models? Surely, it would lead to more accurate predictions.

However, when modelling time-series data where previous results are of limited relevance, a memoryless model delivers vast performance advantages. By considering only the present state, algorithms become highly scalable, stable, fast and, above-all-else, extremely versatile. Speech recognition is a perfect example – nearly all of today’s speech recognition algorthms are built using Markov Models.

In this book we will explore why a Memoryless predictive model can be so advantageous to the modern tech industry. We will take a look at fundamental mathematics and high-level concepts alike, extending our understanding of the subject beyond the simple Markov Model.

You will learn…

Foundations of Markov Models
Markov Chains
Case Study: Google PageRank
Hidden Markov Models
Bayesian Networks
Inference Tasks

Free on 28th - 30th Oct 17
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Reviews:

"For someone from a maths or technical ground, this is a useful primer."

Reviewer: .


There are some very minor flaws with the text formatting, for example Bolding turning off halfway through a word like this. However, the book also contains numerous equations and complex images, and I can't fault the formating on those. The english is also extremely good and it is generally well-written and understandable, although there are a few odd phrasing mistakes - "by enlarge" used for "by and large" on loc 108, and "we have seen" repeated in a sentence at loc 111.

Being pedantic I could point out a few real world flaws in some of his examples. As someone who turns off predictive text due to is inaccuracies and the havoc it can create in a review, telling me that Markov chains run it isn't exactly a ringing endorsement. Nor is a model of site priority Google abandoned in 2014 as it was creating a self-reinforcing system of echo chambers. As examples however they are quickly identifiable and relatable.

The book is tackling a complex subject, and overall I think it does it really well. I'm not sure how useful this would be to a complete beginner, and it states up front that it assumes a high school level of maths. For the UK, you can probably get away with GCSE or O-level maths knowledge: it's all I've got and I didn't have any real problems following it.

I couldn't suggest this to a general reader, as it is dry as bone, but then it isn't meant for one. For students having trouble understanding Markov chains, or someone from a maths or technical ground coming to it as an adult, this is a useful primer.

Rating: 3



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