More and more spam reviews on Amazon


Earlier this month I highlighted how a book that claims to be about using Python to build convolutional neural networks and yet, say readers, contains not a single line of Python, was garnering rave reviews on Amazon.

The trend hasn’t stopped and it is pretty clear to me that these are, in fact, spam.

Plainly Amazon’s review system is broken.

 

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Can you get a useful result with a random convolution filter?


In a number of places I’ve seen it remarked that a random convolution filter makes for a reasonably efficient edge detector for images, so I thought I’d test this.

The answer, perhaps surprisingly, seems to be yes.

With 25 input filters in an untrained convolutional neural net (where kernel values were pseudo-randomly distributed between -0.5 and 0.5), all but three of the first level filters returned something that suggested edge detection (though given the original image was a collection of edges this is not much of a claim.) Some of the second or even third level filters also showed patterns, but most delivered something like universal blackness.

Admittedly this is a small sample size, with just one test image.

Here is the original (100 x 100) image:

orig

Here are some of the useful or at least interesting (98 x 98) filtered images:

I cannot really think of a useful application of this finding, but it does interest me none the less.

Strange reviews on Amazon


Messing about with convolutional neural networks (CNNs) continues to take up some of my time (in case my supervisor reads this – I also have a simulation of a many core system running on the university’s computer atm).

I started my research here with a much cited, but really well-out-0f-date book – Practical Neural Network Recipes in C++. What’s nice about that book is that it is orientated towards getting things done, though the C++ is really from the “C with classes” era.

Another book – Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification – which I can access through the University of York’s library, helped fill in some of the theoretical and other gaps and also showed me why I needed to move away from the perceptron model promoted in the earlier book and move towards a CNN. But like many of Springer’s books it is poorly edited and not fully and properly translated to use online.

So I’m still on the look out for the perfect match – a book with practical coding examples that clearly explains the theory and, bluntly, is written in good English with all the maths actually reproduced in the online format (as I am just not going to be able to afford a printed copy.)

In particular I want a clear explanation of how to do back propagation in a CNN – as it’s plain that the general method outlined in “Practical Neural Network Recipes” doesn’t work beyond a fully connected layer, while the explanation in “Guide to…” is impenetrable and, actually, rather odd (as it seems to imply that we use a fixed weight for every neuron in a filter as opposed to using fixed weights across each filter – if I have explained that properly).

So, I’ve just had another look and came across this book … “Convolutional Neural Networks in Python: Introduction to Convolutional Neural Networks

This book has managed (at time of writing to have collected two, one star, reviews on the Amazon UK website):

Amazon reviews

I have no idea how fair those reviews are, but this passage from the preview version available on the website doesn’t suggest the author is yet rivalling Alan Turing:

But, here’s the odd thing. It would appear a number of “purchasers” of this book through Amazon.com are very enthusiastic about it and all felt the need to say so this very day (9 August):

odd reviews

Even more oddly, the reviews all read like spam comments I get on this blog. But I have no evidence to suggest these are anything other than genuine, if odd, comments on the book…

Free software to chop up your JPEGs


As a public service announcement – if you need some software (on Linux but may well compile and run on other systems if they support Qt) to chop a big JPEG up into smaller files, I have written this (in my case to support building a test set for a neural network).

It’s at https://github.com/mcmenaminadrian/TestSetCreator and it’s GPL licenced (v 3).