machine learning Tag

Shine’s TEL group was established in 2011, initially to share jam-making recipes. We publicise the great technical work that Shine does, and raise the company’s profile as a technical thought-leader in the community through blogs, local meetup talks, and conference presentations. We curate all the noteworthy things that Shiners have been up to and publish a newsletter, this very one that you're reading right now. Hey, when you read this, whose voice do you hear in your head? Is it mine? Or yours? Everything I read is in Frank Walker from National Tiles' voice, please help me. Read on for this edition.
08 August, 2018 /

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Shine’s TEL group was established in 2011, initially as a money-laundering operation. We publicise the great technical work that Shine does, and raise the company’s profile as a technical thought-leader in the community through blogs, local meetup talks, and conference presentations. We curate all the noteworthy things that Shiners have been up to and publish a newsletter, in accordance with a mystical schedule that you wouldn't understand. Read on for this edition.
01 June, 2018 /

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Warning: This post contains pictures of spiders (and Spiderman)!

Google’s new Cloud AutoML Vision is a new machine learning service from Google Cloud that aims to make state of the art machine learning techniques accessible to non-machine learning experts. In this post I will show you how I was able, in just a few hours, to create a custom image classifier that is able to distinguish between different types of poisonous Australian spiders. I didn’t have any data when I started and it only required a very basic understanding of machine learning related concepts. I could probably show my Mum how to do it!
14 March, 2018 /

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Shine’s TEL group was established in 2011 with the aim of publicising the great technical work that Shine does, and to raise the company’s profile as a technical thought-leader in the community through blogs, local meet up talks, and conference presentations. Every now and then (it started off as being monthly, but that was too much work), we curate all the noteworthy things that Shiners have been up to, and publish a newsletter. Read on for this month's edition.
16 February, 2018 /

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pexels-photo-258291.jpeg In part 1, we learnt about recommendation engines in general, and looked at ways to implement a service using the Google Cloud Platform (GCP). In part 2 of the blog series, we are getting our hands dirty on the item-similarity model and TensorFlow implementation of it. This is our first technical blog of the series. Here, I deep dive into the data processing step, the recommendation service, and some hints on how to optimise the code to have real-time responses. You should expect to know how to build a simple item-similarity recommender engine by the end of this blog. So let's get the party started!
24 January, 2018 /

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Intro

Recommendation systems are found under the hood of many popular services and websites. The e-commerce and retail industry use them to increase their sales, the music services provide interesting songs to their listeners, and the news sites rank the daily articles based on their readers interests. If you really think about it, recommendation systems can be used in pretty much every area of daily life. For example, why not automatically recommend better choices to house investors, guide your friends in your hometown without you being around, or suggest which company to apply to if you are looking for a job.

All pretty cool stuff, right!

But, recommendation systems need to be a lot smarter than a plain old vanilla software. In fact, the engine is made up of multiple machine learning modules that aim to rank the items of the interests for the users based on the users preferences and items properties.

In this blog series, you will gain some insight on how recommendation systems work, how you can harness Google Cloud Platform for scalable systems, and the architecture we used when implementing our music recommendation engine on the cloud. This first post will be a light introduction to the overall system, and my follow up articles will subsequently deep dive into each of the machine learning modules, and the tech that powers them.

15 December, 2017 /

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OK Google, generate a clickbait title for my Google I/O 2017 blog post I've generated a title, Gareth. What would you like to add next? OK Google, I'm a bit jet lagged - remind me what I saw at Google I/O 2017 I would love to help, Gareth, but I'm going to need a little more information. Would you like that information in chronological order, or grouped by topic?
25 May, 2017 /

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Last week I had the privilege of attending Google Cloud Next in San Francisco. With Google finally due to open a datacenter in Australia this year, it was certain to be a great opportunity to learn about what's next with Google Cloud. From the moment I arrived at the baggage carousel at San Francisco International Airport, I was swamped with advertising for the conference. It was clear that Google is really pushing their cloud platform to as many developers as possible. This left me really excited for what was about to come over the following week. In this post I'm going to try and sum up how it all went.
14 March, 2017 /

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The decline of Stack Overflow?

A few months back I read this post from 2015 (yes, I know I'm a little late to the party) about how Stack Overflow (SO) was in serious decline, and heading for total and utter oblivion.  In the post, the first item to be called  out was that SO "hated new users":
Stack Overflow has always been a better-than-average resource for finding answers to programming questions. In particular, I have found a number of helpful answers to really obscure questions on the site, many of which helped me get past a road block either at work or in my hobby programming. As such, I decided I’d join the site to see if I could help out. Never before has a website given me a worse first impression.
At the time, I remember thinking that this seemed like somewhat of an unfair statement. That was mostly down to the fact that when I joined the community (many years ago), I had fond memories of a smooth on-boarding, and never experienced any snarky remarks on my initial questions. Yes, gaining traction for noobs is very, very hard, but there is a good reason why it exists. For me, SO is invaluable. How else would I be able to pretend to know what I'm doing? How else could I copy and paste code from some other person who's obviously a lot smarter than me, and take all the credit for it? Anyway, once I had read the post, and gotten on with my life (e.g. copying and pasting more code from SO), I did't think too much more about the post. Maybe I had just been lucky with my foray into the SO community? However, just last week, I was reminded of that post once again, when I noticed that BigQuery (BQ) now has a public dataset which includes all the data from SO - including user comments and answers. Do you see where I am going with this yet? If not, then don't worry. Neither did I when I started writing this.
20 December, 2016 /

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