AI and ML. Helper at Towards Data Science. Formerly at Cambridge University ML. Planting trees at thetreeplant.com

Knowing where to deploy the top AI tool of the century

Image for post
Image for post
Photo by Park Troopers on Unsplash

Deep Learning is a bit of a gimmicky title to give a set of Neural Network algorithms that have hidden layers. There’s actually nothing new compared to a multi-layered perception neural network, but the term deep learning (in particular) has been in the news a lot lately.

The hype of this phenomena is well received though. The application of deep learning algorithms on common problems have improved accuracy scores and improved the deployability of many solutions, however, is this largely due to the fact that now we have more servers and can actually run these algorithms, or are neural networks actually that much better than everything else? …


Mastering the command line to build the best system

Image for post
Image for post
Photo by Shashank Sahay on Unsplash

The skill set for a researcher is now more broad than ever. We have to know how to code, how to do the maths, and how to use programming tools like GIT and Linux etc.

I mean it’s not super hard to learn all of these things but it takes a while to become confident enough to actively engage in this space. How do you even know where to begin?

Either way, knowing linux is super important on your journey of being a researcher because for me as a mac user, Terminal is pretty similar to Linux (Mac OS X is a Unix OS and its command line is 99.9% …


Some of these will blow your mind

Image for post
Image for post
Photo by Andy Kelly on Unsplash

Data Scientists and Machine Learning researchers will both keep a nose around for what’s going on in the community.

There are various ways to do this: by checking out what’s going on in Kaggle or Product Hunt. Maybe having a look through stack overflow trends, or even looking at GitHub trends.

I’m a curious individual, so I wanted to see what had been trending this month: all of which I found to be pretty damn interesting.

Here goes it:

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization: 2,558 Stars

The researchers at Facebook have come out with an update to their Pixel-aligned Implicit Function (PIFu) model that aligns pixels of a 2D image with corresponding pixels of a 3D image. Using PIFu, Facebook have made a Deep Learning model (end-to-end) for digitising people, with the ability to infer 3D surface and texture from either a single image, or multiple. …


Tricks and techniques on how to format strings

Image for post
Image for post
Photo by Nate Grant on Unsplash

The ability to format a string is a pretty basic requirement for anyone that can code, but, there’ve been more than a few ways in the past that you’ve been able to do this in Python. There’s the original % method, there’s the .format method and more recently, there’s the f-String method. So which do you choose?

I’m pretty lazy so once I got the hang of .format methods, I kind of stuck to them, but there are drawbacks that I’ll cover which signify the problem with them.

But first, let’s do a quick overview:

%-formatting

This is the classic method, where those who were coding in the early Python2 days will remember clearly. Essentially you add in a % score with an ‘s’ (to reflect you want to chuck in a string) and add the % sign after the string as follows. …


Programming

Beginners should still learn Python

Image for post
Image for post
Photo by Clément H on Unsplash

Python and Java are relatively different languages. You could say that the Matlab/Java and Python/C++ combo are better comparisons but in terms of broad functionality and in terms of being a beginner, Python and Java are generally the two languages that beginners struggle to decide between.

The reason is that C++ generally gets discarded because it’s an ‘old’ language and more online references will recommend not to learn it. Likewise, for Matlab, it has a very specific use case but past that — you can’t really get much more functionality. …


Analysing authors from the Neural Information Processing Systems Conferences since 1990

Image for post
Image for post
Photo by Samuel Pereira on Unsplash

NIPS is classed as one of the foremost academic conferences in the space of AI. For academics, being published or running a workshop in this conference is a sign that you’re doing well and you’re making a difference.

The competition to get into this conference is high, like really really high. So generally speaking, you would expect that the best of the best have published the most in it. Let’s look at the most prolific authors who’ve submitted in the NIPS conferences since 1990:

Image for post
Image for post
Image by Author

With names like Gharamani, Hinton, Bengio and Jordan: clearly the big guys in AI have been busy! These guys are well known for revolutionising AI but for good reason as they’ve published so much more than others. …


Why are Neural Networks so good?

Image for post
Image for post
Photo by Robynne Hu on Unsplash

We see neural networks everywhere.

Literally everywhere.

There are domains where Neural Networks aren’t actually that powerful (think of problems with high degrees of stochasticity, Heteroscedasticity etc), but even so, the challenges that it faces and the degree to which it succeeds in them is outstanding.

Note that on Kaggle, more projects have been won by using something like XGBoost, rather than Neural Networks.

Either way, the developments in computer vision and reinforcement learning make us really really like neural nets.

But why are neural networks so good?

And why do you need so many hidden layers? What is the mathematical purpose of having them?

The main reason why is that Neural Networks can handle the XOR problem.

The XOR problem is a classification task where a model should return a True value if two inputs are different, and a False if they are equal. …


How to save 2.4 million trees a year

Image for post
Image for post
Photo by Nathan Dumlao on Unsplash

Data Science for Sustainability is becoming of more important as the problem of climate change has been getting more attention. As the UN say, Data is the lifeblood of decision-making and the raw material for accountability.’ so it’s our responsibility to make sure we can make a framework to keep us accountable for keeping the world green.

Sustainability is oddly one of the most documented industries that’s ripe for Data Science. There are so many data points and experiments that are all weakly linked, but little has actually been done to unify them under a mass framework. Sure we have loads of experiments which correlated, but where’’s the direct causation? …


Essential terms to know as we slowly move into the new year

Image for post
Image for post
Photo by Sharon McCutcheon on Unsplash

Artificial Intelligence has had a crazy couple of year and 2020 has been like no other. With a pandemic, a global recession along with incredible gains in AI, there’s simply so much to keep an eye on.

I studied my MPhil in Machine Learning in 2015 and back then, AI was in relative infancy but it was actually the perfect time to see the foundation of the industry that was to form.

Pre 2015, the theory was pretty sophisticated but the active day-to-day implementation of AI or Machine Learning was quite old. …

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store