Nothing feels as good as checking something off a to-do list. In fact, I have added completed tasks to my to-do list purely to enjoy the feeling of checking them off a moment later.
The problem I find is that the tasks which are important to get done in the longer term and which move projects forward, tend to be:
I’d like to address one point of contention some people have had with my previous article about finishing projects – what to do when it isn’t worth finishing. I think the important thing to focus on here is the minimal viable product (MVP) that you can tangibly show as a result of having worked on something.
Sometimes we reach a stage where we realise that a project is no longer a logical use of our time. Maybe you’re working on a startup and realise there is no market for your product, or maybe new opportunities arise and it makes sense to redirect your time to a different project.
In cases like these, I think it’s fair to re-evaluate what ‘finished’ looks like. For some of your projects it may mean releasing an MVP and open sourcing some code, for others it may mean pivoting and salvaging some of your work in the new project.
Recently, the company that provides hosting for this website experienced some technical problems and my website went down. This was rather frustrating, and I sent them a Tweet to ask them to please resolve the issue. Before they even responded to me, I suddenly had this email in my inbox.
I saw your tweet to JustHost about your site being down.
I wanted to reach out to see if you would consider moving over to PeoplesHost? I’d love to speak more about any issues you’re experiencing, how PeoplesHost can help resolve those issues for you, and provide a level of support that you expect as a customer.
I have full confidence that we’d be able to provide a much better service. I’ll be looking forward to hearing back from you!
One of the biggest realisations I’ve come to as part of my PhD, is how little people care about how I spend my time, they only care about what I am able to deliver. Yet in order to deliver, you need to finish. I was recently reading an article on this topic, and I think the advice can be best summed up as:
If you’re always starting interesting projects and not finishing, then no matter how hard you work, you’re just busy, not productive.
I find this message extremely compelling, because I frequently find myself starting new programming projects in my spare time. In a lot of ways they are not a waste – I definitely learn a lot from these projects and gain a new skill. Yet at the same time, because I move on to something else that interests me before I can finish my current side-projects, when I tell people about these projects, all I can say is:
Google have released an open source project on GitHub called Grumpy that converts Python to Go, and then compiles it down to native code.
It’s an interesting development, but since they won’t be supporting C extension modules (which basically rules out all the scientific and machine learning libraries I use), it means I probably won’t end up using this new tool too much.
I was reading a paper by Pedro Domingos this evening which had some tips and advice for people using machine learning. I’ve written down some bullet points for my own reference and I hope someone else finds it useful. I know I’ve made some of the mistakes he gives advice about avoiding.
Recently, I helped establish BioSky – a website dedicated to making medical and health research accessible to people without a scientific background. As we’ve been adding content to the site, I’ve been posting the links to multiple social networks including Facebook, Twitter, LinkedIn and Reddit.
If I had to pick only one platform to use, it would undoubtedly be Reddit. I’ve had this view for a while, but a recent experience in the /r/Biology subreddit, where I posted an article explaining prions really solidified my opinion.
Deep learning is a type of machine learning based on neural networks which were inspired by neurons in the brain. The difference between a deep neural network and a normal natural network is the number of ‘hidden layers’ between the input and output layers.
I recently watched an excellent presentation on Deep Learning by Roelof Pieters titled ‘Python for image and text understanding; One model to rule them all!‘ I can recommend watching it, and I’ve written this post for me to put down a few of my own bullet points from the talk for future reference.
Over the past few years of blogging using WordPress I’ve found a number of essential plugins for the platform. I’ve written this post to provide as a resource for myself and others and will try to periodically update it if I find new/better plugins.
Plugins I’m using at the moment:
A couple of months ago I was approached by an organisation that provided programming training to staff at companies. They asked me if I was interested in becoming a trainer for them based on my experience running Software Carpentry workshops.
After seeking clarification and looking through the teaching materials, I refused.