Soon, we’ll all jump into Deep Learning. I’ve experimented with the first (and very simple) Artificial Neural Networks and Convolutional Neural Networks, trying to identify cats and dogs, semi-successfully.
I think it’s time to start looking for other resources than this tutorial, which is tailored for beginners, thus aims to give a rough overview rather than being exhaustive. Now, still being beginners, we can move on.
We all have different backgrounds and interests. For me, I will focus from now on Deep Learning and Natural Language Processing, whereas my team members will go for Deep Learning and Reinforcement Learning combined with Robotics and more.
We will still follow tutorials (and review), but incorporate reading papers and building projects. Reading papers is a very important part of Machine Learning work, so this might be a good guideline to understand what’s important: How to read a paper (2013)
Another good resource to start with Convolutional Neural Networks is Adit Deshpande’s blog, and especially Part III of his series: Understanding CNNs, that links to 9 very insightful papers.
The next blog post will be dedicated to a review of the Machine Learning tutorial we’ve been working with. Until then: Enjoy Machine Learning!