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There are lots of good reasons why researchers are so fixated on model architectures, but it does mean that there are very few resources available to guide people who are focused on deploying machine learning in production. To address that, my talk at the conference was on “the unreasonable effectiveness of training data”, and I want to expand on that a bit in this blog post, explaining why data is so important along with some practical tips on improving it.
This will be part 1 of a 2 part series, where I'll teach you how to create a bot that plays Tetris. I got the idea to create a bot whilst playing Tetris. The difficulty got so hard that the limiting factor was my reaction time as the blocks dropped almost instantly. Instead of getting better at the game, my solution was to create a bot and then claim the score as my own \ (^_^) /
There’s been a lot of talk recently about whether or not AI research is in a bubble. Some people are worried that we’re approaching another AI winter — a period where AI research funding dries up if AI can’t deliver on the hype.
Deep learning has been at the forefront of the so called AI revolution for quite a few years now, and many people had believed that it is the silver bullet that will take us to the world of wonders of technological singularity (general AI).
The strength and robustness of a machine learning algorithm often lies in the quality of the dataset used to train it. Therefore, it would suffice to say that to gain true mastery within these fields, it is imperative that a person gains experience over a variety of machine learning problems that deal with a variety of datasets – ranging from image processing to speech recognition.