The other day, I decided to read one paper about neural networks a day. I chose publications available at Arxiv and I added the site to my RSS reader. And just two days later I was shocked by the number of papers submitted to Arxiv every day! There were so many new ideas in the field of neural networks that it was impossible to follow them all. I gave up this habit after a week, but I realized one important thing: the majority of these papers were related to tuning or modifying already existing types of neural networks.
I’m about to start my final year at university, which will involve many activities related to obtaining my master degree. One of them is writing my master’s thesis, which is one of the biggest and the most time-consuming challenges. It’s a process that consists of writing the thesis and developing a project. I’m going to write a series of posts that will show you how the project is evolving.
The course named “Machine Learning”, created by Professor Andrew Ng is one of the most popular courses on the Internet. Since its first publication, more than 8 million learners have signed up. Initially, the course was available on YouTube but after some time it has been moved to Coursera.
This is my last technical post in the Get Noticed 2017 category. The last one will be a quick summary of the last three months. Soo…it was a very long week. I didn’t do too much but there was a big progress in using the neural network in the Aksesi Proxy Application.
The neural network requires to train it before using. We are expected to provide sets od date that will be used for the purpose of learning. It means that we need a generator which will create gestures in a form readable by the NN.
It’s time to start implementing support for recognizing gestures with neural networks. As I mentioned in the previous post, I had seen some potential problems. After two days of work, I can finally write that those problems are solved. In this post, I’ll describe how I solved the problem of different drawing area location. In the second paragraph, I’m going to describe implemented resizing strategy. At the end, in the third part, I’ll write some words about flattering gestures and values normalization.
In the previous post, I mentioned that the next thing developed in the Aksesi project will be a management console. After submitting that post, I realized that it is going to be another boring application with 90% of its logic encapsulated in CRUD operations. When I decided to take part in Get Noticed competition, my main goal was to learn new things. To make a long story short. The next step won’t be management console; the next step will be gesture recognition with Artificial Intelligence usage.