Hello, I am Anuraj, a Computer Science(B.E.) and Mathematics(M.Sc.) student. I have been going through the mlpack codebase since quite some time now (since this September to be precise). I am already familiar with the mlpack workflow as i have made a few contributions to mlpack.
/* a little background */ I am approaching the final stages of my graduation (currently in pre-final year), so i have started looking for topics for my thesis. After exploring a lot of options, i finally see my search concluding with ML. I went through the ideas page on the mlpack wiki and also went through some research papers to get a brief overview of the different aspects of ML. In particular, i went through some chapters of Ryan Curtin's dissertation (Improving Dual Tree Algorithms). I will be using it as part of my study-oriented project in the next semester. This project will help me further refine my thesis topic. In the meantime, I also implemented a few ML classifiers such as Decision Trees(using ID3 algorithm), Neural Networks(backpropagation algorithm) for face/pose/sunglass recognition, and the Naive Bayes Algorithm (for face recognition). The code for the above can be found in my github profile ( [1] ). Do note that the focus was on implementation rather than on writing a highly optimized code. Out of these three, ANNs really caught my attention. The power of Neural Networks amazes me and i see this as another potential topic for my thesis (especially after coming across Google's 'Quick, Draw!' ( [2] )). /* the point */ So, right now i am interested in these two topics: 1. Dual Tree Algorithms. 2. Neural Networks. *I would be grateful to you if you could recommend some relevant sources which would further shed some light on these topics. I would also like to hear your opinion on these topics. * I already plan on completing this course on neural networks over the winter: https://www.coursera.org/learn/neural-networks Also, the 'Essential Deep Learning Modules' project from the ideas page is relevant here (from what i understand, it wasn't taken up by anyone). I think this project will give me a chance to learn about different fundamental networks. I am yet to take a look at the relevant tickets and references mentioned there though. *Are there any other sources i should refer in order to prepare for the project? * I look forward to hearing from you. Thank you! [1] https://github.com/akanuraj200/MachineLearning [2] https://quickdraw.withgoogle.com/# <https://github.com/akanuraj200/MachineLearning>
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