Fresh graduate wanting to fill skills gap for finding a job[reddit]/r/datasets
I am a fresh graduate with a degree in Applied Math and Statistics. My degree had a concentration in computer science. I am looking for work in data analytics.
I took the core math/stats classes for my degree. Some examples are data mining, time series analysis, linear regression, algorithms I & II, database systems (SQL), software engineering, and so on. I know Python, R, Matlab, SQL, Java.
I have no work experience and I'm looking for work in data analytics. I was wondering if there are any online edx courses I could take to fill my knowledge gap for what I will need on the job day to day. Preferably some crash course I could get done in under a month working on it full time.
[1905.09550] Revisiting Graph Neural Networks: All We Have is Low-Pass Filters "Our results indicate that graph neural networks only perform low-pass filtering on feature vectors"[reddit]https://arxiv.org/abs/1905.09550/r/MachineLearning
Abstract: Graph neural networks have become one of the most important techniques to
solve machine learning problems on graph-structured data. Recent work on vertex
classification proposed deep and distributed learning models to achieve high
performance and scalability. However, we find that the feature vectors of
benchmark datasets are already quite informative for the classification task,
and the graph structure only provides a means to denoise the data. In this
paper, we develop a theoretical framework based on graph signal processing for
analyzing graph neural networks. Our results indicate that graph neural
networks only perform low-pass filtering on feature vectors and do not have the
non-linear manifold learning property. We further investigate their resilience
to feature noise and propose some insights on GCN-based graph neural network