Resources for developers using Python for scientific computing and quantitative analysis
For roughly $100 USD, you can add deep learning to an embedded system or your next internet-of-things project.
Are you just getting started with machine/deep learning, TensorFlow, or Raspberry Pi? Perfect, this blog series is for you!
But like in most cities, finding a parking space here is always frustrating. Spots get snapped up quickly and even if you have a dedicated parking space for yourself, it’s hard for friends to drop by since they can’t find a place to park.
My solution was to point a camera out the window and use deep learning to have my computer text me when a new parking spot opens up.
A curated list of awesome resources for practicing data science using Python, including not only libraries, but also links to tutorials, code snippets, blog posts and talks. So. Much. Python.
Tutorials covering how to do sentiment analysis using PyTorch 1.0 and TorchText 0.3 using Python 3.7.
The first 2 tutorials will cover getting started with the de facto approach to sentiment analysis: recurrent neural networks (RNNs). The third notebook covers the FastText model and the final covers a convolutional neural network (CNN) model.
