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C Programming for Machine Learning (LIVE)



Siraj Raval

The ability to write implementations of machine learning algorithms in pure C allows developers to very efficiently manage memory allocation, concurrency, and control flow. That means fast implementations that can outperform preexisting models in other languages, including even (gasp) Python. It’s a useful skill to know and in this live stream I’ll use C and C-based Python tools like Cython + spaCy to develop some really fast natural language processing algorithms for text data. We’ll be able to tokenize, tag, normalize, vectorize, and dependency parse articles of text to derive valuable insights. No installation necessary, we’ll do this together using Google Colab in the browser. Join me, there’s a lot to cover here!

Code for this video:
https://github.com/llSourcell/c_programming_for_machine_learning

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More learning resources:
https://pydata.org/berlin2016/schedule/presentation/51/
https://smerity.com/articles/2018/cython_for_high_and_low.html
https://explosion.ai/blog/writing-c-in-cython
https://spacy.io/api/cython
https://medium.com/huggingface/100-times-faster-natural-language-processing-in-python-ee32033bdced

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26 thoughts on “C Programming for Machine Learning (LIVE)
  1. @SirajRaval I need to make time to watch more of your videos. This would have helped me a lot when I was trying to make a text sentiment classifier using tensorflow and RNNs. I think I'm going to do a whole load of research then I'll have another go.

  2. wrg, not want to worry about anyx, no worry for such or anyx, do things not worry things. also no wantx about that, ts just a toolx, nonerx

  3. Java isn't the same anymore. Java8 and Java9 changed the game big time. Most of the times you don't even need loops and structs anymore as you are able to define logical structures in logical sequences (e.g. lambda expressions in combination with streams)

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