Difference between revisions of "Cython"
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| + | [https://www.youtube.com/results?search_query=Cython Youtube search...] | ||
| + | [https://www.google.com/search?q=Cython+machine+learning+ML+artificial+intelligence ...Google search] | ||
* [[Python]] | * [[Python]] | ||
| + | * [[Libraries & Frameworks Overview]] ... [[Libraries & Frameworks]] ... [[Git - GitHub and GitLab]] ... [[Other Coding options]] | ||
| − | + | Cython: Blend the Best of Python and C++ ...superset of Python in C Programming. | |
<youtube>mXuEoqK4bEc</youtube> | <youtube>mXuEoqK4bEc</youtube> | ||
| − | <youtube> | + | <youtube>gMvkiQ-gOW8</youtube> |
| + | |||
| + | === Fast natural language processing (NLP) with spaCy === | ||
| + | |||
| + | C Programming for Machine Learning - 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. | ||
| − | + | * [[spaCy]] | |
| + | * [[Natural Language Processing (NLP)]] | ||
| + | * Google [[Colaboratory]] (Colab) | ||
<youtube>giF8XoPTMFg</youtube> | <youtube>giF8XoPTMFg</youtube> | ||
Latest revision as of 22:57, 1 March 2024
Youtube search... ...Google search
- Python
- Libraries & Frameworks Overview ... Libraries & Frameworks ... Git - GitHub and GitLab ... Other Coding options
Cython: Blend the Best of Python and C++ ...superset of Python in C Programming.
Fast natural language processing (NLP) with spaCy
C Programming for Machine Learning - 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.
- spaCy
- Natural Language Processing (NLP)
- Google Colaboratory (Colab)
