numerical-linear-algebra
NumPy
numerical-linear-algebra | NumPy | |
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6 | 272 | |
10,008 | 26,413 | |
0.3% | 1.1% | |
0.0 | 10.0 | |
17 days ago | about 24 hours ago | |
Jupyter Notebook | Python | |
- | GNU General Public License v3.0 or later |
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numerical-linear-algebra
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I'm a 42-years-old librarian whithout any math background and I'm willing to learn
If you really like to dig into math, I liked the Udacity course on Intro to Deeplearning with Pytorch. Also, the Stanford course CS231n Convolutional Neural Networks for Visual Recognition is a good place to understand some basics. Other two courses to get you jumpstarted are Practical Deep Learning for Coders and Linear Algebra Course by FastAI
- Hi, what are the advanced courses/books in machine learning and neural nets? And where do I find them?
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Math for Machine Learning!
I've also bookmarked Fast.Ai Computational Linear Algebra for Coders. https://github.com/fastai/numerical-linear-algebra/blob/master/README.md
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Textbook for computer algebra using Python?
In that case, I would probably be temped to teach a numerical methods of linear algebra course using NumPy / Numba. Something like https://github.com/fastai/numerical-linear-algebra or https://pythonnumericalmethods.berkeley.edu/notebooks/Index.html
- Interactive Linear Algebra Text Book
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OPEN SOURCE COMPUTER SCIENCE CURRICULUM
Computation Linear Algebra Lectures Study Material To do after completing curricula.
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
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Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
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A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
stacks-project - Repository for the Stacks Project
SymPy - A computer algebra system written in pure Python
pml-book - "Probabilistic Machine Learning" - a book series by Kevin Murphy
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
cornell-cs5785-2020-applied-ml - Teaching materials for the applied machine learning course at Cornell Tech (online edition)
blaze - NumPy and Pandas interface to Big Data
course-nlp - A Code-First Introduction to NLP course
SciPy - SciPy library main repository
Data-Science-FLIGHT-DELAY-PREDICTION-HIT - Our project focuses on predicting flight delays using machine learning techniques. We employ feature engineering and advanced regression algorithms to enhance accuracy. The dataset includes flight info, weather conditions, and other relevant factors. Our model achieves 94% accuracy.
Numba - NumPy aware dynamic Python compiler using LLVM
materials - Bonus materials, exercises, and example projects for our Python tutorials
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).