ml-coursera-python-assignments
NumPy
ml-coursera-python-assignments | NumPy | |
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43 | 272 | |
5,382 | 26,360 | |
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11 months ago | 6 days ago | |
Jupyter Notebook | Python | |
- | GNU General Public License v3.0 or later |
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ml-coursera-python-assignments
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[D] Backpropagation is not just the chain-rule, then what is it?
check this out in particular. It's the week 4 homework from Ng's course, redone by someone to be in Python instead of Octave. It's got a built in grader, so you can grab the jupyter notebook, run it locally and it'll tell you when you've got the answer right. I'd recommend taking a crack at it, then when you figure out how to code it, take a look at that micrograd library and see how you could achieve something similar using an object oriented approach.
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How does Andrew Ng's courses compare to OMSCS ?
Python version of assignments which you can submit: https://github.com/dibgerge/ml-coursera-python-assignments
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Is the new Andrew Ng specialisation course worth it if I finished the original one with Python exercises?
Basically title. I'm halfway thru the original Stanford University Machine Learning course by Andrew Ng, but instead of using the Octave/Matlab exercises, I went with a Python repo. Now, I know the new specialisation course came out and is updated with newer content, more relevant to the state of the industry today. I have the following choices:
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Is the Andrew Ng course worth having to learn Octave?
A language is only worth learning if it is useful to know. But the only reason 99% of people would learn Octave is just to take that course lol. Besides, (a) the original course can be completed in Python using this repo, and now his new course is actually offered in Python.
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What do you think of Andrew Ng's new Machine Learning Specialization that launched last week on Coursera?
FWIW there is a repo you can use to complete the first one in Python. I used it and can vouch that it works perfectly as advertised.
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Andrew Ng updates his Machine Learning course
You can do them in python and submit them! https://github.com/dibgerge/ml-coursera-python-assignments
- Andrew Ng’s Machine Learning course is relaunching in Python in June 2022
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[NEWS] Not sure if this has been posted before, but ML course from Coursera is going to be updated in a new version in June (it will include python)
Andrew Ng ML-Coursera Assignments in Python
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New to ML
Last piece... Octave is super easy to get into. I don't personally think it's worth doing Python versions of the homework, but if you really can't stand screwing around with a new language, this repo has alternate versions of the homework to follow that will use Python instead. You can do either these or the original versions, so don't let the Octave scare you. You don't have to use it if you really don't want to, but... like I said, it's not a big deal either way, I just did it in Octave.
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Has anyone here done Andrew Ng's ML Course in Python and could help me out with the first assignment?
Specifically, I'm referring to this github repository: https://github.com/dibgerge/ml-coursera-python-assignments/blob/master/Exercise1/exercise1.ipynb. I'm currently doing Assignment 1.
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?
coursera-machine-learning-solutions-python - A repository with solutions to the assignments on Andrew Ng's machine learning MOOC on Coursera
SymPy - A computer algebra system written in pure Python
deeplearning-notes - Notes for Deep Learning Specialization Courses led by Andrew Ng.
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
Removeddit - View deleted stuff from reddit
blaze - NumPy and Pandas interface to Big Data
py - Repository to store sample python programs for python learning
SciPy - SciPy library main repository
RStudio Server - RStudio is an integrated development environment (IDE) for R
Numba - NumPy aware dynamic Python compiler using LLVM
ml-coursera-python-assignments-master - Python Machine Learning Exercises
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).