missing-semester
scikit-learn
Our great sponsors
missing-semester | scikit-learn | |
---|---|---|
374 | 81 | |
4,679 | 57,985 | |
1.2% | 0.9% | |
6.8 | 9.9 | |
about 2 months ago | 6 days ago | |
CSS | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
missing-semester
-
Please advise, still struggling intensely
You mentioned having issues with accessory concepts so perhaps this might help: https://missing.csail.mit.edu/. There's also a chapter on git
- Curso del IPN
-
CS2030S and CS2040S advice
https://missing.csail.mit.edu/ is a good way to pass the Dec-Jan break if you want to prep for CS2030S + some more general stuff.
-
I cancelled my Replit subscription
Reflecting a little bit more I don't think it was replit's fault, per-say. But that change should have been made together with a larger adjustment to the program. Like adding a class/unit in the style of [the missing semester](https://missing.csail.mit.edu/) to make sure people came away with a good range of intuitions.
-
Advice to a Novice Programmer
From MJD's post: I think CS curricula should have a class that focuses specifically on these issues, on the matter of how do you actually write software?
But they never do.
FWIW, MIT's "The Missing Semester of Your CS Education" attempts to deal with this lack, though, even there, it's an unofficial course taught between terms, during MIT's IAP -- Independent Activities Period[1] -- and not an actual CS course.
[0] https://missing.csail.mit.edu/
[1] https://en.wikipedia.org/wiki/Traditions_and_student_activit...
- School of SRE: Curriculum for onboarding non-traditional hires and new grads
-
Advice / Resources from a "Seasoned Beginner"
Link to the "missing semester of your CS degree" course by MIT.
-
MIT's Missing Semester Class: Beyond the CS Curriculum
Rightly called The Missing Semester (of Your CS Education), this class from MIT will teach you how to use some of the tools that are fundamental to the software engineering ecosystem. From shell scripting to the fundamentals of information security—spanning around 12 lectures—you can add a bunch of practical skills to your toolbox.
- ¿Recomendaciones sobre que aprender?
-
How to do Btech without a college
Also highly suggest going through the missing semester
scikit-learn
-
AutoCodeRover resolves 22% of real-world GitHub in SWE-bench lite
Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:
- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...
- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.
There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).
-
Polars
sklearn is adding support through the dataframe interchange protocol (https://github.com/scikit-learn/scikit-learn/issues/25896). scipy, as far as I know, doesn't explicitly support dataframes (it just happens to work when you wrap a Series in `np.array` or `np.asarray`). I don't know about PyTorch but in general you can convert to numpy.
-
[D] Major bug in Scikit-Learn's implementation of F-1 score
Wow, from the upvotes on this comment, it really seems like a lot of people think that this is the correct behavior! I have to say I disagree, but if that's what you think, don't just sit there upvoting comments on Reddit; instead go to this PR and tell the Scikit-Learn maintainers not to "fix" this "bug", which they are currently planning to do!
- Contraction Clustering (RASTER): A fast clustering algorithm
-
Ask HN: Learning new coding patterns – how to start?
I was in a similar boat to yours - Worked in data science and since then have made a move to data engineering and software engineering for ML services.
I would recommend you look into the Design Patterns book by the Gang of Four. I found it particularly helpful to make extensible code that doesn't break specially with abstract classes, builders and factories. I would also recommend looking into the book The Object Oriented Thought Process to understand why traditional OOP is build the way it is.
You can also look into the source code of popular data science libraries such as sklearn (https://github.com/scikit-learn/scikit-learn/tree/main/sklea...) and see how a lot of them have Base classes to define shared functionality between object of the same nature.
As others mentioned, I would also encourage you to try and implement design patterns in your everyday work - maybe you can make a Factory to load models or preprocessors that follow the same Abstract class?
-
Transformers as Support Vector Machines
It looks like you've been the victim of some misinformation. As Dr_Birdbrain said, an SVM is a convex problem with unique global optimum. sklearn.SVC relies on libsvm which initializes the weights to 0 [0]. The random state is only used to shuffle the data to make probability estimates with Platt scaling [1]. Of the random_state parameter, the sklearn documentation for SVC [2] says
Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.
[0] https://github.com/scikit-learn/scikit-learn/blob/2a2772a87b...
[1] https://en.wikipedia.org/wiki/Platt_scaling
[2] https://scikit-learn.org/stable/modules/generated/sklearn.sv...
-
How to Build and Deploy a Machine Learning model using Docker
Scikit-learn Documentation
- Planning to get a laptop for ML/DL, is this good enough at the price point or are there better options at/below this price point?
-
Link Prediction With node2vec in Physics Collaboration Network
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy.
-
WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole.
What are some alternatives?
cs-topics - My personal curriculum covering basic CS topics. This might be useful for self-taught developers... A work in development! This might take a very long time to get finished!
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
computer-science - :mortar_board: Path to a free self-taught education in Computer Science!
Surprise - A Python scikit for building and analyzing recommender systems
CS50x-2021 - 🎓 HarvardX: CS50 Introduction to Computer Science (CS50x)
Keras - Deep Learning for humans
vimrc - The ultimate Vim configuration (vimrc)
tensorflow - An Open Source Machine Learning Framework for Everyone
javascript - JavaScript Style Guide
gensim - Topic Modelling for Humans
materials - Bonus materials, exercises, and example projects for our Python tutorials
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.