scikitlearn
Keras
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scikitlearn  Keras  

24  30  
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0.8%  0.7%  
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3 days ago  5 days ago  
Python  Python  
BSD 3clause "New" or "Revised" License  GNU General Public License v3.0 or later 
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scikitlearn

Data Science toolset summary from 2021
Scikitlearn  It is one of the most widely used frameworks for Python based Data science tasks. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, kmeans and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Link  https://scikitlearn.org/

Intel Extension for ScikitLearn
Hi all,
Currently some works is being done to improve computational primitives of scikitlearn to enhance its overhaul performances natively.
You can have a look at this exploratory PR: https://github.com/scikitlearn/scikitlearn/pull/20254
This other PR is a clear revamp of this previous one:

ScikitLearn Version 1.0
Just to clarify, scikitlearn 1.0 has not been released yet. The latest tag in the github repo is 1.0.rc2
https://github.com/scikitlearn/scikitlearn/releases/tag/1....

Top 10 Python Libraries for Machine Learning
Website: https://scikitlearn.org/ Github Repository: https://github.com/scikitlearn/scikitlearn Developed By: SkLearn.org Primary Purpose: Predictive Data Analysis and Data Modeling

where is binary_metric function in sklearn package
There is a function named binary_metric in https://github.com/scikitlearn/scikitlearn/blob/main/sklearn/metrics/_base.py

Use ScikitLearn and Runflow
If you're not familiar with Scikitlearn and Runflow,

Confused as to what exaclty a piece of code does
well you can start at https://github.com/scikitlearn/scikitlearn/blob/main/sklearn/model_selection/_validation.py, or maybe someone will guide you later

What Makes Python Libraries So Important For Data Science Learning?
Next comes the complexity of drawing the maximum possible number of valuable insights. Using different python libraries such as ScikitLearn, PyTorch, Pandas, etc., complications of data analysis can be solved within a minute. And the complexity associated with visualisation gets handled by other data visualisation libraries like Matploitlib, PyTorch, etc.

Is there a way to map cluster centers back to a dataframe?
To avoid the issue with convergence (and the discrepancy between the labels_ and cluster_centers_), you can set tol=0, though this can of course lead to issues if convergence is a problem. There was an issue about it here. Assuming it's converged, then the order is fine.

Any from scratch Hamming Loss implementations?
The source code for the function you refer to is quite straightforward anyway. The definition of count_nonzero() is here.
Keras

5 ways to keep your skills fresh after finishing a coding bootcamp
One way to improve your projects and coding skills is to try new models and libraries. For example, if you did classification with logistic regression, try also with random forest; if you used Tensorflow, now try Keras; if you scraped a website with BeautifulSoup, now do it with Scrapy. You get the point.

[P] Walkthrough of Keras.Model Internals. Includes: distribution, performance optimizations, callbacks, training loop, and more.
The source for the keras.Model class has grown to be several thousand lines of code. This makes it incredibly challenging to sift through, especially for beginners.

Data Science toolset summary from 2021
Keras  Keras is an opensource software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library. Link  https://keras.io/
 structuring larger projects, and good practises

Steps_per_epoch=1
I think I found my answer here Thank you for your help
 Data Science with JavaScript: What we've learned so far?

Top 10 Python Libraries for Machine Learning
Website: https://keras.io/ Github Repository: https://github.com/kerasteam/keras Developed By: various Developers, initially by Francois Chollet Primary purpose: Focused on Neural Networks

[D] Getting Started
I also recommend trying to understand the software that's being built by the machine learning class. If you want to build your own machine learning software, check out Keras (http://keras.io/) and the machine learning API's that Keras provides.

JAX  COMPARING WITH THE BIG ONES
These four points lead to an enormous differentiation in the ecosystem: Keras, for example, was originally thought to be almost completely focused on point (4), leaving the other tasks to a backend engine. In 2015, on the other hand, Autograd focused on the first two points, allowing users to write code using only "classic" Python and NumPy constructs, providing subsequently many options for point (2). Autograd's simplicity greatly influenced the development of the libraries to follow, but it was penalized by the clear lack of the points (3) and (4), i.e. adequate techniques to speed up the code and sufficiently abstract modules for neural network development.

What are the icons used in the banner of this subreddit?
Keras
What are some alternatives?
MLP Classifier  A handwritten multilayer perceptron classifer using numpy.
Surprise  A Python scikit for building and analyzing recommender systems
Prophet  Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or nonlinear growth.
tensorflow  An Open Source Machine Learning Framework for Everyone
gensim  Topic Modelling for Humans
TFLearn  Deep learning library featuring a higherlevel API for TensorFlow.
xgboost  Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
PyBrain
skflow  Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning