Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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100 Days Of Ml Code | 42,417 | 3 months ago | 60 | mit | ||||||
100 Days of ML Coding | ||||||||||
Python Machine Learning Book | 11,645 | 2 years ago | 11 | mit | Jupyter Notebook | |||||
The "Python Machine Learning (1st edition)" book code repository and info resource | ||||||||||
Tensorflow Book | 4,443 | 4 years ago | 14 | mit | Jupyter Notebook | |||||
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations. | ||||||||||
Machine_learning_basics | 3,754 | a year ago | 2 | mit | Jupyter Notebook | |||||
Plain python implementations of basic machine learning algorithms | ||||||||||
Interviews.ai | 3,146 | 2 years ago | 4 | |||||||
It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well. | ||||||||||
Machine Learning Specialization Coursera | 2,082 | 3 months ago | 16 | mit | Jupyter Notebook | |||||
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG | ||||||||||
Dat8 | 1,549 | a year ago | Jupyter Notebook | |||||||
General Assembly's 2015 Data Science course in Washington, DC | ||||||||||
Deeplearning.ai Natural Language Processing Specialization | 523 | 3 years ago | gpl-3.0 | Jupyter Notebook | ||||||
This repository contains my full work and notes on Coursera's NLP Specialization (Natural Language Processing) taught by the instructor Younes Bensouda Mourri and Łukasz Kaiser offered by deeplearning.ai | ||||||||||
Tensorflow Ios Example | 420 | 7 years ago | 8 | Swift | ||||||
Source code for my blog post "Getting started with TensorFlow on iOS" | ||||||||||
Ytk Learn | 351 | 2 years ago | n,ull | mit | Java | |||||
Ytk-learn is a distributed machine learning library which implements most of popular machine learning algorithms(GBDT, GBRT, Mixture Logistic Regression, Gradient Boosting Soft Tree, Factorization Machines, Field-aware Factorization Machines, Logistic Regression, Softmax). |