12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
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Updated
Jul 5, 2022 - Jupyter Notebook
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Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
100 Days of ML Coding
A complete daily plan for studying to become a machine learning engineer.
Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.
Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.
The "Python Machine Learning (1st edition)" book code repository and info resource
手写实现李航《统计学习方法》书中全部算法
Minimal and clean examples of machine learning algorithms implementations
Matlab code of machine learning algorithms in book PRML
This repositary is a combination of different resources lying scattered all over the internet. The reason for making such an repositary is to combine all the valuable resources in a sequential manner, so that it helps every beginners who are in a search of free and structured learning resource for Data Science. For Constant Updates Follow me in Twitter.
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)
Plain python implementations of basic machine learning algorithms
Hello everyone,
First of all, I want to take a moment to thank all contributors and people who supported this project in any way ;) you are awesome!
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PS: You need to be familiar with python and machine learning
Many estimators provide a random_state parameter to let users provide seeds for random number generators. Scikit-learn estimators can accept either an integer or a numpy.random.RandomState for random_state, and some PyData ecosystem tools (e.g. Boruta) pass RandomStates to estimators, so it would be nice if we could accept these as well.
import cuml
from sklearn.datasets iCurrently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.
This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see https://docs.pytest.org/en/latest/
A high performance implementation of HDBSCAN clustering.
On MacOS, the tslearn.datasets does not work out-of-the-box.
In order to make it work, you need to apply the following steps:
certifi package with pip.Perhaps we should add this to the documentation page of our datasets module?
Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web
Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning
A modular active learning framework for Python
The Python code to reproduce the illustrations from The Hundred-Page Machine Learning Book.
An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)
2021年的算法实习岗位/校招公司信息表,和常见深度学习基础、计算机视觉知识笔记、算法岗面试题答案,及暑期计算机视觉实习面经和总结。
The collaboration workspace for Machine Learning
Bug with GPU Model
Currently, while using pruning methods like
TaylorFOWeightPruner, If I use a model on GPU for getting the metrics (as calculated for getting masks), it fails on line while creating masks. The reason why it fails i