100 Days of ML Coding
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Updated
Jul 15, 2020
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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.
100 Days of ML Coding
A complete daily plan for studying to become a machine learning engineer.
The "Python Machine Learning (1st edition)" book code repository and info resource
Minimal and clean examples of machine learning algorithms implementations
I'm using mxnet to do some work, but there is nothing when I search the mxnet trial and example.
手写实现李航《统计学习方法》书中全部算法
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
Plain python implementations of basic machine learning algorithms
There are (at least) two ways in which the reinterpret_cast is misused:
static_cast:void*.
A high performance implementation of HDBSCAN clustering.
I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in generate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.
Of course, piping is a solution, but not for development in Jupyter Notebook, for example.
All dev environments should install faiss from conda. The 10.2 environment is currently missing it. It's also missing ucx-proc. Should follow the other env styles.
See: https://github.com/rapidsai/cuml/blob/branch-0.15/conda/environments/cuml_dev_cuda10.2.yml
Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web
Currently 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/
An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)
Hi, Thanks for the awesome library!
So I am running a Kmeans on lots of different datasets, which all have roughly four shapes, so I initialize with those shapes and it works well, except for just a few times. There are a few datasets that look different enough that I end up with empty clusters and the algorithm just hangs ("Resumed because of empty cluster" again and again).
I conceptually
The Python code to reproduce the illustrations from The Hundred-Page Machine Learning Book.
Find label errors in datasets, weak supervision, and learning with noisy labels.
Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning
Code of my MOOC Course <Play with Machine Learning Algorithms>. Updated contents and practices are also included. 我在慕课网上的课程《Python3 入门机器学习》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。
This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.
A modular active learning framework for Python
Deeplearning Algorithms Tutorial
Java Statistical Analysis Tool, a Java library for Machine Learning
Android TensorFlow Lite Machine Learning Example
Added object oriented approach for BST. Please review and let me know if any changes are required.