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pingdow's Projects

ailearning icon ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

fpgrowth icon fpgrowth

FP-growth codes in "Machine Learning in Action"

huawei-2018-software icon huawei-2018-software

华为2018软件精英挑战赛初赛代码,pure python without numpy,实现拉格朗日均值、线性回归、随机森林、指数平滑、模拟退火等算法。

interview icon interview

📚 C/C++ 技术面试基础知识总结,包括语言、程序库、数据结构、算法、系统、网络、链接装载库等知识及面试经验、招聘、内推等信息。This repository is a summary of the basic knowledge of recruiting job seekers and beginners in the direction of C/C++ technology, including language, program library, data structure, algorithm, system, network, link loading library, interview experience, recruitment, recommendation, etc.

siraj_course_how-to-do-linear-regression-using-gradient-descent icon siraj_course_how-to-do-linear-regression-using-gradient-descent

# linear_regression_live This is the code for the "How to Do Linear Regression the Right Way" live session by Siraj Raval on Youtube ## Overview This is the code for [this](https://youtu.be/uwwWVAgJBcM) video on Youtube by Siraj Raval. I'm using a small dataset of student test scores and the amount of hours they studied. Intuitively, there must be a relationship right? The more you study, the better your test scores should be. We're going to use [linear regression](https://onlinecourses.science.psu.edu/stat501/node/250) to prove this relationship. Here are some helpful links: #### Gradient descent visualization https://raw.githubusercontent.com/mattnedrich/GradientDescentExample/master/gradient_descent_example.gif #### Sum of squared distances formula (to calculate our error) https://spin.atomicobject.com/wp-content/uploads/linear_regression_error1.png #### Partial derivative with respect to b and m (to perform gradient descent) https://spin.atomicobject.com/wp-content/uploads/linear_regression_gradient1.png ## Dependencies * numpy Python 2 and 3 both work for this. Use [pip](https://pip.pypa.io/en/stable/) to install any dependencies. ## Usage Just run ``python3 demo.py`` to see the results: ``` Starting gradient descent at b = 0, m = 0, error = 5565.107834483211 Running... After 1000 iterations b = 0.08893651993741346, m = 1.4777440851894448, error = 112.61481011613473 ``` ## Credits Credits for this code go to [mattnedrich](https://github.com/mattnedrich). I've merely created a wrapper to get people started.

tensorrtx icon tensorrtx

Implementation of popular deep learning networks with TensorRT network definition API

yolov5 icon yolov5

YOLOv5 in PyTorch > ONNX > CoreML > TFLite

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