100 Days of ML Coding
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Updated
Feb 28, 2022
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100 Days of ML Coding
AiLearning:数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
Python code for common Machine Learning Algorithms
Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation),etc. All codes are implemented intensorflow 2.0.
Vehicle detection using machine learning and computer vision techniques for Udacity's Self-Driving Car Engineer Nanodegree.
The Operator Splitting QP Solver
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
利用pytorch实现图像分类的一个完整的代码,训练,预测,TTA,模型融合,模型部署,cnn提取特征,svm或者随机森林等进行分类,模型蒸馏,一个完整的代码
Java Statistical Analysis Tool, a Java library for Machine Learning
Curso de Introducción a Machine Learning con Python
Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).
I'm submitting a ...
[/] enhancement
Summary
As a result of upgrading the Tensorflow version to 0.15.1, we should refactor all the dataSycn with arraySync. This will greatly improve the overall readability of the code.
你好,根据我个人理解,app启动时间的测试,默认的黑盒标准一般是从click event触发开始。
请教一下根据report中的各类图表,首先要获取到精确的click event的时间戳t1,然后获取到界面加载完成的t2,这样就可以得到Δt
我这边打开Android的轨迹,这样点击时就会有一个圆点,可以用于辅助识别。
根据你的设计理念,如何根据图表分析得出app启动的时间呢?
谢谢。
Regression, Scrapers, and Visualization
Simple machine learning library / 簡單易用的機器學習套件
Ruby language bindings for LIBSVM
도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
SimpleSvmHook is a research purpose hypervisor for Windows on AMD processors.
Pytorch、Scikit-learn实现多种分类方法,包括逻辑回归(Logistic Regression)、多层感知机(MLP)、支持向量机(SVM)、K近邻(KNN)、CNN、RNN,极简代码适合新手小白入门,附英文实验报告(ACM模板)
Open Source Landmarking Library
ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python
TextClf :基于Pytorch/Sklearn的文本分类框架,包括逻辑回归、SVM、TextCNN、TextRNN、TextRCNN、DRNN、DPCNN、Bert等多种模型,通过简单配置即可完成数据处理、模型训练、测试等过程。
Add a description, image, and links to the svm topic page so that developers can more easily learn about it.
To associate your repository with the svm topic, visit your repo's landing page and select "manage topics."
Hi I would like to propose a better implementation for 'test_indices':
We can remove the unneeded np.array casting:
Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))
Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))