100 Days of ML Coding
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Updated
May 14, 2020 - Python
100 Days of ML Coding
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
Python code for common Machine Learning Algorithms
Documents, papers and codes related to NLP, 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.
Java Statistical Analysis Tool, a Java library for Machine Learning
The Operator Splitting QP Solver
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.
Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).
Curso de Introducción a Machine Learning con Python
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.
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。
Open Source Landmarking Library
Regression, Scrapers, and Visualization
Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language
A minimalistic educational hypervisor for Windows on AMD processors.
Interactive SVM Explorer, using Dash and scikit-learn
Scene text detection and recognition based on Extremal Region(ER)
SimpleSvmHook is a research purpose hypervisor for Windows on AMD processors.
LinearGo (Go wrapper for LIBLINEAR): A Library for Large Linear Classification
real-time Vehicle Detection( tiny YOLO ver) and HOG+SVM method
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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)))