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机器学习시작!!! 机器学习1일차 이이다!! 에에문룰들룰들룰들다다다다다다다다다다다다다다다다다다다 监督学习>>标签주기적으로적으（训练数据集）ex）고양이사고양이label을구별한다 例）regression로투자프르그램프르그램，-휴대폰배터리측정-回归（회귀） 回归（회귀）란이란：변수변수해해측측해터나터나터나터나영향영향영향영향영향영향영향영향 용이용 无监督学习>> Super이터를보고 tensorflow기초 tensorflow会话（Session）会话（Session.run） tensorflow发行人Tensor（）发行人!!（Session을!!같다같같같같같같））））） 시점에서는v2이고Session에서만v1용사때문에기때문에import tensorflow.compat.v1 as tf \ n tf.disable_v2_behavi

更快的高分辨率神经修复：使用多尺度神经斑块合成的高分辨率图像修复

谷歌研究 该存储库包含发布的代码。 此存储库中的所有数据集均根据CC BY 4.0 International许可发布，可以在以下位置找到： : 。 此存储库中的所有源文件都是根据Apache 2.0许可发布的，其文本可以在LICENSE文件中找到。 由于存储库很大，因此建议您仅下载感兴趣的子目录： SUBDIR=foo svn export https://github.com/google-research/google-research/trunk/$SUBDIR 如果您想提交拉取请求，则需要克隆存储库； 我们建议进行浅表克隆（无历史记录）。 git clone git@github.com:google-research/google-research.git --depth=1 免责声明：这不是Google的官方产品。

TinyML Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers by Pete Warden, Daniel Situnayake Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. * Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures * Work with Arduino and ultra-low-power microcontrollers

Deep Learning For Dummies

XGBoost With Python Discover The Algorithm That Is Winning Machine Learning Competitions

pattern recognition and machine learning 绝对经典的机器学习/模式识别教材，配套完整的学习笔记，习题答案，中文版，英文版电子档)，值得下载

TensorFlow Machine Learning Cookbook by Nick McClureEnglish | 14 Feb. 2017 | ISBN: 1786462168 | 370 Pages Key FeaturesYour quick guide to implementing TensorFlow in your day-to-day machine learning activitiesLearn advanced techniques that bring more accuracy and speed to machine learningUpgrade your knowledge to the second generation of machine learning with this guide on TensorFlowBook DescriptionTensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow.This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.What you will learnBecome familiar with the basics of the TensorFlow machine learning libraryGet to know Linear Regression techniques with TensorFlowLearn SVMs with hands-on recipesImplement neural networks and improve predictionsApply NLP and sentiment analysis to your dataMaster CNN and RNN through practical recipesTake TensorFlow into productionAbout the AuthorNick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow and Caesar's Entertainment. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict an