Machine learning with CloudCoins

阿新 • • 發佈：2018-12-28

A few months ago I wrote about how we built our wellness app experiment called "Kubecoin". It's a side project, a type of hobby app that we built to take to developer events, to challenge participants to walk more, and to offer an immersive taste of IBM Cloud technology. We updated the app, and renamed it CloudCoins (because it is built on more technology than just Kubernetes) and we're experimenting with it at the Cloud Foundry Summit Europe 2018 in Basel, Switzerland, this week. It is built for iPhone and Android. CloudCoins is a mobile app, backed by a blockchain system that anonymously converts participants' steps into a cryptocurrency, as they walk around the conference.

Machine learning with CloudCoins

A few months ago I wrote about how we built our wellness app experiment called "Kubecoin". It's a side project, a type of hobby app that we built to take t

OReilly.Hands-On.Machine.Learning.with.Scikit-Learn.and.TensorFlow學習筆記彙總

其中用到的知識點我都記錄在部落格中了：https://blog.csdn.net/dss_dssssd 第一章知識點總結： supervised learning k-Nearest Neighbors Linear Regression

Hands-on Machine Learning with Scikit-Learn and TensorFlow（中文版）和深度學習原理與TensorFlow實踐-學習筆記

監督學習：新增標籤。學習的目標是求出輸入與輸出之間的關係函式y=f(x)。樸素貝葉斯、邏輯迴歸和神經網路等都屬於監督學習的方法。監督學習主要解決兩類核心問題，即迴歸和分類。迴歸和分類的區別在於強調一個是連續的，一個是離散的。非監督學習：不新增標籤。學習目標是為了探索樣本資料之間是否

Introduction to Machine Learning with Python/Python機器學習基礎教程_程式碼修改與更新

2.3.1樣本資料集 --程式碼bug及修改意見 import matplotlib.pyplot as plt import mglearn X,y=mglearn.datasets.make_forge() mglearn.discrete_scatter(X[:,0

【Machine Learning with Peppa】分享機器學習，數學，統計和程式設計乾貨

專欄達人授予成功建立個人部落格專欄

Machine Learning with Peppa

把Scala List的幾種常見方法梳理彙總如下，日常開發場景基本上夠用了。建立列表scala> val days = List("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Sat

spark機器學習原始碼 Machine Learning With Spark source code

@rover這個是C++模板 --胡滿超 stack<Postion> path__;這個裡面 ”<> “符號是什麼意思？我在C++語言裡面沒見過呢？初學者，大神勿噴。

Combining Machine Learning with Credit Risk Scorecards

With all the hype around artificial intelligence, many of our customers are asking for some proof that AI can get them better results in areas where other

Machine Learning with Kaggle Kernels

In the last article we introduced Kaggle's primary offerings and proceeded to execute our first "Hello World" program within a Kaggle Kernel. In this artic

Machine Learning with Time Series Data

As with any data science problem, exploring the data is the most important process before stating a solution. The dataset collected had data on Chicago wea

Nvidia looks to transform machine learning with GPUs

Nvidia is no stranger to data crunching applications of its GPU architecture. It's been dominating the AI deep learning development space for years and sat

Machine Learning with GPUs on vSphere

Performance of Machine Learning workloads using GPUs is by no means compromised when running on vSphere. In fact, you can often achieve better aggregate pe

Introduction to Machine Learning with IBM Watson Studio

After logging into Watson Studio, select New Modeler Flow. Enter a name, keep the default settings, and then click Create. Next expand the Import menu, dra

Removing Obstacles to Production Machine Learning with OpnIDS and Dragonfly MLE

Machine learning promises to address many of the challenges faced by network security analysts; however, there are still many obstacles that prevent widesp

Book Review: Machine Learning with Python Cookbook

Additional Considerations The only criticism I can place is that I wish there were more topics covered in the content. Some specific areas I would have li

Attacking Machine Learning with Adversarial Examples

Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they're like o

[Machine Learning with Python] Cross Validation and Grid Search: An Example of KNN

Train model: from sklearn.model_selection import GridSearchCV param_grid = [ # try 6 (3×2) combinations of hyperparameters {'n_neighbors': [3,

[Machine Learning with Python] Data Preparation by Pandas and Scikit-Learn

In this article, we dicuss some main steps in data preparation. Drop Labels Firstly, we drop labels for train set. Here we use drop() method in Pandas li

[Machine Learning with Python] My First Data Preprocessing Pipeline with Titanic Dataset

The Dataset was acquired from https://www.kaggle.com/c/titanic For data preprocessing, I firstly defined three transformers: DataFrameSelector: S

二、《Hands-On Machine Learning with Scikit-Learn and TensorFlow》一個完整的機器學習專案

本章中，你會假裝作為被一家地產公司剛剛僱傭的資料科學家，完整地學習一個案例專案。下面是主要步驟： 1. 專案概述。 2. 獲取資料。 3. 發現並可視化資料，發現規律。 4. 為機器學習演算法準備資料。 5. 選擇模型，進行訓練。 6. 微調模型。 7. 給出解決方案。 8. 部