This document provides an overview of ROS and Gazebo for robot simulation. It introduces ROS as a framework for robot software development that encourages code reuse. Key ROS concepts covered include nodes, topics, services, and packages. The document demonstrates creating simple ROS packages and nodes that publish, subscribe and use custom messages. Gazebo is introduced as a 3D physics simulator for robot control and environment simulation. Example demonstrations are provided on using Gazebo and ROS for SLAM with the PR2 robot in Rviz. Resources for further learning about ROS and Gazebo are also listed.
Elena Grewal, Data Science Manager, Airbnb at MLconf SF 2016MLconf
Before the Model: How Machine Learning Products Start, with Examples from Airbnb: Often the most important part of building a machine learning product is the formulation of the problem; the most elegant model is rendered useless without the right application and model architecture. Airbnb is an online marketplace for accommodations which has found many interesting applications for machine learning products by taking a data driven approach to investment in Machine learning products. Come hear about how the Airbnb team generates and vets ideas for machine learning products and tailors the product to business problems, with some examples of success and lessons learned along the way.
商業價值主張設計:價值地圖 Value proposition design canvas -Canvas士杰 戴
The document outlines steps for creating a value map, including stepping into a customer's shoes to understand their perspective, mapping how products and services create value for customers, and exercises to assess fit between problems and solutions, products and markets, and business models. It is divided into sections on customer profiling, value creation mapping, and fit assessment exercises.
This document provides an overview of ROS and Gazebo for robot simulation. It introduces ROS as a framework for robot software development that encourages code reuse. Key ROS concepts covered include nodes, topics, services, and packages. The document demonstrates creating simple ROS packages and nodes that publish, subscribe and use custom messages. Gazebo is introduced as a 3D physics simulator for robot control and environment simulation. Example demonstrations are provided on using Gazebo and ROS for SLAM with the PR2 robot in Rviz. Resources for further learning about ROS and Gazebo are also listed.
Elena Grewal, Data Science Manager, Airbnb at MLconf SF 2016MLconf
Before the Model: How Machine Learning Products Start, with Examples from Airbnb: Often the most important part of building a machine learning product is the formulation of the problem; the most elegant model is rendered useless without the right application and model architecture. Airbnb is an online marketplace for accommodations which has found many interesting applications for machine learning products by taking a data driven approach to investment in Machine learning products. Come hear about how the Airbnb team generates and vets ideas for machine learning products and tailors the product to business problems, with some examples of success and lessons learned along the way.
商業價值主張設計:價值地圖 Value proposition design canvas -Canvas士杰 戴
The document outlines steps for creating a value map, including stepping into a customer's shoes to understand their perspective, mapping how products and services create value for customers, and exercises to assess fit between problems and solutions, products and markets, and business models. It is divided into sections on customer profiling, value creation mapping, and fit assessment exercises.