Build models using Jupyter Notebooks in IBM Watson Studio

This tutorial is part of the Getting started with Watson Studio learning path.
Level
Topic
Type
100
Introduction to IBM Watson Studio
Article
101
Data visualization, preparation, and transformation using IBM Watson Studio
Tutorial
201
Automate model building in IBM Watson Studio
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Creating SPSS Modeler flows in IBM Watson Studio
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401
Build models using Jupyter Notebooks in IBM Watson Studio
Tutorial

Introduction
This tutorial explains how to set up and run Jupyter Notebooks from within IBM® Watson Studio. We start with a data set for customer churn that is available on Kaggle. The data set has a corresponding Customer Churn Analysis Jupyter Notebook (originally developed by Sandip Datta), which shows the archetypical steps in developing a machine learning model by going through the following essential steps:
Import the data set.

Analyze the data by creating visualizations and inspecting basic statistic parameters (for example, mean or standard variation).

Prepare the data for machine model building (for example, by transforming categorical features into numeric features and by normalizing the data).

Split the data


Original URL: https://developer.ibm.com/tutorials/watson-studio-using-jupyter-notebook/

Original article

Create your first Assistant-powered chatbot

This article is part of the Watson Assistant learning path.
Level
Topic
Type
100
Introduction to Watson Assistant
Article
101
Create your first Assistant-powered chatbot
Tutorial
200
Create a web-based chatbot with voice input and output
Code pattern
201
Create a banking chatbot
Code pattern
300
Create a Google Action with Watson Assistant
Code pattern
301
Create an Alexa skill with serverless and a conversation
Code pattern
400
Create a next-generation call center with Voice Gateway
Code pattern

Watson Assistant can help you solve a problem by providing an intelligent interface using natural language. You can use the tools provided by the Assistant service with skills that will directly help your customers. The flexibility of the GUI tools and APIs combine to allow you to power applications and tools using AI in simple and powerful ways. The videos in this tutorial explain how to create the Watson Assistant service and how to add intents and entities.
What you’re going to learn
The following video gives a brief explanation of what you’ll create with this tutorial.

Create the Assistant


Original URL: https://developer.ibm.com/tutorials/create-your-first-assistant-powered-chatbot/

Original article

Facebook open sources PyText NLP framework

Facebook AI Research is open sourcing some of the conversational AI tech it is using to power its Portal video chat display and M suggestions on Facebook Messenger.
The company announced today that its PyTorch-based PyText NLP framework is now available to developers.
Natural language processing deals with how systems parse human language and are able to make decisions and derive insights. The PyText framework, which the company sees as a conduit for AI researchers to move more quickly between experimentation and deployment will be particularly useful for tasks like document classification, sequence tagging, semantic parsing and multitask modeling, among others, Facebook says.
The company has built the framework to fit pretty seamlessly into research and production workflows with an emphasis on robustness and low-latency to meet the company’s real-time NLP needs. The product is responsible for models powering more than a billion daily predictions at Facebook.

Another big highlight is the framework’s modularity, allowing it


Original URL: http://feedproxy.google.com/~r/Techcrunch/~3/_l0qJPEBrJI/

Original article

Facebook announces PyTorch 1.0, a more unified AI framework

Though Facebook’s focus on day 1 of its F8 conference centered on the company’s recent struggles and their relationship with the phrase “taking broader responsibility,” day 2 shifted most of the pizazz to the technical advances its giant team has made over the past year.
Today, the company announced PyTorch 1.0, a new iteration of the framework that merges Python-based PyTorch with Caffe2 allowing developers to move from research to production in a more frictionless way without having to deal with migration.
At Facebook, the company’s AI efforts are split between two teams, the Facebook AI Research group (FAIR) and the company’s Applied Machine Learning team (AML). The distinction ultimately boils down to one division researching AI with seemingly limitless computational resources at their disposal and the other looking to implement lightweight machine learning models more suited for consumers. In the past, the former mission has been better-suited for the research-optimized PyTorch


Original URL: http://feedproxy.google.com/~r/Techcrunch/~3/yKxJJgs3XBo/

Original article

Microsoft Open Sources Its Artificial Brain to One-Up Google

Microsoft Open Sources Its Artificial Brain to One-Up Google

The company has open sourced the AI it uses to power speech recognition in its Cortana digital assistant and Skype Translate applications.

The post Microsoft Open Sources Its Artificial Brain to One-Up Google appeared first on WIRED.



Original URL: http://feeds.wired.com/c/35185/f/661370/s/4d236a9f/sc/15/l/0L0Swired0N0C20A160C0A10Cmicrosoft0Etries0Eto0Eone0Eup0Egoogle0Ein0Ethe0Eopen0Esource0Eai0Erace0C/story01.htm

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