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
Tutorial
301
Creating SPSS Modeler flows in IBM Watson Studio
Tutorial
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

How YACHT fed their old music to the machine and got a killer new album

The band YACHT, named for a mysterious sign seen in Portland around the turn of the century. [credit:
YACHT / Google I/O 2019 ]

The dance punk band YACHT has always felt like a somewhat techy act since debuting in the early 2000s. They famously recorded instrumental versions of two earlier albums and made them available for artists under a Creative Commons license at the Free Music Archive. Post-Snowden, they wrote a song called “Party at the NSA” and donated proceeds to the EFF. One album cover of theirs could only be accessed via fax initially (sent through a Web app YACHT developed to ID the nearest fax to groups of fans; OfficeMax must’ve loved it). Singer Claire L. Evans literally wrote the book (Broad Band) on female pioneers of the Internet.
So when Evans showed up at Google I/O this summer, we


Original URL: https://arstechnica.com/?p=1560697

Original article

Amazon Forecast – Now Generally Available

Getting accurate time series forecasts from historical data is not an easy task. Last year at re:Invent we introduced Amazon Forecast, a fully managed service that requires no experience in machine learning to deliver highly accurate forecasts. I’m excited to share that Amazon Forecast is generally available today!
With Amazon Forecast, there are no servers to provision. You only need to provide historical data, plus any additional metadata that you think may have an impact on your forecasts. For example, the demand for a particular product you need or produce may change with the weather, the time of the year, and the location where the product is used.
Amazon Forecast is based on the same technology used at Amazon and packages our years of experience in building and operating scalable, highly accurate forecasting technology in a way that is easy to use, and can be used for lots of different use cases, such as


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/ZQy1XBIBmms/

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

Amazon Polly Introduces Neural Text-To-Speech and Newscaster Style

From Robbie the Robot to Jarvis, science fiction writers have long understood how important it was for an artificial being to sound as lifelike as possible. Speech is central to human interaction, and beyond words, it helps us express feelings and emotions: who can forget HAL 9000’s haunting final scene in 2001: A Space Odyssey?
In the real world, things are more complicated of course. Decades before the term ‘artificial intelligence’ had even been coined, scientists were designing systems that tried to mimic the human voice. In 1937, almost 20 years before the seminal Dartmouth workshop, Homer Dudley invented the Voder, the first attempt to synthesize human speech with electronic components: this video has sound samples and extra information on this incredible device.
We’ve come a long way since then! At AWS re:Invent 2016, we announced Polly, a managed service that turns text into lifelike speech, allowing customers to create


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/I9vl0mGfmJU/

Original article

Facebook updates PyTorch with a focus on production use

During last year’s F8 developer conference, Facebook announced the 1.0 launch of PyTorch, the company’s open-source deep learning platform. At this year’s F8, the company launched version 1.1. The small increase in version numbers belies the importance of this release, which focuses on making the tool more appropriate for production usage, including improvements to how the tool handles distributed training.
“What we’re seeing with PyTorch is an incredible moment internally at Facebook to ship it and then an echo of that externally with large companies,” Joe Spisak, Facebook AI’s product manager for PyTorch, told me. “Make no mistake, we’re not trying to monetize PyTorch […] but we want to see PyTorch have a community. And that community is starting to shift from a very research-centric community — and that continues to grow fast — into the production world.”
So with this release, the team and the more than 1,000 open-source committers that


Original URL: http://feedproxy.google.com/~r/Techcrunch/~3/EIcmuFj3yBM/

Original article

OpenAI shifts from nonprofit to ‘capped-profit’ to attract capital

OpenAI may not be quite so open going forward. The former nonprofit announced today that it is restructuring as a “capped-profit” company that cuts returns from investments past a certain point. But some worry that this move — or rather the way they made it — may result in making the innovative company no different from the other AI startups out there.
From now on, profits from any investment in the OpenAI LP (limited partnership, not limited profit) will be passed on to an overarching nonprofit company, which will disperse them as it sees fit. Profits in excess of a 100x return, that is.
In simplified terms, if you invested $10 million today, the profit cap will come into play only after that $10 million has generated $1 billion in returns. You can see why some people are concerned that this structure is “limited” in name only.
In a blog post, OpenAI explained


Original URL: http://feedproxy.google.com/~r/Techcrunch/~3/8mJ4a2Hh0jA/

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

Amazon Comprehend adds customized language lists to machine learning tool

Last year Amazon announced Comprehend, a natural language processing tool to help companies extract common words and phrases from a corpus of information. Today, a week ahead of its Re:invent customer conference, Amazon announced an enhancement to Comprehend that allows developers to build lists of specialized words and phrases without machine learning domain knowledge.
“Today we are excited to bring new customization features to Comprehend, which allow developers to extend Comprehend to identify natural language terms and classify text which is specialized to their team, business or industry,” Matt Wood, GM for deep learning and AI wrote in a blog post announcing the enhancement.
The key aspect of this is that Amazon is handling all of the complexity, allowing developers to add customized lists without having deep machine learning or natural language processing background. “Under the hood, Comprehend will do the heavy lifting to build, train, and host the customized machine learning


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

Original article

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