Build a recurrent neural network using Pytorch

Deep learning is vast field that employs artificial neural networks to process data and train a machine learning model. Within deep learning, two learning approaches are used, supervised and unsupervised. This tutorial focuses on recurrent neural networks (RNN), which use supervised deep learning and sequential learning to develop a model. This deep learning technique is especially useful when handling time series data, as is used in this tutorial.
When creating any machine learning model, it’s important to understand the data that you’re analyzing so that you can use the most relevant model architecture. In this tutorial, the goal is to create a basic model that can predict a stock’s value using daily Open, High, Low, and Close values. Because the stock market can be extremely volatile, there are many factors that can influence and contribute to a stock’s value. This tutorial uses the following parameters for the stock data.

Open: The stock’s


Original URL: https://developer.ibm.com/tutorials/build-a-recurrent-neural-network-pytorch/

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OpenAI upgrades its natural language AI coder Codex and kicks off private beta

OpenAI has already made some big changes to Codex, the AI-powered coding assistant the company announced last month. The system now accepts commands in plain English and outputs live, working code, letting someone build a game or web app without so much as naming a variable. A few lucky coders (and, one assumes, non-coders) will be able to kick the tires on this new Codex API in a free private beta.
Codex is best thought of as OpenAI’s versatile language engine, GPT-3, but trained only on code instead of ordinary written material. That lets it do things like complete lines of code or entire sections, but when it was announced it wasn’t really something a non-coder would be able to easily interact with.
That’s changed with this new API, which interprets ordinary, everyday requests like “make the ball bounce off the sides of the screen” or “download that data using the public


Original URL: http://feedproxy.google.com/~r/Techcrunch/~3/2TAhVs0pqnc/

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GitLab acquires UnReview as it looks to bring more ML tools to its platform

DevOps platform GitLab today announced that it has acquired UnReview, a machine learning-based tool that helps software teams recommend the best reviewers for when developers want to check in their latest code. GitLab, which is looking to bring more of these machine learning capabilities to its platform, will integrate UnReview’s capabilities into its own code review workflow. The two companies did not disclose the price of the acquisition.
“Last year we decided that the future of DevOps includes ML/AI, both within the DevOps lifecycle as well as the growth of adoption of ML/AI with our customers,” David DeSanto, GitLab’s senior director, Product Management – Dev & Sec, told me. He noted that when GitLab recently surveyed its customers, 75% of the teams said they are already using AI/ML. The company started by adding a bot to the platform that can automatically label issues, which then led to the team meeting with


Original URL: http://feedproxy.google.com/~r/Techcrunch/~3/C8AtflhWN68/

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Onit acquires legal startup McCarthyFinch to inject AI into legal workflows

Onit, a workflow software company based in Houston with a legal component, announced this week that it has acquired 2018 TechCrunch Disrupt Battlefield alum McCarthyFinch.  Onit intends to use the startup’s AI skills to beef up its legal workflow software offerings.
The companies did not share the purchase price.
After evaluating a number of companies in the space, Onit focused on McCarthyFinch, which gives it an artificial intelligence component the company’s legal workflow software had been lacking. “We evaluated about a dozen companies in the AI space and dug in deep on six of them. McCarthyFinch stood out from the pack. They had the strongest technology and the strongest team,” Eric M. Elfman, CEO and co-founder of Onit told TechCrunch.
The company intends to inject that AI into its existing Aptitude workflow platform.”Part of what really got me excited about McCarthyFinch was the very first conversation I had with their CEO, Nick Whitehouse.


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Majority of Alexa Now Running on Faster, More Cost-Effective Amazon EC2 Inf1 Instances

Today, we are announcing that the Amazon Alexa team has migrated the vast majority of their GPU-based machine learning inference workloads to Amazon Elastic Compute Cloud (EC2) Inf1 instances, powered by AWS Inferentia. This resulted in 25% lower end-to-end latency, and 30% lower cost compared to GPU-based instances for Alexa’s text-to-speech workloads. The lower latency allows Alexa engineers to innovate with more complex algorithms and to improve the overall Alexa experience for our customers.
AWS built AWS Inferentia chips from the ground up to provide the lowest-cost machine learning (ML) inference in the cloud. They power the Inf1 instances that we launched at AWS re:Invent 2019. Inf1 instances provide up to 30% higher throughput and up to 45% lower cost per inference compared to GPU-based G4 instances, which were, before Inf1, the lowest-cost instances in the cloud for ML inference.
Alexa is Amazon’s cloud-based voice service that powers Amazon Echo devices


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/yxWTmcGMxo8/

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Amazon Transcribe Now Supports Automatic Language Identification

In 2017, we launched Amazon Transcribe, an automatic speech recognition service that makes it easy for developers to add a speech-to-text capability to their applications. Since then, we added support for more languages, enabling customers globally to transcribe audio recordings in 31 languages, including 6 in real-time.
A popular use case for Amazon Transcribe is transcribing customer calls. This allows companies to analyze the transcribed text using natural language processing techniques to detect sentiment or to identify the most common call causes. If you operate in a country with multiple official languages or across multiple regions, your audio files can contain different languages. Thus, files have to be tagged manually with the appropriate language before transcription can take place. This typically involves setting up teams of multi-lingual speakers, which creates additional costs and delays in processing audio files.
The media and entertainment industry often uses Amazon Transcribe to convert media content


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/8C4zZN0Ntvs/

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Amazon ECS Now Supports EC2 Inf1 Instances

As machine learning and deep learning models become more sophisticated, hardware acceleration is increasingly required to deliver fast predictions at high throughput. Today, we’re very happy to announce that AWS customers can now use the Amazon EC2 Inf1 instances on Amazon ECS, for high performance and the lowest prediction cost in the cloud. For a few weeks now, these instances have also been available on Amazon Elastic Kubernetes Service.
A primer on EC2 Inf1 instancesInf1 instances were launched at AWS re:Invent 2019. They are powered by AWS Inferentia, a custom chip built from the ground up by AWS to accelerate machine learning inference workloads.
Inf1 instances are available in multiple sizes, with 1, 4, or 16 AWS Inferentia chips, with up to 100 Gbps network bandwidth and up to 19 Gbps EBS bandwidth. An AWS Inferentia chip contains four NeuronCores. Each one implements a high-performance systolic array matrix multiply engine,


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/tabsX62Nap8/

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Amazon launches new Alexa developer tools

Amazon today announced a slew of new features for developers who want to write Alexa skills. In total, the team released 31 new features at its Alexa Live event. Unsurprisingly, some of these are relatively minor but a few significantly change the Alexa experience for the over 700,000 developers who have built skills for the platform so far.
“This year, given all our momentum, we really wanted to pay attention to what developers truly required to take us to the next level of what engaging [with Alexa] really means,” Nedim Fresko, the company’s VP of Alexa Devices & Developer Technologies, told me.
Maybe it’s no surprise then that one of the highlights of this release is the beta launch of Alexa Conversations, which the company first demonstrated at its re:Mars summit last year. The overall idea here is, as the name implies, to make it easier for users to have a natural


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Find Your Most Expensive Lines of Code – Amazon CodeGuru Is Now Generally Available

Bringing new applications into production, maintaining their code base as they grow and evolve, and at the same time respond to operational issues, is a challenging task. For this reason, you can find many ideas on how to structure your teams, on which methodologies to apply, and how to safely automate your software delivery pipeline.
At re:Invent last year, we introduced in preview Amazon CodeGuru, a developer tool powered by machine learning that helps you improve your applications and troubleshoot issues with automated code reviews and performance recommendations based on runtime data. During the last few months, many improvements have been launched, including a more cost-effective pricing model, support for Bitbucket repositories, and the ability to start the profiling agent using a command line switch, so that you no longer need to modify the code of your application, or add dependencies, to run the agent.

You can use CodeGuru in two ways:
CodeGuru Reviewer uses


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/Y06GFelvVHo/

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