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Data Science Weekly – Issue 165

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Data Science Weekly – Issue 165

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Curated news, articles and jobs related to Data Science. 
Keep up with all the latest developments

Issue #165

Jan 19 2017

Editor Picks

 

  • A short history of color theory
    Although a basic understanding of the color spectrum is rather easy to develop, color theory is an almost infinitely complex subject with roots in both science and art. It can therefore be a daunting task to learn about color composition in a way that is true to both art history and scientific truth…

 


 

A Message from this week's Sponsor:

DataScience.com

 

 

 


 

Data Science Articles & Videos

 

  • Scraping for Craft Beers
    If you have read some of my posts in the past, you know by now that I enjoy a good craft beer. I decided to mix business with pleasure and write a tutorial about how to scrape a craft beer dataset from a website in Python…
  • The More You Know: Using Knowledge Graphs for Image Classification
    Humans have the remarkable capability to learn a large variety of visual concepts, often with very few examples, whereas current state-of-the-art vision algorithms require hundreds or thousands of examples per category and struggle with ambiguity. One characteristic that sets humans apart is our ability to acquire knowledge about the world and reason using this knowledge. This paper investigates the use of structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves performance on image classification…
  • Scaling Recommendation Engine: 15,000 to 130M Users in 24 Months
    Delivering users with precise product recommendations (recs) is the creative force that drives Retention Science to continue to iterate, improve and innovate. In this post, our team unveils our iteration from a minimum viable product to a production-ready solution…

 


 

Jobs

 

  • Data Scientist & Machine Learning Researcher – American Express – NYC

    As a Data Scientist in the Machine Learning and Data Science Team, you will help American Express accelerate its digital transformation. You will be challenged with designing winning data products and developing new big data capabilities that will elevate American Express to the forefront of the digital revolution…

 


 

Training & Resources

 

  • The Anatomy of Deep Learning Frameworks
    In this post, I have tried to sketch out these common principles which would help you better understand the frameworks and for the brave hearts among you, provide a guide on how to implement your own deep learning framework…

  • Machine Learning for Artists
    In general, this book will try to minimize the use of math, and rely on visual aides more than equations, both because neural networks can be well understood this way, and because it helps reduce the need for other qualifications…

 


 

Books

 

  • Algorithms for Data Science

    "This groundbreaking textbook on practical data analytics unites fundamental principles, algorithms, and data. Programming fluency and experience with real and challenging data sets are gained through more than 20 Python and R tutorials and lots of exercises with solutions."...

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.

 


 
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details 🙂 – All the best, Hannah & Sebastian

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Explore Stackdriver Monitoring data with Cloud Datalab

By | machinelearning, TensorFlow | No Comments

Google Stackdriver Monitoring allows users to create charts and alerts on monitoring metrics gathered across their Google Cloud Platform (GCP) and Amazon Web Services environments. Stackdriver users who want to drill deeper into their monitoring data can use Cloud Datalab, an easy-to-use tool for large-scale data exploration, analysis and visualization. Based on Jupyter (formerly IPython), Cloud Datalab allows you access to a thriving ecosystem, including Google BigQuery and Google Cloud Storage, plus many statistics and machine learning packages, including TensorFlow. We include notebooks of detailed tutorials to help you get started with your Stackdriver data, and the vibrant Jupyter community is a great source for more published notebooks and tips.

Libraries from the Jupyter community open up a variety of visualization options. For example, a heatmap is a compact representation of data, often used to visually highlight patterns. With a few lines of code included in the sample notebook, Getting Started.ipynb, we can visualize utilization across different instances to look for opportunities to reduce spend.

The Datalab environment also makes it possible to do advanced analytics. For example, in the included notebook, Time-shifted data.ipynb, we walk through time-shifting the data by day to compare today vs. historical data. This powerful analysis allows you to identify anomalies in your system metrics at a glance, by visualizing how they change from their historical values.

Compare today’s CPU utilization to the weekly average by zone

Stackdriver metrics, viewed with Cloud Datalab


Get started

The first step is to sign up for a 30-day free trial of Stackdriver Premium, which can monitor workloads on GCP and AWS. It takes two minutes to set up. Next, set up Cloud Datalab, which can be easily configured to run on Docker with this Quickstart. Sample code and notebooks for exploring trends in your data, analyzing group performance and heat map visualizations are included in the Datalab container.

Let us know what you think, and we’ll do our best to address your feedback and make analysis of your monitoring data even simpler for you.



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Data Science Weekly – Issue 164

By | machinelearning, TensorFlow | No Comments

Data Science Weekly – Issue 164

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Curated news, articles and jobs related to Data Science. 
Keep up with all the latest developments

Issue #164

Jan 12 2017

Editor Picks

 

  • TensorKart: self-driving MarioKart with TensorFlow
    This winter break, I decided to try and finish a project I started a few years ago: training an artificial neural network to play MarioKart 64. It had been a few years since I’d done any serious machine learning, and I wanted to try out some of the new hotness (aka TensorFlow) I’d been hearing about. The timing was right…
  • Five 2016 Trends We Expect to Come to Fruition in 2017
    The start of a new year is an excellent occasion for audacious extrapolation. Based on 2016 developments, what do we expect for 2017? This blog post covers five prominent trends: Deep Learning Beyond Cats, Chat Bots – Take Two, All the News In The World – Turning Text Into Action, The Proliferation of Data Roles, and What Are You Doing to My Data?…

 


 

A Message from this week's Sponsor:

Yhat

 

 

 


 

Data Science Articles & Videos

 

  • Analyzing Emotions using Facial Expressions in Video with Microsoft AI and R
    The Emotion API uses Deep Convolutional Neural Network based model that has been trained by a number of images that were pre-labeled with universal expressions.
    We thought this was super cool and wanted to give it a try for ourselves. The original post was using Python partially, but we couldn’t see any reason why we couldn’t do all in R, so one of our team member, Yosuke, has quickly taken the original code and translated it all in R…
  • My Experience as a Freelance Data Scientist
    Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing (2015). In my most recent response, I was a little more detailed than usual, so I figured it’d make sense as a blog post too…
  • Is Google Hyping it?
    Why Deep Learning cannot be Applied to Natural Languages Easily

    Neural networks (NNs), recently referred to as deep learning, only work “effectively” with data that is produced from a process of a continuous function.

    My article should actually stop here with one sentence. However, there is so much hype, sadly, keeping the entire AI industry busy, not to mention some announcements from big players like Google and IBM. Not knowing what they are doing exactly forces us to give them the benefit of the doubt for now. Nevertheless, NNs are not a natural fit for natural languages and knowledge representation as I explained below in layman’s terms…

  • king – man + woman is queen; but why?
    word2vec is an algorithm that transforms words into vectors, so that words with similar meaning end up laying close to each other. Moreover, it allows us to use vector arithmetics to work with analogies, for example the famous king – man + woman = queen. I will try to explain how it works, with special emphasis on the meaning of vector differences, at the same time omitting as many technicalities as possible…
  • An economics analogy for why adversarial examples work
    One of the most interesting results from “Explaining and Harnessing Adversarial Examples” is the idea that adversarial examples for a machine learning model do not arise because of the supposed complexity or nonlinearity of the model, but rather because of high dimensionality of the input space. I want to take a stab at explaining “Explaining”’s result with an economics analogy. Take it with a grain of salt, since I have little to no formal training in either machine learning or economics. Let’s go!…
  • How to do an NLG Evaluation: Human Ratings in Artificial Context
    The quickest, cheapest, and most common type of human NLG evaluation is to ask human subjects to rate NLG texts in an artificial context (ie, not in the context of actually using the texts in a real-world context). I give advice here on how to conduct such a study…

 


 

Jobs

 

  • Data Scientist – Airtime – New York

    We are pioneering a new social experience, designed for togetherness. It’s an intimate space for people to share conversations and content in real time. A place for us to truly be together. This is Airtime.

    Our company was founded a few years ago by Sean Parker and Shawn Fanning and is backed by Kleiner Perkins, Andreessen Horowitz, Google Ventures, Founders Fund, and a host of other amazing partners.

    Airtime is built on some amazing new technology crafted by a world-class team of brainiacs in Palo Alto and New York City.

    We’re well-funded, running at full sprint, and looking for extraordinary people to join us on this exciting adventure!…

 


 

Training & Resources

 

  • A comprehensive introduction to data wrangling
    You may have heard the term data wrangling before. This example-filled guide will help you understand what exactly it is, and how you can start doing some data wrangling yourself, with plenty of code examples for you to follow along…

 


 

Books

 

  • Weapons of Math Destruction

    "A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life — and threaten to rip apart our social fabric"...

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.

 


 
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details 🙂 – All the best, Hannah & Sebastian

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TensorFlow 0.12 adds support for Windows

By | machinelearning, TensorFlow | No Comments

Posted by Derek Murray, Software Engineer
Today we are launching preliminary Windows support for TensorFlow.

Native support for TensorFlow on Windows was one of the first requests we received after open-sourcing TensorFlow. Although some Windows users have managed to run TensorFlow in a Docker container, we wanted to provide a more complete experience including GPU support.

With the release of TensorFlow r0.12, we now provide a native TensorFlow package for Windows 7, 10, and Server 2016. This release enables you to speed up your TensorFlow training with any GPU that runs CUDA 8.

We have published the latest release as a pip package in PyPI, so now you can install TensorFlow with a single command:

     C:> pip install tensorflow

And for GPU support:

     C:> pip install tensorflow-gpu

More details about Windows support and all of the other new features in r0.12 are included in the release notes.

We’re excited to offer more people the opportunity to use TF at maximum speed. Follow us on Twitter to be the first to hear about future releases – we’re @tensorflow.

Acknowledgements

Many people have contributed to making this release possible. In particular, we’d like to thank Guenther Schmuelling and Vit Stepanovs from Microsoft for their significant contributions to Windows support.



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Google and Intel announce strategic alliance to accelerate cloud adoption in the Enterprise

By | machinelearning, TensorFlow | No Comments

Today, the Google Cloud team is excited to announce a strategic alliance with Intel to support and accelerate enterprise adoption of the cloud. We’ve worked closely with Intel for years on datacenter processor technology, and are now expanding our collaboration to help enterprise customers move from legacy infrastructure to an open, secure and future-proof cloud. The alliance will focus on technology integrations and joint market education efforts.

Google and Intel have been working closely for many years to engineer and validate processor technology within the data center. With this new alliance, we’ll explore technology solutions for our enterprise customers in the areas of Kubernetes, machine learning, IoT and security, combining Google Cloud software capabilities with Intel’s advanced hardware. The alliance will focus on:

  • Kubernetes – Optimizing Kubernetes for Intel architecture to support a broad range of workloads. Intel is a major contributor to the Kubernetes ecosystem and enables enterprises to run OpenStack as a managed Kubernetes workload. Intel and Google engineers have already delivered code optimizations that have improved feature transparency on Intel architecture. This work is also expected to improve workload capabilities, including virtual network performance and prioritization of shared resources.

  • Machine Learning – Accelerating TensorFlow performance on Intel processors, for example by allowing TensorFlow to leverage all CPU cores and vector lanes for improved parallelism, integrating high-performance libraries such as the Math Kernel library (MKL) into TensorFlow, and optimizing memory allocation and data layer operations across a range of topologies.

  • IoT – Creating a secure platform for connecting Intel’s IoT edge devices to Google Cloud Platform (GCP), where the data can be analyzed to inform better decisions — a critical capability for industries like retail and manufacturing.

  • Security – Enhancing security integrations between Intel hardware and GCP infrastructure that will further improve security for enterprise customers.

In addition to exploring a number of new joint cloud solutions, with Intel we’re focused on developing technical education and market development materials that support the IT practitioners who are managing the transition to a multi-cloud world.

By deepening Google’s unique relationship with Intel, we can better help enterprises transition to the cloud.



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Data Science Weekly – Issue 156

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Data Science Weekly – Issue 156

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Curated news, articles and jobs related to Data Science. 
Keep up with all the latest developments

Issue #156

Nov 17 2016

Editor Picks

 

  • Maths Zeroes In On Perfect Cup Of Coffee
    Mathematicians are a step closer to understanding what makes a perfect cup of coffee. Through some complex calculations, they have shone a light on the processes governing how coffee is extracted from grains in a filter machine…
  • Media in the Age of Algorithms
    Since Tuesday’s election, there’s been a lot of finger pointing, and many of those fingers are pointing at Facebook, arguing that their newsfeed algorithms played a major role in spreading misinformation and magnifying polarization. Some of the articles are thoughtful in their criticism, others thoughtful in their defense of Facebook, while others are full of the very misinformation and polarization that they hope will get them to the top of everyone’s newsfeed. But all of them seem to me to make a fundamental error in how they are thinking about media in the age of algorithms…

 


 

A Message from this week's Sponsor:

 

 


 

Data Science Articles & Videos

 

  • Data Scientists Need More Automation
    Many data scientists aren't lazy enough…Whether we are managing production services or running computations on AWS machines, many data scientists are working on computers besides their laptops… And as we all know, a simple solution that works can be preferable to a fragile solution that requires constant maintenance. That said, I suspect many of us aren't lazy enough. We don't spend enough time automating tasks and processes. Even when we don't save time by doing it, we may save mental overhead…
  • Google Arts & Culture Experiments
    Try out experiments at the crossroads of art and technology, created by artists and creative coders with Google Arts & Culture…
  • Designing with Machine Learning
    WeWork Soho, London. A standard 6-person meeting room (C) is adjacent to the brainstorm room covered with whiteboards (D). A variety of meeting spaces is an essential part of the WeWork experience, but finding the right combination can be challenging. The research team is currently developing ways to ensure our spaces have the right mix of meeting spaces in our locations…
  • Bias in ML, and Teaching AI
    Yesterday I gave a super duper high level 12 minutes presentation about some issues of bias in AI. I should emphasize (if it's not clear) that this is something I am not an expert in; most of what I know is by reading great papers by other people (there is a completely non-academic sample at the end of this post). This blog post is a variant of that presentation…
  • Moving machine learning from practice to production
    With growing interest in neural networks and deep learning, individuals and companies are claiming ever-increasing adoption rates of artificial intelligence into their daily workflows and product offerings…That said, I feel that this field suffers from a gulf between appreciating these developments and subsequently deploying them to solve "real-world" tasks…A number of frameworks, tutorials and guides have popped up to democratize machine learning, but the steps that they prescribe often don't align with the fuzzier problems that need to be solved…This post is a collection of questions (with some (maybe even incorrect) answers) that are worth thinking about when applying machine learning in production…
  • Machine-Learning Algorithm Can Show Whether State Secrets Are Properly Classified
    The U.S. State Department generates some two billion e-mails every year. A significant fraction of these contain sensitive or secret information and so have to be classified, a process that is time-consuming and costly. In 2015 alone, it spent $16 billion to protect classified information. AI might be able to determine why information gets either classified or declassified in error…
  • A tensorflow implementation of French-to-English machine translation using DeepMind's ByteNet
    A tensorflow implementation of French-to-English machine translation using DeepMind's ByteNet from the paper Nal et al's Neural Machine Translation in Linear Time. This paper proposed the fancy method which replaced the traditional RNNs with conv1d dilated and causal conv1d, and they achieved fast training and state-of-the-art performance on character-level translation…

 


 

Jobs

 

  • Machine Learning Engineer – HyperScience – NYC

    Our mission is to help our clients run their businesses more efficiently and effectively by introducing our artificial intelligence solutions into their tech stacks. As a serious expert or practitioner in machine learning, working at HyperScience you’ll have the opportunity to solve a diverse set of problems that to date have simply been unsolvable by humans. Moreover, you’ll work on building artificial intelligences capable of identifying and exploiting information no engineer would think of seeking out…

 


 

Training & Resources

 

  • An HDFS Tutorial for Data Analysts Stuck With Relational Databases
    By now, you have probably heard of the Hadoop Distributed File System (HDFS), especially if you are data analyst or someone who is responsible for moving data from one system to another. One of the questions many people ask when first learning about HDFS is: How do I get my existing data into the HDFS? In this article, we will examine how to import data from a PostgreSQL database into HDFS…

 


 

Books

 

 


 
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details 🙂 – All the best, Hannah & Sebastian

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Google Cloud Machine Learning family grows with new API, editions and pricing

By | machinelearning, TensorFlow | No Comments

Google Cloud Machine Learning is one of our fastest growing products areas. Since we first announced our machine learning offerings earlier this year, we’ve released a steady stream of new APIs, tools and services to help you harness the power of machine learning. We’ve seen machine learning transform users’ experiences, accelerate business operations by solving problems that have existed for decades and delight us with novel applications.

The key to creating a culture of innovation is having the right team, technology and strategy in place. To further these efforts, we’re excited to announce the creation of a new Google Cloud Machine Learning group that will be focused exclusively on delivering cloud-based machine learning solutions to all businesses. Fei-Fei Li and Jia Li, two world-renowned researchers in the subject of machine intelligence, will lead this new group.

Building a centralized team within Google Cloud will accelerate our ability to deliver machine learning products and services to enterprise customers in every industry. Today also marks an exciting next step in Google Cloud’s product commitment to make machine learning more accessible for all businesses. We’re excited to introduce:

  • A brand new Cloud Machine Learning API to help people find careers
  • New hardware options to accelerate your machine learning workloads
  • Improved efficiencies and expanded features for our Cloud Translation, Cloud Vision and Cloud Natural Language APIs

Introducing Google Cloud Jobs API

Machine learning presents new opportunities to solve some pretty difficult business problems. Since so much of what every business achieves depends on great employees, how can we help there? What if we could use machine learning to change the nature of finding jobs and hiring people? We think we can.

Hiring is one of the hardest things organizations do. Part of the difficulty comes from a lack of industry standards to define and describe occupations and how they align to specific skills. Over the past year, Google has developed a new machine-learning model that has the potential to greatly improve the recruitment efforts of any company. We call this the Google Cloud Jobs API. It provides businesses with Google-strength capabilities to find, match and recommend relevant jobs to candidates.

In order to provide the most relevant recommendations to job seekers, Cloud Jobs API uses machine learning to understand how job titles and skills relate to one another and what job content, location, and seniority are the closest match to a jobseeker’s preferences. You can learn more about how it works here.

The API is intended for job boards, career sites and applicant tracking systems. Early adopters of Cloud Jobs API are Jibe, Dice and CareerBuilder.

“Large enterprises have come to expect that integrating new solutions takes month or years, and these long implementation cycles are a major roadblock in delivering innovation. Jibe was able to seamlessly deploy the Google Jobs API as a turnkey machine learning solution for one of our customer’s career sites in a matter of 3 weeks, and we expect that implementation time to shrink for future customers.” – Joe Essenfeld, Founder and CEO at Jibe

Dice, a career website that serves opportunities for technology and engineering professionals, is a launch tester of the API to help job candidates browse over 80,000 tech job listings. Tech jobs tend to be complex and skill specific. For example, if a tech professional enters “front-end engineer” in a job search without using typical Boolean standards, search results will also return UI engineer, UI developer, web developer, and UX engineer. Complicated, right? By using the API, Dice will be able to better understand a candidate’s background and preferences and match the tech pro to the right roles.

CareerBuilder, using a prototype that they created with Cloud Jobs API in just 48 hours, found improved, more accurate results when compared to its existing search algorithm. In one test, CareerBuilder chose a top 100 term, “part time,” and compared results using the Google Cloud Jobs API versus their existing solution. Jobs API returned a richer set of results by applying an expanded set of synonyms including “PT.” Another test showcased how Jobs API can refine search results. CareerBuilder has one of the largest repositories of healthcare industry jobs. CareerBuilder tested the terms “CNA psych” (Certified Nurses Assistant) against a dataset and reduced the results returned  delivering only CNA roles in a psychiatric setting  to notably increase accuracy for the job seeker. Based on these results, CareerBuilder is making plans to leverage the API for its customers in the near future.

Cloud Jobs API is now available in a limited alpha. To learn more, visit the Cloud Jobs API page.

Welcoming GPUs for Google Cloud Platform

Machine learning greatly benefits from fast and reliable hardware, and as hardware advances so do the capabilities of machine learning. This is exactly why Google continues to harness hardware innovations that can help to accelerate machine learning applications.

Beginning in 2017, Google Cloud will offer more hardware choices for businesses that want to use Google Cloud Platform (GCP) for their most complex workloads, including machine learning. For Google Compute Engine and Google Cloud Machine Learning, businesses will be able to use GPUs (Graphics Processing Units) that are highly-specialized processors capable of handling the complexities of machine learning applications. Making GPUs available in Google Cloud means that you can focus on solving challenging computational problems while accessing GPU machines from anywhere and only paying for what you need.

In other words, you’ll be able to strap your ML-powered applications to a rocket engine, resulting in faster and more affordable machine learning models. To learn more, visit our GPU page.

Making Cloud Vision API affordable for everyone

Google has been leveraging the latest hardware and tuned algorithms to significantly improve the performance of our Cloud Machine Learning services. Cloud Vision API now takes advantage of Google’s custom TPUs, our custom ASIC built for machine learning, to improve performance and efficiency. These improvements have enabled us to reduce prices for Cloud Vision API by ~80%. By offering the API at a more affordable price-point, more organizations than ever will be able to take advantage of Cloud Vision API to power new capabilities.

Along with the price reductions, we have made significant improvements to our image recognition capabilities over the last six months. For example: the logo detection feature can identify millions of logos and label detection can identify an expanded number of entities, such as landmarks and objects in images.

Since we first introduced Cloud Vision API, we’ve been very happy with the positive feedback and creativity customers have in using it to power their experiences. Since the beta release, businesses have analyzed well over a billion images. Image analysis, the core capability of Vision API, is fundamentally changing how businesses operate and interact with their end-users. We have thousands of customers using the product to do amazing things. For example, the e-discovery firm Platinum IDS uses Cloud Vision API to power content relevancy for millions of paper and digital files and deliver its new e-Discovery app, and Disney has leveraged Vision API as the basis of innovative marketing campaigns.

Now offering Cloud Translation API Premium

Most recently, Google announced the launch of our Google Neural Machine Translation system (GNMT) that uses state-of-the-art training techniques and runs on TPUs to achieve some of the largest improvements for machine translation in the past decade. Now, Google Cloud is offering these capabilities to all partners, developers and businesses with a Premium edition of Cloud Translation API (formerly Google Translate API).

This new edition provides:

  • Highest-quality model that reduces translation errors by more than 55%-85% on several major language pairs
  • Support for up to eight languages (English to Chinese, French, German, Japanese, Korean, Portuguese, Spanish, Turkish) and 16 language pairs. We’ll support more languages in the near future.

The Premium edition is tailored for users who need precise, long-form translation services. Examples include livestream translations, high volume of emails and detailed articles and documents. The Standard edition continues to offer translation in over 100 languages and price-performance that’s ideal for short, real-time conversational text.

To make Cloud Translation API more affordable, we also decreased the price of the standard edition for higher usage volumes. Please visit our pricing page for more information.

Graduating Cloud Natural Language API to general availability

Cloud Natural Language API, our text analysis machine learning service, is now generally available for all businesses. Based on valuable feedback shared by beta testers such as Evernote, a productivity service used by over 200 million people to store billions of notes and attachments, we’re releasing new features:

  • Expanded entity recognition to increase the accuracy at which the API identifies the names of things, such as people, companies, or locations in the text.
  • Granular sentiment analysis with expanded language support to provide sentiment analysis at the sentence level and not just within a document or record.
  • Improved syntax analysis with additional morphologies such as number, gender, person and tense, to improve coreference resolution required of advanced NLP tasks.

Click here to learn more about the technical details and to see the Cloud Natural Language API in action.

Our team is hard at work to enable new machine learning scenarios in the upcoming months. This year alone, we introduced brand new APIs and a fully-managed platform that are now available for all businesses to use. And as users explore our existing machine learning ecosystem, Google continues to invest in research and models that will bring new scenarios to life. We’re committed to quickly delivering new machine learning solutions for businesses in 2017 and beyond. Stay tuned for what’s next.



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Making it easier for anyone to start exploring A.I.

By | machinelearning, TensorFlow | No Comments

Alexander Chen, Creative Lab

With all the exciting A.I. stuff happening, there are lots of people eager to start tinkering with machine learningtechnology. We want to help make it easier for anyone to do that – whether you’re an engineer, hobbyist, student, or someone who’s just curious. But sometimes, it can feel pretty intimidating when you’re just getting started.

That’s why we’ve created a site called A.I. Experiments. The site showcases simple experiments that let anyone play with this technology hands-on, and resources for creating your own experiments.

The experiments show how machine learning can make sense of all kinds of things – images, drawings, language, sound, and more. They were made by people with all different interests – web developers, musicians, game designers, bird sound enthusiasts, data visualizers – with everyone bringing their own ideas for how to use machine learning.

We also want to make it easier for coders to make their own experiments. Many of the projects we’re featuring are built with tools anyone can use, like Cloud Vision API, Tensorflow, and other libraries from the machine learning community. The site has videos by the creators explaining how they work, and links to open-source code to help you get started. To submit something you’ve made, or just play with things other people are making, visit A.I. Experiments.

And if you’re looking for even more inspiration for what’s possible using machine learning, check out these new experiments from our friends in Google Arts & Culture.



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