Google gives everyone machine learning superpowers with TensorFlow 1.0

Google gives everyone machine learning superpowers with TensorFlow 1․0

  • That began to change with the release of a number of open-source machine learning frameworks like Theano, Spark ML, Microsoft’s CNTK, and Google’s TensorFlow.
  • Among them, TensorFlow stands out for its powerful, yet accessible, functionality, coupled with the stunning growth of its user base.
  • With this week’s release of TensorFlow 1.0, Google has pushed the frontiers of machine learning further in a number of directions.
  • In an effort to make TensorFlow a more-general machine learning framework, Google has added both built-in Estimator functionality, and support for a number of more traditional machine learning algorithms including K-means, SVM (Support Vector Machines), and Random Forest.
  • While there are certainly other frameworks like SparkML that support those tools, having a solution that can combine them with neural networks makes TensorFlow a great option for hybrid problems.

Google gives everyone machine learning superpowers with TensorFlow 1.0
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The Top Emerging Technologies To Watch: 2017 To 2021

The Top Emerging Technologies : 2017 To 2021 #CIO #iot #AI #fintech #AR #VR #bigdata

  • Recipients must have client access to read the full report.
  • Systems Of Engagement Technologies Keep Customers Coming Back
  • The report presents Forrester’s top emerging technologies in three groups of five – systems of engagement technologies, systems of insight technologies, and supporting technologies.
  • Invest In Emerging Technologies That Drive Customer Obsession
  • The Top Emerging Technologies To Watch: 2017 To 2021

Recipients must have client access to read the full report. Use Click & Share to grant full access to recipients who are not Forrester clients.
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Baidu Open-Sources Python-Driven Machine Learning Framework

Baidu Open-Sources #Python-Driven #MachineLearning Framework:  #BigData #DataScience

  • Many of the latest machine learning and data science tools purport to be easy to work with compared to previous generations of such frameworks and libraries.
  • PaddlePaddle — “Paddle” stands for “PArallel Distributed Deep LEarning” — was developed by Baidu to augment many of its own products with deep learning.
  • The Linux Foundation is a non-profit consortium enabling collaboration and innovation through an open source development model.
  • Chinese search engine giant Baidu now has an open source project in the same vein: a machine learning system it claims is easier to train and use because it exposes its functions through Python libraries.
  • Baidu touted PaddlePaddle’s speech transcription in Chinese, either for transcribing broadcasts or as a speech-to-text system to replace keyboards in smartphones.

Many of the latest machine learning and data science tools purport to be 
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Apple buys Turi artificial intelligence startup for $200 million

Apple just spent $200 million for more artificial intelligence expertise

  • Apple confirmed the purchase to Geekwire , giving its standard non-denial:Â
  • Apple has heavily invested in artificial intelligence in recent years, especially through purchases.
  • Apple has bought Turi , a Seattle-based machine learning startup.
  • Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans.Â
  • Apple has bought Turi, a Seattle-based…

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@businessinsider: “Apple just spent $200 million for more artificial intelligence expertise”


Apple has bought Turi, a Seattle-based machine learning startup.


Apple buys Turi artificial intelligence startup for $200 million

Distributed deep learning on Spark

Distributed #deeplearning on @ApacheSpark: nice overview by @avulanov @hplabs  @OReillyAI #AI

  • Alexander Ulanov offers an overview of tools and frameworks that have been proposed for performing deep learning on Spark.
  • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners.
  • How Baidu combined Tachyon with Spark SQL to increase speed 30-fold
  • In the O’Reilly training video, the “Hadoop Application Architectures” authors present an end-to-end case study of a clickstream analytics engine to provide a concrete example of how to architect and implement a complete solution with Hadoop.
  • The Lambda Architecture has its merits, but alternatives are worth exploring.

Read the full article, click here.


@bigdata: “Distributed #deeplearning on @ApacheSpark: nice overview by @avulanov @hplabs @OReillyAI #AI”


Alexander Ulanov offers an overview of tools and frameworks that have been proposed for performing deep learning on Spark.


Distributed deep learning on Spark

Laws, Sausages and ConvNets

Laws, Sausages and #ConvNets - great overview #DeepLearning #NeuralNets

  • Convolution is a simple mathematical operation, so the enormous complexity involved in implementing convolutional layers may be surprising.
  • Instead of dealing with networks, I take the point of view that a convolutional layer is simply a differentiable function.
  • Full-blown ConvNets may incorporate a variety of ideas and mechanisms, but in the following I’m going to focus on their very core: convolutional layers.
  • Convolutional Neural Networks (CNNs or ConvNets in short) give the state-of-the-art results in many problem domains.
  • The post is about the nuts and bolts: algorithms, implementations and optimizations.

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@kdnuggets: “Laws, Sausages and #ConvNets – great overview #DeepLearning #NeuralNets”


Laws, like sausages, cease to inspire respect in proportion as we know howthey are made.


Laws, Sausages and ConvNets