Deep Learning Cheat Sheet (using Python Libraries)

#DeepLearning Cheat Sheet (using Python Libraries) #abdsc

  • This cheat sheet was produced by DataCamp, and it is based on the Keras library.
  • Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.
  • For other cheat sheets covering all data science topics, click here.

This cheat sheet was produced by DataCamp, and it is based on the Keras library..Keras is an easy-to-use and powerful library for Theano and TensorFlow that pr…
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Google’s plan to best Amazon rests on one particular piece of software

#Google's plan to best #Amazon rests on one piece of software  #aws #cloud #AI

  • Two years later the tool, which is used in building machine-­learning software, underpins many future ambitions of Google and its parent company, Alphabet.
  • But just months after TensorFlow was released to Google’s army of coders, the company also began offering it to the world for free, as an open source tactic.
  • S. Somasegar, a managing director at venture fund Madrona who was previously head of Microsoft’s developer division, says TensorFlow’s prominence poses a genuine challenge to Google’s cloud rivals.
  • The company has created specialized processors to make TensorFlow faster and reduce the power it consumes inside Google’s data centers.
  • Since Google released TensorFlow, its competitors in cloud computing, Microsoft and Amazon, have released, or started supporting, their own free software tools to help coders build machine-learning systems.

Google has pinned its cloud computing hopes on a bit of software that helps programmers build artificial intelligence apps called TensorFlow.
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This machine-learning software has transformed Google, and the rest of the world may be next

Google Stakes Its Future on a Piece of Software #ML #AI #tensorflow #Google

  • Early in 2015, artificial-intelligence researchers at Google created an obscure piece of software called ­TensorFlow.
  • But just months after TensorFlow was released to Google’s army of coders, the company also began offering it to the world for free.
  • S. Somasegar, a managing director at venture fund Madrona who was previously head of Microsoft’s developer division, says TensorFlow’s prominence poses a genuine challenge to Google’s cloud rivals.
  • The company has created specialized processors to make TensorFlow faster and reduce the power it consumes inside Google’s data centers.
  • Since Google released TensorFlow, its competitors in cloud computing, Microsoft and Amazon, have released or started supporting their own free software tools to help coders build machine-learning systems.

Alphabet thinks it can wrest the cloud computing market away from Amazon by helping companies make use of machine learning with a tool called TensorFlow.
Continue reading “This machine-learning software has transformed Google, and the rest of the world may be next”

An Overview of Python Deep Learning Frameworks

An Overview of #Python #DeepLearning Frameworks #KDN

  • I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years.
  • Since Theano aims first and foremost to be a library for symbolic mathematics, Lasagne offers abstractions on top of Theano that make it more suitable for deep learning.
  • Similar to Lasagne, Blocks is a shot at adding a layer of abstraction on top of Theano to facilitate cleaner, simpler, more standardized definitions of deep learning models than writing raw Theano.
  • More recently, the TensorFlow team decided to incorporate support for Keras, the next deep learning library on our list.
  • It’s a loose port of Lua’s Torch library to Python, and is notable because it’s backed by the Facebook Artificial Intelligence Research team (FAIR), and because it’s designed to handle dynamic computation graphs — a feature absent from the likes of Theano, TensorFlow, and derivatives.


Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.

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Google’s latest platform play is artificial intelligence, and it’s already winning

Google’s latest platform play is artificial intelligence, and it’s already winning

  • It wants to wield influence in the wider AI ecosystem, and to do so has put together an impressive stack of machine learning tools — from software to servers — that mean you can build an AI product from the ground up without ever leaving the Google playpen.
  • The heart of this offering is Google’s machine learning software TensorFlow.
  • They attract talent to Google and help make the company’s in-house software the standard for machine learning.
  • “There are technical differences between [different AI frameworks], but machine learning communities live off community support and forums, and in that regard Google is winning,” he tells The Verge.
  • Yesterday, for example, Google announced that Android now has a staggering two billion monthly active users, and to keep the software’s edge, the company is honing it with machine learning.

Google has always used its annual I/O conference to connect to developers in its sprawling empire. It announces new tools and initiatives, sprinkles in a little hype, and then tells those watching:…
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An Overview of Python Deep Learning Frameworks

An Overview of #Python #DeepLearning Frameworks

  • I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years.
  • Since Theano aims first and foremost to be a library for symbolic mathematics, Lasagne offers abstractions on top of Theano that make it more suitable for deep learning.
  • Similar to Lasagne, Blocks is a shot at adding a layer of abstraction on top of Theano to facilitate cleaner, simpler, more standardized definitions of deep learning models than writing raw Theano.
  • More recently, the TensorFlow team decided to incorporate support for Keras, the next deep learning library on our list.
  • It’s a loose port of Lua’s Torch library to Python, and is notable because it’s backed by the Facebook Artificial Intelligence Research team (FAIR), and because it’s designed to handle dynamic computation graphs — a feature absent from the likes of Theano, TensorFlow, and derivatives.


Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.

Continue reading “An Overview of Python Deep Learning Frameworks”

Deep Learning Cheat Sheet (using Python Libraries)

#DeepLearning Cheat Sheet (using #Python Libraries) | @DataScienceCtrl  #Keras #TensorFlow

  • This cheat sheet was produced by DataCamp, and it is based on the Keras library.
  • Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.
  • For other cheat sheets covering all data science topics, click here.

This cheat sheet was produced by DataCamp, and it is based on the Keras library..Keras is an easy-to-use and powerful library for Theano and TensorFlow that pr…
Continue reading “Deep Learning Cheat Sheet (using Python Libraries)”