GitHub

A #Java Toolbox for Scalable Probabilistic #MachineLearning

  • The AMIDST Toolbox allows you to model your problem using a flexible probabilistic language based on graphical models.
  • AMIDST Toolbox has been used to track concept drift and do risk prediction in credit operations, and as data is collected continuously and reported on a daily basis, this gives rise to a streaming data classification problem.
  • As an example, the following figure shows how the data processing capacity of our toolbox increases given the number of CPU cores when learning an a probabilistic model (including a class variable C, two latent variables (dashed nodes), multinomial (blue nodes) and Gaussian (green nodes) observable variables) using the AMIDST’s learning engine.
  • As can be seen, using our variational learning engine, AMIDST toolbox is able to process data in the order of gigabytes (GB) per hour depending on the number of available CPU cores with large and complex PGMs with latent variables.
  • If your data is really big and can not be stored in a single laptop, you can also learn your probabilistic model on it by using the AMIDST distributed learning engine based on a novel and state-of-the-art distributed message passing scheme implemented on top of Apache Flink.

toolbox – A Java Toolbox for Scalable Probabilistic Machine Learning
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Book: Machine Learning Algorithms From Scratch

Book: #MachineLearning Algorithms From Scratch

  • From First Principles With Pure Python and

    Use them on Real-World Datasets

    You must understand algorithms to get good at machine learning.

  • In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
  • I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones.
  • (yes I have written tons of code that runs operationally)

    I get a lot of satisfaction helping developers get started and get really good at machine learning.

  • I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.

Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets

$37 USD
You must understand algorithms t…
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Free Machine Learning eBooks

Free #MachineLearning eBooks - March 2017 #abdsc

  • Machine learning is one of the fastest growing areas of computer science, with far-reaching applications.
  • The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
  • The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
  • These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
  • Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Here are three eBooks available for free.
MACHINE LEARNING
Edited by Abdelhamid Mellouk and Abdennacer Chebira
Machine Learning can be defined in various ways…
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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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Is China in the driver’s seat when it comes to AI?

Is #China in the driver’s seat when it comes to #AI?

  • In the final months of the Obama administration, the U.S. government published two separate reports noting that the U.S. is no longer the undisputed world leader in AI innovation and expressing concern about China’s emergence as a major player in the field.
  • The reports recommended increased expenditure on machine learning research and enhanced collaboration between the U.S. government and tech industry leaders to unlock the potential of AI.
  • But despite these efforts, 91 percent of the 1,268 tech founders, CEOs, investors, and developers surveyed at the international Collision tech conference in New Orleans in May 2017 believed that the U.S. government is “fatally under-prepared” for the impact of AI on the U.S. ecosystem.
  • Research firm CB Insights found that Chinese participation in funding rounds for American startups came close to $10 billion in 2016, while recent figures indicate that Chinese companies have invested in 51 U.S. AI companies, to the tune of $700 million.
  • But of further surprise was the 50 percent of all respondents who believed the U.S. would lose its dominant position in the tech world to China within just five years.

In the battle of technological innovation between East and West, artificial intelligence (AI) is on the front line. And China’s influence is growing.
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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.
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Data in, intelligence out: Machine learning pipelines demystified

How machine learning pipelines work: Data in, intelligence out #AI #ML #datascience

  • It’s tempting to think of machine learning as a magic black box.
  • If you’re in the business of deriving actionable insights from data through machine learning, it helps for the process not to be a black box.
  • The more you know what’s inside the box, the better you’ll understand every step of the process for how data can be transformed into predictions, and the more powerful your predictions can be.
  • There’s also a pipeline for data as it flows through machine learning solutions.
  • Mastering how that pipeline comes together is a powerful way to know machine learning itself from the inside out.

Data plus algorithms equals machine learning, but how does that all unfold? Let’s lift the lid on the way those pieces fit together, beginning to end
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