The hard thing about deep learning

The hard thing about #deeplearning  #machinelearning #datascience

  • In a nutshell: the deeper the network becomes, the harder the optimization problem becomes.
  • The simplest neural network is the single-node perceptron , whose optimization problem is convex .
  • To provably solve optimization problems for general neural networks with two or more layers, the algorithms that would be necessary hit some of the biggest open problems in computer science.
  • There is a rich variety of optimization algorithms to handle convex optimization problems, and every few years a better polynomial-time algorithm for convex optimization is discovered.
  • Judd also shows that the problem remains NP-hard even if it only requires a network to produce the correct output for just two-thirds of the training examples, which implies that even approximately training a neural network is intrinsically difficult in the worst case.

Deeper neural nets often yield harder optimization problems.
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Creating .NET Core Console Application With NoSQL- MongoDB At Back-end

Creating .NET Core #ConsoleApp W/ #NoSQL- #MongoDB At Back-end by @itgyanvriksh  #dotnetcore

  • Just run (Crl + F5) the console application.
  • public string FirstName { get; set; }
  • public string LastName { get; set; }
  • public string City { get; set; }
  • public string Age { get; set; }

In this article, you will learn how to create Microsoft .NET Core console application with NoSQL- MongoDB at the back-end.
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Learn TensorFlow and deep learning, without a Ph.D.

Learn TensorFlow and deep learning, without a Ph.D.

  • The installment covers dense and convolutional networks and is also available as a self-paced codelab If you have 3 hours (recommended; recurrent networks are worth it!
  • Deep learning (aka neural networks) is a popular approach to building machine-learning models that is capturing developer imagination .
  • If you have 1 hour : watch this presentation while following the slide deck .
  • You’ll need both slide decks, Part 1 and Part 2 .
  • Code a neural network yourself with the self-paced codelab

This 3-hour course offers developers a quick introduction to deep-learning fundamentals
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Neural Networks with R – A Simple Example

Neural Networks with R. #BigData #MachineLearning #DataScience #AI #RStats #RLang

  • In this tutorial a neural network (or Multilayer perceptron depending on naming convention) will be build that is able to take a number and calculate the square root (or as close to as possible).
  • As you can see the neural network does a reasonable job at finding the square root, the largest error in in finding the square root of 1 which is out by ~4%
  • The R library ‘neuralnet’ will be used to train and build the neural network.
  • sqrt , testdata ) #Run them through the neural network #Lets see what properties net.sqrt has ls ( net.
  • The tutorial will produce the neural network shown in the image below.

In this tutorial a neural network (or Multilayer perceptron depending on naming convention) will be build that is able to take a number and calculate the square root (or as close to as possible). Later tutorials will build upon this to make forcasting / trading models.The R library ‘neuralnet’ will be used to train and build the neural network.
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11 Great Hadoop, Spark and Map-Reduce Articles

11 Great Hadoop, Spark and Map-Reduce Articles

  • You need to be a member of Data Science Central to add comments!
  • A synthetic variance designed for Hadoop and big data
  • The reference is a part of a new series of DSC articles, offering selected tutorials, references/resources, and interesting articles on subjects such as deep learning, machine learning, data science, deep data science, artificial intelligence, Internet of Things, algorithms, and related topics.
  • Tutorial: How to Become a Data Scientist – On Your Own
  • Implementing a Distributed Deep Learning Network over Spark

This reference is a part of a new series of DSC articles, offering selected tutorials, references/resources, and interesting articles on subjects such as deep…
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Digital Transformation

#DigitalTransformation initiative | #AI #BigData #Industry

  • Introducing the Digital Transformation Initiative
  • Launched in 2015, the initiative offers unique insights into the impact of digital technologies on business and wider society over the next decade.
  • DTI research supports collaboration between the public and private sectors focused on ensuring that digitalization unlocks new levels of prosperity for both industry and society.
  • Introducing Digital Value to Society
  • The Internet of Things and connected devices: making the world smarter

Welcome to the Digital Transformation Initiative (DTI). Launched in 2015, the initiative offers unique insights into the impact of digital technologies on business and wider society over the next decade. DTI research supports collaboration between the public and private sectors focused on ensuring that digitalization unlocks new levels of prosperity for both industry and society.
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What Is The Difference Between Artificial Intelligence And Machine Learning?

What Is The Difference Between #AI And #MachineLearning? Thoughts by @BernardMarr

  • You can fulfill your dreams, not someone else’s expectations of your dreams.
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  • Have your own definition of success…

There is little doubt that Machine Learning (ML) and Artificial Intelligence (AI) are transformative technologies in most areas of our lives. While the two concepts are often used interchangeably there are important ways in which they are different. Let’s explore the key differences between them.
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