Apple Machine Learning Journal

  • However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly.
  • An alternative to labelling huge amounts of data is to use synthetic images from a simulator.
  • This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images.
  • We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.
  • Read the article View the article “Improving the Realism of Synthetic Images”

Most successful examples of neural nets today are trained with supervision. However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly. An alternative to labelling huge amounts of data is to use synthetic images from a simulator. This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images. To help close this performance gap, we’ve developed a method for refining synthetic images to make them look more realistic. We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.
Continue reading “Apple Machine Learning Journal”

Building AI: 3 theorems you need to know – DXC Blogs

Building #AI: 3 theorems you need to know #MachineLearning

  • The mathematical theorem proving this is the so-called “no-free-lunch theorem” It tells us that if a learning algorithm works well with one kind of data, it will work poorly with other types of data.
  • In a way, a machine learning algorithm projects its own knowledge onto data.
  • In machine learning, overfitting occurs when your model performs well on training data, but the performance becomes horrible when switched to test data.
  • Any learning algorithm must also be a good model of the data; if it learns one type of data effectively, it will necessarily be a poor model — and a poor student – of some other types of data.
  • Good regulator theorem also tells us that determining if inductive bias will be beneficial or detrimental for modeling certain data depends on whether the equations defining the bias constitute a good or poor model of the data.

Editor’s note: This is a series of blog posts on the topic of “Demystifying the creation of intelligent machines: How does one create AI?” You are now reading part 3. For the list of all, see here: 1, 2, 3, 4, 5, 6, 7.
Continue reading “Building AI: 3 theorems you need to know – DXC Blogs”

The Microsoft Cognitive Toolkit 2.0 Is Now Generally Available with Keras Support

The Microsoft Cognitive Toolkit 2.0 Is Now Generally Available with Keras Support

  • That was the inspiration behind the company’s Cognitive Toolkit (previously CNTK) for deep learning, and on Thursday it got a major upgrade.
  • The Microsoft Cognitive Toolkit 2.0 is now generally available, open-source.
  • Though version 2 of the toolkit has been in beta since October, the full release builds on previous functionality.
  • It improves the performance for neural nets outside of speech recognition and also makes it easier for Microsoft to extend it later.
  • With this release, utilization should only grow, as will the toolkit’s functionality.

The general available of the Microsoft Cognitive Toolkit 2.0 adds a number of new features, including Java language bindings for model evaulation, Keras support, performance improvements, and more.
Continue reading “The Microsoft Cognitive Toolkit 2.0 Is Now Generally Available with Keras Support”

A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1

Understanding Convolutional Neural Networks Part 1  via @kdnuggets #DataScience #deeplearning

  • The filters on the first layer convolve around the input image and “activate” (or compute high values) when the specific feature it is looking for is in the input volume.
  • Remember, what we have to do is multiply the values in the filter with the original pixel values of the image.
  • As the filter is sliding, or convolving , around the input image, it is multiplying the values in the filter with the original pixel values of the image (aka computing element wise multiplications ).
  • Image classification is the task of taking an input image and outputting a class (a cat, dog, etc) or a probability of classes that best describes the image.
  • When a computer sees an image (takes an image as input), it will see an array of pixel values.


Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.

Continue reading “A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1”

A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1

A Beginner’s Guide To Understanding Convolutional #NeuralNetworks Part 1  #DeepLearning

  • The filters on the first layer convolve around the input image and “activate” (or compute high values) when the specific feature it is looking for is in the input volume.
  • Remember, what we have to do is multiply the values in the filter with the original pixel values of the image.
  • As the filter is sliding, or convolving , around the input image, it is multiplying the values in the filter with the original pixel values of the image (aka computing element wise multiplications ).
  • Image classification is the task of taking an input image and outputting a class (a cat, dog, etc) or a probability of classes that best describes the image.
  • When a computer sees an image (takes an image as input), it will see an array of pixel values.


Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.

Continue reading “A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1”

KDnuggets™ News 16:n30, Aug 17: Why Deep Learning Works; Neural Networks with R; Central Limit Theorem for Data Science

KDnuggets™ News 16:n30, Aug 17: Why #DeepLearning Works; #NeuralNetworks with #rstats

  • ; Central Limit Theorem for Data Science; Cartoon: Make Data Great Again
  • 3 Thoughts on Why Deep Learning Works So Well; A Beginner’s Guide to Neural Networks with R!
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  • KDnuggets@” News 16:n30, Aug 17: Why Deep Learning Works; Neural Networks with R; Central Limit Theorem for Data Science

Read the full article, click here.


@kdnuggets: “KDnuggets™ News 16:n30, Aug 17: Why #DeepLearning Works; #NeuralNetworks with #rstats”


3 Thoughts on Why Deep Learning Works So Well; A Beginner’s Guide to Neural Networks with R!; Central Limit Theorem for Data Science; Cartoon: Make Data Great Again


KDnuggets™ News 16:n30, Aug 17: Why Deep Learning Works; Neural Networks with R; Central Limit Theorem for Data Science