Security on Android has always been a challenge for Google due to the operating system’s open nature. But in recent years, the company has been gaining ground in its fight against malware and…
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- For that reason, I suggest starting with image recognition tasks in Keras, a popular neural network library in Python.
- Deep learning is a name for machine learning techniques using many-layered artificial neural networks.
- See a plot of AUC score for logistic regression, random forest and deep learning on Higgs dataset (data points are in millions):
In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees.
- Deep learning (that is – neural networks with many layers) uses mostly very simple mathematical operations – just many of them.
- Its mathematics is simple to the point that a convolutional neural network for digit recognition can be implemented in a spreadsheet (with no macros), see: Deep Spreadsheets with ExcelNet.
I teach deep learning both for a living (as the main deepsense.io instructor, in a Kaggle-winning team1) and as a part of my volunteering with the Polish Chi…
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- This is a TensorFlow implementation of the (Variational) Graph Auto-Encoder model as described in our paper:
Graph Auto-Encoders (GAEs) are end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs.
- GAEs have successfully been used for:
GAEs are based on Graph Convolutional Networks (GCNs), a recent class of models for end-to-end (semi-)supervised learning on graphs:
A high-level introduction is given in our blog post:
In order to use your own data, you have to provide
Have a look at the function in for an example.
- In this example, we load citation network data (Cora, Citeseer or Pubmed).
- The original datasets can be found here: and here (in a different format): can specify a dataset as follows:
You can choose between the following models:
Please cite our paper if you use this code in your own work:
gae – Implementation of Graph Auto-Encoders in TensorFlow
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- To prevent the next global malware crisis, an upcoming update will rely on machine learning from more than 400 million computers running Windows 10, Microsoft said Tuesday.
- In its Fall Creators Update, Microsoft will use a wide range of data coming from its cloud programs such as Azure, Endpoint and Office to create an artificial intelligence antivirus that can pick up on malware behavior, said Rob Lefferts, director of program management for Windows Enterprise and Security.
- If new malware is detected on any computer running Windows 10 in the world, Microsoft said it will be able to develop a signature for it and protect all the other users worldwide.
- Microsoft sees artificial intelligence as the next solution for security as attacks get more sophisticated.
- Using cloud data from Microsoft Office to develop malware signatures is crucial, for example, because recent attacks relied on Word vulnerabilities.
An upcoming security update will incorporate machine learning from millions of computers fending off malware, the company says.
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- Researchers in artificial intelligence and machine learning have started to design software that allows computers to learn about causes the way that scientists do.
- In one experiment, we showed preschool children a simple machine with a switch on one side and two disks that spin on top.
- Bayesian inference considers both the strength of new evidence and the strength of your existing hypotheses.
- Both toddlers and scientists hold on to well-confirmed hypotheses, but eventually enough new evidence can overturn even the most cherished idea.
- Several studies show that youngsters integrate existing knowledge and new evidence in this way.
Alison Gopnik, author of “ Making AI Human ” in Scientific American ’s June issue describes the use of Bayesian statistics to outline how youngsters infer the basics of cause and effect.
Continue reading “Artificial Intelligence Helps in Learning How Children Learn”
I am briefly sharing a video from the last TensorFlow Dev Summit in February 2017. My choice has fallen to a presentation by François Chollet of the deep learning library API Keras and its integration with TensorFlow. As Dr. Chollet explains, Keras integrated with TensorFlow promises to streamline deep learning frameworks in ways that will…
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