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InfoWorld5y
Deep learning vs. machine learning: Understand the differences
Leaving out neural networks and deep learning, which require a much higher level of computing resources, the most common algorithms are Naive Bayes, Decision Tree, Logistic Regression, K-Nearest ...
InfoWorld5y
Deep learning frameworks: PyTorch vs. TensorFlow - InfoWorld
Of all the excellent machine learning and deep learning frameworks available, TensorFlow is the most mature, has the most citations in research papers (even excluding citations from Google ...
ZDNet6y
Salesforce Research: Knowledge graphs and machine learning to power Einstein
Combining graphs and machine learning has been getting a lot of attention lately, especially since the work published by researchers from DeepMind, Google Brain, MIT, and the University of Edinburgh.
IEEE5mon
Comparative Analysis of Federated Learning, Deep Learning, and Traditional Machine Learning Techniques for IoT Malware Detection - IEEE Xplore
This paper addresses the challenge of classifying Internet of Things (IoT) binary files as either malware or benign using graph-theoretical features derived from Control Flow Graphs (CFGs). We use a ...
ZDNet6y
Google ponders the shortcomings of machine learning | ZDNET
The paper, "Relational inductive biases, deep learning, and graph networks," posted on the arXiv pre-print service, is authored by Peter W. Battaglia of Google's DeepMind unit, along with ...
IEEE4mon
Comparative Analysis of Federated Learning, Deep Learning, and Traditional Machine Learning Techniques for IoT Malware Detection
This paper addresses the challenge of classifying Internet of Things (IoT) binary files as either malware or benign using graph-theoretical features derived from Control Flow Graphs (CFGs). We use a ...