- Top KDnuggets tweets, Aug 25-31: How to become a #DataScientist for Free; The R universe of Hadley Wickham - Sep 1, 2015.
How to become a Data Scientist for Free; #BigData is Out, #MachineLearning is in; The universe of Hadley Wickham, the Man Who Revolutionized R; Book review: Fundamentals of #DeepLearning.
- Data Science 101: Preventing Overfitting in Neural Networks - Apr 17, 2015.
Overfitting is a major problem for Predictive Analytics and especially for Neural Networks. Here is an overview of key methods to avoid overfitting, including regularization (L2 and L1), Max norm constraints and Dropout.
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- Inside Deep Learning: Computer Vision With Convolutional Neural Networks - Apr 9, 2015.
Deep Learning-powered image recognition is now performing better than human vision on many tasks. We examine how human and computer vision extracts features from raw pixels, and explain how deep convolutional neural networks work so well.
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- Deep Learning, The Curse of Dimensionality, and Autoencoders - Mar 12, 2015.
Autoencoders are an extremely exciting new approach to unsupervised learning and for many machine learning tasks they have already surpassed the decades of progress made by researchers handpicking features.
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- Genetics as a Social Network – A Data Scientist Perspective - Jan 19, 2015.
You can think about a cell’s genetics as a huge social network. We can then take the DNA sequences of the transcription factor footprints associated with each gene and predict the proteins bound to these regulatory regions, and in this way reconstruct the genetic regulatory networks in every cell type.
- Deep Learning in a Nutshell – what it is, how it works, why care? - Jan 12, 2015.
Deep learning and neural networks are increasingly important concepts in computer science with great strides being made by large companies like Google and startups like DeepMind.