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Popular Deep Learning Tools – a review
By Ran Bi, NYU on June 18, 2015 in Convolutional Neural Networks, CUDA, Deep Learning, GPU, Pylearn2, Python, Ran Bi, Theano, TorchDeep Learning is the hottest trend now in AI and Machine Learning. We review the popular software for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch.
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Dark Knowledge Distilled from Neural Network
Geoff Hinton never stopped generating new ideas. This post is a review of his research on “dark knowledge”. What’s that supposed to mean?
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Talking Machine – 3 Deep Learning Gurus Talk about History and Future of Machine Learning, part 1
An recent interview from the talking machine podcast with three deep learning experts. They talked about the neural network winter and its renewal.
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Facebook Open Sources deep-learning modules for Torch
By Ran Bi, NYU on February 9, 2015 in Artificial Intelligence, Deep Learning, Facebook, GPU, Neural Networks, NYU, Ran Bi, Torch, Yann LeCunWe review Facebook recently released Torch module for Deep Learning, which helps researchers train large scale convolutional neural networks for image recognition, natural language processing and other AI applications.
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Deep Learning can be easily fooled
It is almost impossible for human eyes to label the images below to be anything but abstract arts. However, researchers found that Deep Neural Network will label them to be familiar objects with 99.99% confidence. The generality of DNN is questioned again.
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Geoffrey Hinton talks about Deep Learning, Google and Everything
A review of Dr. Geoffrey Hinton’s Ask Me Anything on Reddit. He talked about his current research and his thought on some deep learning issues.
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IBM Watson Analytics – Will it Replace Data Scientists ?
We review IBM Watson Analytics Beta version, the service which aims to provide an automated data scientist and intended for business users who want to move beyond spreadsheets for analysis .
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Will Deep Learning take over Machine Learning, make other algorithms obsolete?
Will deep learning will take over machine learning and make other algorithms obsolete, or is it too complex to use on simpler problems? We look at both sides of this discussion.
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Sibyl: Google’s system for Large Scale Machine Learning
A review of 2014 keynote talk about Sibyl, Google system for large scale machine learning. Parallel Boosting algorithm and several design principles are introduced.
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OpenML: Share, Discover and Do Machine Learning
OpenML is designed to share, organize and reuse data, code and experiments, so that scientists can make discoveries more efficiently. It is an interesting idea to build a network of machine learning.
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Ran Bi received MS in Data Science from New York University, where she worked on machine learning, deep learning and big data analytics. She is currently a Data Scientist at AIG.