MRNet: Deep-learning-assisted diagnosis for knee magnetic resonance imaging Nicholas Bien *, Pranav Rajpurkar *, Robyn L. Ball, Jeremy Irvin, Allison Park, Erik Jones, Michael Bereket, Bhavik N. Patel, Kristen W. Yeom, Katie Shpanskaya, Safwan [...]
Artificial intelligence tracks biological age at every level and rewinds the aging clock Insilico Medicine publishes a new paper presenting the latest advances in artificial intelligence for aging research INSILICO MEDICINE, INC. Monday, December [...]
Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning By Dan Clark, KDnuggets. Here are the top 15 Python libraries across Data Science, Data Visualization. Deep Learning, and Machine Learning. We recently [...]
We all expected that the Summit supercomputer at Oak Ridge National Lab would be a major part of pushing deep learning forward in HPC given its balanced GPU and IBM Power9 profile (not to mention the on-site expertise [...]
Deep Learning: Which Loss and Activation Functions should I use? The purpose of this post is to provide guidance on which combination of final-layer activation function and loss function should be used in a [...]
Summary: If Deep Learning is powered by 2nd generation neural nets. What will the 3rd generation look like? What new capabilities does that imply and when will it get here? By far the fastest expanding frontier [...]
A Big Data Center collaboration between computational scientists at Lawrence Berkeley National Laboratory’s (Berkeley Lab) National Energy Research Scientific Computing Center (NERSC) and engineers at Intel and Cray has yielded another first in the quest to [...]
ODSC East - Boston - May 1-4 2018 ThirdEye is pleased to announce its partnership with Open Data Science Conference (ODSC) and announce the ODSC East event in Boston fromMay 1st through May 4th [...]
Overview Theano is a Python library for fast numerical computation that can be run on the CPU or GPU. It is a key foundational library for Deep Learning in Python that you can use [...]
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD [...]
