Efficient Distributed Deep Learning Using MXNet

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=ScRtj2bNMJE


Duration: 45:18
3,930 views
58

Anima Anandkumar, UC Irvine
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/anima-anandkumar-2017-5-5



Other Videos By Simons Institute for the Theory of Computing


2017-05-04System and Algorithm Co-Design, Theory and Practice, for Distributed Machine Learning
2017-05-04Does Computational Complexity Restrict Artificial Intelligence (AI) and Machine Learning?
2017-05-04Computational Efficiency and Robust Statistics
2017-05-04Robust Estimation of Mean and Covariance
2017-05-04Embedding as a Tool for Algorithm Design
2017-05-03The Imitation Learning View of Structured Prediction
2017-05-03Exponential Computational Improvement by Reduction
2017-05-03Machine Learning Combinatorial Optimization Algorithms
2017-05-03A Cost Function for Similarity-Based Hierarchical Clustering
2017-05-03Machine Learning for Healthcare Data
2017-05-02Efficient Distributed Deep Learning Using MXNet
2017-05-02Your Neural Network Can't Learn Mine
2017-05-02Sampling Polytopes: From Euclid to Riemann
2017-05-02Understanding Generalization in Adaptive Data Analysis
2017-05-02On Gradient-Based Optimization: Accelerated, Distributed, Asynchronous and Stochastic
2017-05-01Stochastic Gradient MCMC for Independent and Dependent Data Sources
2017-05-01Scaling Up Bayesian Inference for Big and Complex Data
2017-05-01Unbiased Estimation of the Spectral Properties of Large Implicit Matrices
2017-05-01Representational and Optimization Properties of Deep Residual Networks
2017-05-01Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference
2017-05-01Variational Inference: Foundations and Innovations


Tags:
Simons Institute
Theory of Computing
Theory of Computation
Theoretical Computer Science
Computer Science
UC Berkeley
Anima Anandkumar
Computational Challenges in Machine Learning