Our friends at Pokersites recently shared a blog & Infographic with us, titled: Poker & AI: The Raise of Machines Against Humans. It details insights and research about the evolution of poker artificial intelligence, the history, as well as where it is now. They thought our community might be interested and asked us to share […]
The winner of the 2016 MLconf Industry Impact Student Research Award, which is sponsored by Google has been announced. Our committee has reviewed several nominees and found Tianqi Chen’s research on XGBoost and MXNet to be the most impactful and interesting for future developments in industry. Tianqi Chen is the winner of the 2016 MLconf Industry Impact […]
Morgan & Claypool: Active Learning Algorithms for Reinforcement Learning Analyzing Analytics Automatic Detection of Verbal Deception Bayesian Analysis in Natural Language Processing Essentials of Game Theory: A Concise Multidisciplinary Introduction General Game Playing Graph Mining: Laws, Tools, and Case Studies Graph-Based Semi-Supervised Learning Human Computation Introduction to Intelligent Systems in Traffic and Transportation Introduction to […]
Our past Technical Chair, interviewed Metis Senior Data Scientist Rumman Chowdhury and Metis bootcamp graduate Nathan Wieneke, regarding their 12 week Data Science bootcamp and some of the interesting projects that they’ve had their students working on, including a student success story.
Hussein Mehana, Director of Engineering, Facebook: https://arxiv.org/abs/1502.01710 Patrick Koch, Principal Data Scientist, and Funda Gunes, Sr. Research Statistician Developer, SAS Institute Inc: 1. Bottou, L., Curtis, F. E., Nocedal, J., Optimization Methods for Large-Scale Machine Learning,arXiv:1606.04838 [stat.ML], 2016. 2. Sutskever, I., Martens, J., Dahl, G. and Hinton, G., E. On the importance of initialization and momentum […]
Our past Technical Chair, interviewed Hussein Mehanna, Engineering Director – Core ML, Facebook, regarding his upcoming presentation Applying Deep Learning at Facebook Scale, scheduled for 09/23/16 at MLconf Atlanta. One of the criticism against deep learning models was the complexity of inference. In your talk you will explain how you reduced the inference time. Does this […]