Deep Forest: Towards An Alternative to Deep Neural Networks

Alex will be presenting on "Deep FOREST: TOWARDS AN ALTERNATIVE TO DEEP NEURAL NETWORKS" by Zhi-Hua Zhou and Ji Feng. You can read the paper here: https://arxiv.org/pdf/1702.08835.pdf [https://arxiv.org/pdf/1702.08835.pdf]Here's an abstract straight from Alex: "It is much easier to find deep learning hagiography than it is to find an explanation of why it works or even a good definition of the phrase. A better concept, I will argue, is representation learning, and this means approximately that feature engineering is integrated into the training process in an essential way. The linked paper was my original entree to this subject and it provides an example of representation learning which uses decision trees (as opposed to artificial neurons) as it's primitive building block. In my talk, I'll explain what representation learning is in more detail, argue that the algorithms called 'deep learning' are applications of representation learning via neural networks, and demonstrate that this is why techniques like convolutional neural nets are effective. Having set the stage, I will walk through the algorithm described in the paper and give similar insight into why representation learning drives it's effectiveness." Alex Mueller is an ancient SAINT LOUIS townie, mathematician and data scientist. He recently founded a Capnion to develop cryptographic data privacy tools to prevent data breaches. Read more about his new company here: https://www.capnion.com/ [https://www.capnion.com/] Food from Qdoba supplied by Capnion.