Kevin Noe

Batch renormalization in Deep Learning - June 2017

Neural Network Super Sheet

Deep Learning: Grid LSTM

Recent articles on Deep Learning progress

Entropy, Deep Learning vs Machine Learning

Tensor / Factorization Machines for retail

Symbolic regression

Deep Learning and Renormalization Group

2013 paper on Deep learning and Renormalization Group :
by Cedric Beny

Renormalization group (RG) methods, which model the way in which the effective
behavior of a system depends on the scale at which it is observed, are key to
modern condensed-matter theory and particle physics.


https://arxiv.org/pdf/1301.3124.pdf

Insight on Deep Learning: Renormalization Group in Physics.

We investigate the random generators used in Finance: Mersenne Twister and Sobol Quasi Random Generator. We focus the analysis on the statistical properties of the random numbers generated at high dimension and over a wide range of dimensions (From 1 to 20000). We describe the degenerate patterns of random numbers produced by Sobol Generator and Randomized Sobol Generator across a wide series of dimension (800 dimension pairs), leading to charaterize those patterns. We provide an algorithm to filter Sobol sequences. Additionally, we mention the used cases in Finance, especially, we highlight the dimensions typically encountered in quantative finance model simulations. We highlight the simulation with CPU/GPU (Graphic Processor Unit) for random numbers generation and leading some conclusions on their practical usage.

Analysis of Random Generators in Monte Carlo Simulation: Mersenne Twister and Sobol

Equations that changed the World

L0, L1 Cache in CPU memory

Alien mathematics

Prefered piece of Python code

Secret Python function

Good Stochastic Analysis Book

Another interesting boook

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GCE - Coogle Compute Engine

Deep Learning

Genetic Algorithm

geo-iot

time series

kalman

Kevin Noe

Kevin Noe

Machine Learning / Quantitative Analysis / Algo 専門家

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