Pinned Repositories
bigdata
started 17 oct 2016
connected-cars
connected cars d3js project
deep-NLP-copy-from-cahya
a copy of the cahya's repository of deep NLP
deep-regression-from-sonaam1234
PRONE-TO-OVERFITTING---pure copy of deep regression work
hello-world
polynomial_solver_degree_smaller_than_4
This code enables to find the roots of low degree polynomials. This is an implementation of Cardano's formulas (degree 3 poly.) and Euler formulas (degree 4 poly.). Degree 2, 1 and 0 are also dealt with. The main difference with numpy.roots is that it computes the roots with floats. It may be a very (extremely ?) small difference. The problem I encountered is that numpy.roots seems to return 0.0 results for very small coefficient whereas, for all it is good for, using float you do get usually non zero results. Indeed the result are approximate as always.
tensorflow
Computation using data flow graphs for scalable machine learning
mage
Magic Another Game Engine
starter-kit-datascience
drwi's Repositories
drwi/bigdata
started 17 oct 2016
drwi/connected-cars
connected cars d3js project
drwi/deep-NLP-copy-from-cahya
a copy of the cahya's repository of deep NLP
drwi/deep-regression-from-sonaam1234
PRONE-TO-OVERFITTING---pure copy of deep regression work
drwi/hello-world
drwi/polynomial_solver_degree_smaller_than_4
This code enables to find the roots of low degree polynomials. This is an implementation of Cardano's formulas (degree 3 poly.) and Euler formulas (degree 4 poly.). Degree 2, 1 and 0 are also dealt with. The main difference with numpy.roots is that it computes the roots with floats. It may be a very (extremely ?) small difference. The problem I encountered is that numpy.roots seems to return 0.0 results for very small coefficient whereas, for all it is good for, using float you do get usually non zero results. Indeed the result are approximate as always.
drwi/tensorflow
Computation using data flow graphs for scalable machine learning