palashahuja

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palashahuja

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palashahuja

" - For Neovim: stdpath('data') . '/plugged'
" - Avoid using standard Vim directory names like 'plugin'
call plug#begin('~/.vim/plugged')
Plug 'dracula/vim', { 'as': 'dracula' }
Plug 'jceb/vim-orgmode'
Plug 'tpope/vim-speeddating'
call plug#end()

set relativenumber
colorscheme dracula

palashahuja

# Reference
# =========
# Why does deep and cheap learning work so well?∗
# Henry W. Lin, Max Tegmark, and David Rolnick
# Dept. of Physics, Harvard University, Cambridge, MA 02138
# Dept. of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139 and Dept. of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139
# Here the x input takes two numbers and produces the multiplication as output
import numpy as np

# multiplication approximator

palashahuja

 template<
     typename PerformanceFunction,
     typename OutputLayerType,
     typename PerformanceFunctionType,
     typename MatType = arma::mat
>
void BuildDropConnectNetwork(MatType& trainData,
                             MatType& trainLabels,
                             MatType& testData,
                             MatType& testLabels,

palashahuja

if evidence:
            for evidence_var in evidence:
                for factor in working_factors[evidence_var]:
                    factor_reduced = factor.reduce('{evidence_var}_{state}'.format(evidence_var=evidence_var,
                                                                                   state=evidence[evidence_var]),
                                                   inplace=False)
                    for var in factor_reduced.scope():
                        working_factors[var].remove(factor)
                        working_factors[var].add(factor_reduced)
                del working_factors[evidence_var]