# abisen

Follow## Magazines

## Flips

# Iterative methods done right (life’s too short to write for-loops)

# 10 Reasons Why You Should Learn Julia

# Half Integer

# now integrates

# How to Improve Deep Learning Model Robustness by Adding Noise

# EMNLP 2018 Highlights: Inductive bias, cross-lingual learning, and more

# An Introduction to GPU Programming in Julia

# Random noise

# Preprocessing for deep learning: from covariance matrix to image whitening

# Julia Flux for Machine Learning

# Deep Transfer Learning on Small Dataset

# Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

# Monotonicity constraints in machine learning

# Extensible broadcast fusion

# Julia for Probabilistic Metaprogramming

# The Inspection Paradox is Everywhere

# GSoC 2018: Adding Newer Features and Speeding up Convolutions in Flux

# Monte Carlo simulation of airline overbooking

simonensemble.github.io - Mira Khare, Melanie Huynh, Arni Sturluson, Cory Simon

# Monte Carlo Methods in Practice

# Monte Carlo Methods in Practice (Monte Carlo Simulation)

# First-Class Statistical Missing Values Support in Julia 0.7

# Comparing Julia, C++, Fortran, and Python Run Times with Coin Flip Code

# Training a Simple Linear Regression Model From Scratch

# Learning Curves in Linear & Polynomial Regression

# Figure Eight Datasets

# An Introduction to Deep Learning for Tabular Data

# Selection Bias Corrections in Julia, Part 1

# Solve the ‘unsolvable’ with Monte Carlo methods