- @fergbyrne
- HTM = Hierarchical Temporal Memory
- Slides
- big data is like teenage sex
- noone knows how to do it
- everyone thinks everyone else is doing it
Inspired by "Parsing CSS with Parsec".
Just quick notes and code that you can play with in REPL.
By @kachayev
#!/bin/bash | |
# install CUDA Toolkit v8.0 | |
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network)) | |
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb" | |
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG} | |
sudo dpkg -i ${CUDA_REPO_PKG} | |
sudo apt-get update | |
sudo apt-get -y install cuda |
The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:
PyTorch defines a new package torch
. In this post we will consider the ._C
module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor
) and to call C/C++ functions.
#!/usr/bin/env bash | |
set -Eeuo pipefail | |
trap cleanup SIGINT SIGTERM ERR EXIT | |
script_dir=$(cd "$(dirname "${BASH_SOURCE[0]}")" &>/dev/null && pwd -P) | |
usage() { | |
cat <<EOF | |
Usage: $(basename "${BASH_SOURCE[0]}") [-h] [-v] [-f] -p param_value arg1 [arg2...] |