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ELF Format Cheatsheet

Introduction

Executable and Linkable Format (ELF), is the default binary format on Linux-based systems.

Compilation

eknight7

// Copyright 2019 The MediaPipe Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,

asimshankar

Training TensorFlow models in C++

Python is the primary language in which TensorFlow models are typically developed and trained. TensorFlow does have bindings for other programming languages. These bindings have the low-level primitives that are required to build a more complete API, however, lack much of the higher-level API richness of the Python bindings, particularly for defining the model structure.

This file demonstrates taking a model (a TensorFlow graph) created by a Python program and running the training loop in C++.

mugli

1. Hackers & Painters: http://www.paulgraham.com/hp.html

2. Logicomix: https://bd.backpackbang.com/item/1596914521

3. Learn Hapi:

• https://github.com/dwyl/learn-hapi
• https://scotch.io/tutorials/getting-started-with-hapi-js

4. React Boilerplate: https://github.com/facebookincubator/create-react-app
5. React Native Boilerplate: https://facebook.github.io/react-native/blog/2017/03/13/introducing-create-react-native-app.html

subfuzion

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)