Github Trending and Good Stuff 2019-08-07

jupyter-notebook

NervanaSystems / distiller

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://nervanasystems.github.io/distiller
Good Stuff

• Quantization: Several new features in range-based linear quantization:
• More control over post-training quantization configuration: Additional command line arguments in image classification sample.
• We’ve fixed a couple of Early Exit bugs, and improved the documentation
• Support for quantization-aware training in the loop
• Training with knowledge distillation, in conjunction with the other available pruning / regularization / quantization methods.
• A set of Jupyter notebooks to plan experiments and analyze compression results. The graphs and visualizations you see on this page originate from the included Jupyter notebooks.
• Sample implementations of published research papers, using library-provided building blocks. See the research papers discussions in our model-zoo.
• • Frequently-asked questions (FAQ)
  • Model zoo
  • Compression scheduling
  • Usage
  • Preparing a model for quantization
  • Tutorial: Using Distiller to prune a PyTorch language model
  • Tutorial: Pruning Filters & Channels
  • Tutorial: Post-Training Quantization of a Language Model
  • Post-training quantization command line examples
• The set of notebooks that come with Distiller is described here, which also explains the steps to install the Jupyter notebook server.
  
• The authors of the research papers implemented in the Distiller model-zoo have shared their research ideas, theoretical background and results.

MorvanZhou / PyTorch-Tutorial

Build your neural network easy and fast
Good Stuff

• Visit 莫烦 Python for more.
• Thanks for liufuyang’s notebook files which is a great contribution to this tutorial.
  • pyTorch basic
    • torch and numpy
    • Variable
    • Activation
  • Build your first network
    • Regression
    • Classification
    • An easy way
    • Save and reload
    • Train on batch
    • Optimizers
  • Advanced neural network
    • CNN
    • RNN-Classification
    • RNN-Regression
    • AutoEncoder
    • DQN Reinforcement Learning
    • A3C Reinforcement Learning
    • GAN (Generative Adversarial Nets) / Conditional GAN
  • Others (WIP)
    • Why torch dynamic
    • Train on GPU
    • Dropout
    • Batch Normalization

lse-me314 / lse-me314.github.io

ME314 2019 Course Website
Good Stuff

• Detailed instructions can also be found here for installing the tools you need and working with the lab materials.**
• See https://lse-me314.github.io/instructions for detailed instructions on obtaining and working with each day’s lab materials.
• The class assignment for Day 5 will count as the mid-term assignment, which will count for 25% of the grade. The mid-term is available here.
• Instructions: The exam is here. Complete and submit the exam just as you would any lab assignment: by renaming the file, editing the R Markdown, knitting, and submitting through Moodle your knitted HTML file.
• Lab 1 materials
• Lab 1 solution as RMarkdown
• Lecture Notes
• Lab 2 materials
• Lab 2 solution as RMarkdown or as HTML.
• Lecture Notes
• Lab 3 materials
• Lab 3 solution as RMarkdown or as HTML.
• Lab 4 materials
• Lab 4 solution as RMarkdown or as HTML.
• Midterm/Lab 5 materials. This is also the mid-term exam.] To submit this, you should upload your single HTML file to the Moodle page by Wednesday 7 August 19:00.
• Lab 6 materials
• Lab 7 materials
• Lab 8 materials
• Lab 09 materials
• Lab 10 materials
• Lab 11 materials

Cyb3rWard0g / HELK

The Hunting ELK
Good Stuff

• Welcome to HELK! : Enabling Advanced Analytics Capabilities
• Spark
• Spark Standalone Mode
• Setting up a Pentesting.. I mean, a Threat Hunting Lab - Part 5
• An Integrated API for Mixing Graph and Relational Queries
• Graph queries in Spark SQL
• Graphframes Overview
• Elastic Producs
• Elastic Subscriptions
• Elasticsearch Guide
• spujadas elk-docker
• deviantony docker-elk

HazyResearch / snorkel

A system for quickly generating training data with weak supervision
Good Stuff

• As part of this refactor, we will be bringing under one roof a number of projects in the Snorkel ecosystem that have previously been posted in separate repositories—Snorkel, Snorkel MeTaL, TANDA, etc.—and which have been used to achieve state-of-the-art results on the GLUE and SuperGLUE benchmarks, automate cardiac MRI classification and genetic research database curation (as featured in two forthcoming Nature papers), and extract information from electronic health record (EHR) data for national medical device surveillance.
• We see Snorkel as providing a general framework for many weak supervision techniques, and as defining a new programming model for weakly-supervised machine learning systems.
• The Role of Massively Multi-Task and Weak Supervision in Software 2.0 (CIDR 2019)
• Snorkel: Fast Training Set Generation for Information Extraction (SIGMOD DEMO 2017)

MVIG-SJTU / AlphaPose

Real-Time and Accurate Multi-Person Pose Estimation&Tracking System

Pose Estimation

Pose Tracking

CrowdPose

Good Stuff

• AlphaPose is based on RMPE(ICCV’17), authored by Hao-shu Fang, Shuqin Xie, Yu-Wing Tai and Cewu Lu, Cewu Lu is the corresponding author. Currently, it is developed and maintained by Hao-shu Fang, Jiefeng Li, Yuliang Xiu and Ruiheng Chang.

python

tkipf / gcn

Implementation of Graph Convolutional Networks in TensorFlow
Good Stuff

• Thomas Kipf, Graph Convolutional Networks (2016)

Helpsypoo / primer

code that makes videos for this: youtube.com/c/primerlearning
Good Stuff
Requirements

Blender

• Download
• API

Script Runner addon for Blender

• Download

TeX

dvisvgm

• https://dvisvgm.de/

OpenBabel if you want to do the molecule stuff http://openbabel.org/wiki/Main_Page

• Chem stuff adapted from here: https://github.com/patrickfuller/blender-chemicals
• Also helpful: https://patrickfuller.github.io/molecules-from-smiles-molfiles-in-blender/

swift

socketio / socket.io-client-swift

Good Stuff

• Checkout the FAQs for commonly asked questions.
• Checkout the 12to13 guide for migrating to v13+ from v12 below.

• Docs

• Client
• Manager
• Engine
• Options

awslabs / aws-sdk-ios-samples

This repository has samples that demonstrate various aspects of the AWS SDK for iOS, you can get the SDK source on Github https://github.com/aws-amplify/aws-sdk-ios/
Good Stuff

• Developer Guide for AWS SDK for iOS

javascript

svaarala / duktape

Duktape - embeddable Javascript engine with a focus on portability and compact footprint
Good Stuff

• kangax/compat-table

select2 / select2

Select2 is a jQuery based replacement for select boxes. It supports searching, remote data sets, and infinite scrolling of results.
Good Stuff

• [flat-ui]: http://designmodo.github.io/Flat-UI/

facebook / jest

Delightful JavaScript Testing.
Good Stuff

• Jest can be used in projects that use webpack to manage assets, styles, and compilation. webpack does offer some unique challenges over other tools. Refer to the webpack guide to get started.

uber / react-map-gl

React friendly API wrapper around MapboxGL JS
Good Stuff

• • In addition to exposing MapboxGL functionality to React apps, react-map-gl also integrates seamlessly with deck.gl.

go

sqshq / sampler

A tool for shell commands execution, visualization and alerting. Configured with a simple YAML file.
Release
Download .exe

graph-gophers / graphql-go

GraphQL server with a focus on ease of use
Good Stuff

• The goal of this project is to provide full support of the GraphQL draft specification with a set of idiomatic, easy to use Go packages.