Interesting Stuff

Bookmarks

http://www.webpagetest.org/  checks speed of a web page from different locations, browsers, with auth, protocols ...

http://scienceandpublic.com/  DeJargonizer

https://github.com/vinta/awesome-python  A curated list of awesome Python frameworks, libraries, software and resources.

https://distill.pub/2016/misread-tsne/  - A nice _interactive_ overview of the effect of hyperparameters for t-SNE

https://nlp.stanford.edu/projects/glove/  Global Vectors for Word Representation

https://hackernoon.com/a-documentation-crash-course-45006a85c15c

http://timdettmers.com/2018/08/21/which-gpu-for-deep-learning/

https://gist.github.com/sloria/7001839  - The Best of the Best Practices (BOBP) Guide for Python
https://www.vinaysahni.com/best-practices-for-building-a-microservice-architecture?fbclid=IwAR1LEKYyJ6p1N2v8sf7HpxkCjgj_MQaDL6t7OizR4FWGma-hzWeFSQfHjJg#platform
https://cloudncode.blog/2017/03/02/best-practices-aws-lambda-function/?fbclid=IwAR2t2c23c1VM21GNPIh0yHHGRhV9LoWYm0QOEofZ-youUYtDRLdR_UOj5Vs

https://pudding.cool/2018/10/city_3d/?fbclid=IwAR3YX_t3CyRYCMKhuqXcQ4Xxy-eg1gGJpmsK8AA8_GpPfzmfcTwWmaMk2bw  
  - Human Terrain - Visualizing World Population in 3D
  
https://medium.com/predict/five-interview-questions-to-predict-a-good-data-scientist-40d310cdcd68

https://medium.com/kudos-engineering/faking-fires-get-better-incident-management-with-practise-e61a5d66578d

https://www.slideshare.net/farizbashirov  - 8 do's and don't of chatbots

--> moved to orgmode Bookmarks at 2019-03-16

Datasets

# AudioSet
Data: https://research.google.com/audioset/download.html
Paper: https://static.googleusercontent.com/media/research.google.com/de//pubs/archive/45857.pdf

Impressive Articles

# General
https://blog.ycombinator.com/ycs-essential-startup-advice/

https://medium.com/swlh/how-we-got-to-zero-bugs-and-implemented-a-zero-bug-policy-c77ee3f2e50b

https://hackernoon.com/work-small-even-if-it-makes-no-sense-6bd1f401fc3a. Very small Sprints Plädoyer

https://medium.com/@johnpcutler/our-intuition-says-instead-try-7808c0bfd0bb

https://thebetterstory.co/everything-i-wish-id-known-before-i-started-demoing-saas-f83c1c4fad99

https://medium.freecodecamp.org/ten-rules-for-negotiating-a-job-offer-ee17cccbdab6

https://towardsdatascience.com/the-4-fastest-ways-not-to-get-hired-as-a-data-scientist-565b42bd011e

https://medium.com/s/story/imaginary-problems-d4f2921bd1b8

https://zwischenzugs.com/2018/10/02/why-are-enterprises-so-slow/

https://hackernoon.com/write-good-documentation-6caffb9082b4
https://codeascraft.com/2018/10/10/etsys-experiment-with-immutable-documentation/

https://www.trek10.com/blog/business-case-for-serverless/

# Data Science and ML

https://medium.freecodecamp.org/aspiring-data-scientist-master-these-fundamentals-be7c54350868
https://towardsdatascience.com/how-to-visualize-a-decision-tree-from-a-random-forest-in-python-using-scikit-learn-38ad2d75f21c

## DataScience Tools

https://www.edureka.co/blog/python-matplotlib-tutorial/

https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/
https://arogozhnikov.github.io/2015/09/29/NumpyTipsAndTricks1.html
https://arogozhnikov.github.io/2015/09/30/NumpyTipsAndTricks2.html

https://docs.google.com/presentation/d/1n2RlMdmv1p25Xy5thJUhkKGvjtV-dkAIsUXP-AL4ffI  - I Don't Like Notebooks - Joel Grus - #JupyterCon 2018
https://docs.google.com/presentation/d/1ivK8AKgz8Hx-ZYzPC9gJyQK6tzuhR3UuhCEajFGJDlA  - If Not Notebooks, Then What? - AAAI 2019

https://www.blog.pythonlibrary.org/2018/09/25/creating-presentations-with-jupyter-notebook/

## Visualizations

http://glengilchrist.co.uk/post/the-3d-challenge-can-you-read-this-chart

https://medium.com/intuitionmachine/our-minds-see-and-hear-only-what-we-imagine-dc303056171

## ML
https://towardsdatascience.com/the-10-deep-learning-methods-ai-practitioners-need-to-apply-885259f402c1

https://mlwave.com/kaggle-ensembling-guide/

https://bair.berkeley.edu/blog/2018/08/06/recurrent/  - When Recurrent Models Don't Need to be Recurrent

https://nbviewer.jupyter.org/github/roamanalytics/roamresearch/blob/master/BlogPosts/Modern_TensorFlow/modern-tensorflow.ipynb
  - Good practices in Modern Tensorflow for NLP

## Python
https://hackernoon.com/10-common-security-gotchas-in-python-and-how-to-avoid-them-e19fbe265e03

https://medium.com/@ali_oguzhan/how-to-use-scrapy-with-django-application-c16fabd0e62e

## DevOps

https://presumably.de/monorepos-and-the-fallacy-of-scale.html

https://medium.com/devopslinks/building-chatbot-with-google-dialogflow-with-aws-lambda-e19872e1589

https://queue.acm.org/detail.cfm?id=3300018  - SQL is No Excuse to Avoid DevOps

Impressive Papers

https://arxiv.org/pdf/1712.09913.pdf  - Visualizing the loss landscape of Neural Nets
... shortcuts (like in residual nets) changes chaotic, untrainable surfaces into highly convex, smooth surfaces
... descent path is very low dimensional: between 40% and 90% of the variation in the descent paths lies in a space of only 2 dimensions

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.442.5724&rep=rep1&type=pdf  - libfm Paper

https://arxiv.org/pdf/1312.6114.pdf  - Auto-Encoding Variational Bayes

https://arxiv.org/pdf/1803.01271.pdf  - An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Impressive Videos

https://www.youtube.com/watch?v=NKpuX_yzdYs  - State of Artificial Intelligence by Andrew Ng

https://www.youtube.com/watch?v=T3octNTE7Is  - Lecture 16: Dynamic Neural Networks for Question Answering

https://www.youtube.com/watch?v=o64FV-ez6Gw  - Joel Grus - Livecoding Madness - Let's Build a Deep Learning Library (Solves XOR and FizzBuzz in the end, uses mypy typing)

https://www.youtube.com/watch?v=t4vKPhjcMZg  - What nobody tells you about documentation

https://www.youtube.com/watch?v=DtRy79jIsS8  - Deprecating Simplicity (Chaos Engineering)

Open Source Projects

https://github.com/hanfried/tmux-battery/tree/assume_no_battery_percentage_as_ok
  Fork from https://github.com/tmux-plugins/tmux-battery

https://github.com/hanfried/gitsome/tree/configurable_default_prompt
  Fork from https://github.com/donnemartin/gitsome with black instead white text,
  PR: configurable_prompt outstanding

Python modules

https://github.com/pypa/pipenv  - now official recommended tool from python.org that handles pip and virtualenv

https://plot.ly/products/dash/  - Dash is a Python framework for building analytical web applications. No JavaScript required.
... Built on top of Plotly.js, React, and Flask, Dash ties modern UI elements like dropdowns, sliders, and graphs to your analytical Python code.

https://github.com/RJT1990/pyflux  - working with time series

https://github.com/scikit-learn-contrib/imbalanced-learn  - module to perform under sampling and over sampling with various techniques

https://github.com/vi3k6i5/flashtext  - replace keywords in sentences or extract keywords from sentences
... in constant and quick time for many replacements instead of O(#regexps solution)

https://github.com/google/python-fire  - automatically generate a CLI for any Python project

https://github.com/sdispater/pendulum  - drop-in replacement for datetime, even more convinient than Arrow, can parse dirty dates still correctly

ti;lr

https://medium.com/s/story/how-i-fully-quit-google-and-you-can-too-4c2f3f85793a

https://checklyhq.com/blog/2018/08/an-in-depth-look-at-100-zero-downtime-deployments-with-terraform/

Worth to look again

https://github.com/tryolabs/luminoth  - toolkit for computer vision, built using TensorFlow and Sonnet
... Currently, it out-of-the-box supports object detection in the form of a model called Faster R-CNN.

https://github.com/tryolabs/requestium  - merges the power of Requests, Selenium, and Parsel into a single integrated tool for automatizing web actions.

http://www.kdnuggets.com/2017/09/essential-data-science-machine-learning-deep-learning-cheat-sheets.html

https://github.com/Miserlou/Zappa  Zappa - Serverless Python

https://www.quantamagazine.org/new-theory-cracks-open-the-black-box-of-deep-learning-20170921/

https://blaze.readthedocs.io/en/latest/index.html

https://arxiv.org/abs/1406.2661  - GAN paper from Goodfellow

http://blog.paralleldots.com/technology/deep-learning/effective-learning-near-future-ai/

https://arxiv.org/pdf/1412.6980.pdf  - Adam Optimization Paper

https://arxiv.org/abs/1506.02640 and https://arxiv.org/abs/1612.08242  - YOLO papers
https://pjreddie.com/darknet/yolo/  - YOLO official website

https://www.linkedin.com/pulse/how-implement-object-detection-yolo-v2-yan-pang/

https://arxiv.org/pdf/1406.4729.pdf  - Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

https://arxiv.org/pdf/1712.09662.pdf  - CNN Is All You Need 

https://arxiv.org/pdf/1505.04597.pdf  - Unet Convolutional Networks for Biomedical Image Segmentation

https://arxiv.org/abs/1701.05369  - Variational Dropout Sparsifies Deep Neural Networks
https://arxiv.org/abs/1505.05770  - Variational Inference with Normalizing Flows
https://arxiv.org/abs/1702.04008  - Soft Weight-Sharing for Neural Network Compression

https://arxiv.org/pdf/1804.02559v1.pdf  - Guiding Neural Machine Translation with Retrieved Translation Pieces

http://web.stanford.edu/class/cs224n/reports.html  - Stanford NLP Course Projects

https://lvdmaaten.github.io/publications/papers/JMLR_2008.pdf  - Visualizing Data using t-SNE
https://arxiv.org/pdf/1703.07737.pdf  - In Defense of the Triplet Loss for Person Re-Identification

https://towardsdatascience.com/data-science-for-startups-model-production-b14a29b2f920