Awesome List Updates on Nov 04, 2017
12 awesome lists updated today.
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1. Papers We Love
Info / Other Good Places to Find Papers
2. Awesome Steam
Node.js / WebAPI
- steamapi (⭐126) - A nice Steam API wrapper.
3. Awesome Incident Response
IR Tools Collection / Disk Image Creation Tools
- Bitscout (⭐413) - Bitscout by Vitaly Kamluk helps you build your fully-trusted customizable LiveCD/LiveUSB image to be used for remote digital forensics (or perhaps any other task of your choice). It is meant to be transparent and monitorable by the owner of the system, forensically sound, customizable and compact.
4. Awesome Speakers
Europe / Germany 🇩🇪
Topics: Tooling, Standards, webpack
General / UX
Sponsored dinners in San Francisco where the women coming from all over are invited to share their experiences, alongside prominent speakers from the female tech communities. The events also offer networking and potential recruiting by the sponsoring company. It has been hosted by 100+ companies and has several events per month.
6. Awesome Sre
- rachelbythebay - Techincal Blog Posts.
7. Awesome Electron
For Electron / Other
- electron-util (⭐1.2k) - Useful utilities for developing apps and modules.
8. Awesome Swift
Utility / Barcode
- WhatsNew (⭐1.5k) - Showcase new features after an app update similar to Pages, Numbers and Keynote.
9. Awesome Nextjs
10. Awesome Book Authoring
11. Awesome Influxdb
Projects / Dedicated
- aprs2influxdb (⭐26) - Interfaces ham radio APRS-IS servers and saves packet data into an influxdb database
12. Awesome H2o
- Machine Learning Using R Karthik Ramasubramanian, Abhishek Singh. (2016)
- Disruptive Analytics Thomas Dinsmore. (2016)
- Computer Age Statistical Inference: Algorithms, Evidence, and Data Science Bradley Efron, Trevor Hastie. (2016)
- Spark in Action Petar Zečević, Marko Bonaći. (2016)
- Algorithmic trading using deep neural networks on high frequency data Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón. (2017)
- Generic online animal activity recognition on collar tags Jacob W. Kamminga, Helena C. Bisby, Duc V. Le, Nirvana Meratnia, Paul J. M. Havinga. (2017)
- Robust and flexible estimation of data-dependent stochastic mediation effects: a proposed method and example in a randomized trial setting Kara E. Rudolph, Oleg Sofrygin, Wenjing Zheng, and Mark J. van der Laan. (2017)
- Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, Dan Cervone. (2017)
- Using deep learning to predict the mortality of leukemia patients Reena Shaw Muthalaly. (2017)
- Use of a machine learning framework to predict substance use disorder treatment success Laura Acion, Diana Kelmansky, Mark van der Laan, Ethan Sahker, DeShauna Jones, Stephan Arnd. (2017)
- Ultra-wideband antenna-induced error prediction using deep learning on channel response data Janis Tiemann, Johannes Pillmann, Christian Wietfeld. (2017)
- Inferring passenger types from commuter eigentravel matrices Erika Fille T. Legara, Christopher P. Monterola. (2017)
- Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 Christopher Krauss, Xuan Anh Doa, Nicolas Huckb. (2016)
- Identifying IT purchases anomalies in the Brazilian government procurement system using deep learning Silvio L. Domingos, Rommel N. Carvalho, Ricardo S. Carvalho, Guilherme N. Ramos. (2016)
- Predicting recovery of credit operations on a Brazilian bank Rogério G. Lopes, Rommel N. Carvalho, Marcelo Ladeira, Ricardo S. Carvalho. (2016)
- Deep learning anomaly detection as support fraud investigation in Brazilian exports and anti-money laundering Ebberth L. Paula, Marcelo Ladeira, Rommel N. Carvalho, Thiago Marzagão. (2016)
- The value of points of interest information in predicting cost-effective charging infrastructure locations Stéphanie Florence Visser. (2016)
- Adaptive modelling of spatial diversification of soil classification units. Journal of Water and Land Development Krzysztof Urbański, Stanisław Gruszczyńsk. (2016)
- Scalable ensemble learning and computationally efficient variance estimation Erin LeDell. (2015)
- Understanding random forests: from theory to practice (⭐498) Gilles Louppe. (2014)