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Presenter: Michael Conover, LinkedIn Interesting, impactful problems are rarely easy to define, model, or evaluate - in fact, the very newness of a problem often suggests a scarcity of data and structured thinking about viable approaches. In this talk, we'll work through some of the challenges faced by LinkedIn's machine learning research teams in building and shipping intelligent systems that make sense of the world's economy. From creating novel training data and productionizing models with complex structure to evangelizing and evaluating the results of unsupervised algorithms, this talk will examine real-world case studies describing some of the hardest and most interesting challenges faced by one of the world's largest technology companies.
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This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language. The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson. A video of Dr. Anderson using these slides is available on YouTube at: http://youtu.be/SPW-wRmjoZ8
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This presentation summarizes some early efforts on an open drug discovery collaboration between scientists in Brazil and the US. The amazing virus images were created by John Liebler and can be licensed from him http://www.artofthecell.com/animation/will-the-real-zika-virus-please-stand-up The homology models were created with Swiss Model by Sean Ekins: Marco Biasini, Stefan Bienert, Andrew Waterhouse, Konstantin Arnold, Gabriel Studer, Tobias Schmidt, Florian Kiefer, Tiziano Gallo Cassarino, Martino Bertoni, Lorenza Bordoli, Torsten Schwede. (2014). SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Research; (1 July 2014) 42 (W1): W252-W258; doi: 10.1093/nar/gku340. Arnold K., Bordoli L., Kopp J., and Schwede T. (2006). The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling. Bioinformatics, 22,195-201. Kiefer F, Arnold K, Künzli M, Bordoli L, Schwede T (2009). The SWISS-MODEL Repository and associated resources. Nucleic Acids Research. 37, D387-D392. Guex, N., Peitsch, M.C., Schwede, T. (2009). Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: A historical perspective. Electrophoresis, 30(S1), S162-S173.
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This talk details patterns and processes at LinkedIn for building real-time machine learning systems to provide our members with actionable, relevant opportunities to nurture their professional networks. Featuring the Connected mobile app as an in-depth case study of how to combine compute-intensive features describing billions of relationships with information that isn’t known until the moment a user opens the app, in this talk we’ll discuss the architectural, modeling, and experimentation patterns leveraged by the Connected relevance team to rank and serve mobile content. Additionally, this session will touch on the human element of machine learning product development, outlining collaboration and communication patterns for working effectively across the organization – from reporting and documentation to evangelism, skills transfer and user experience research. Taken together, these insights will provide a detailed picture of some of LinkedIn’s best practices for building data products at a global scale.
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This presentation summarizes some early efforts on an open drug discovery collaboration between scientists in Brazil and the US. The amazing virus images were created by John Liebler and can be licensed from him http://www.artofthecell.com/animation/will-the-real-zika-virus-please-stand-up The homology models were created with Swiss Model by Sean Ekins: Marco Biasini, Stefan Bienert, Andrew Waterhouse, Konstantin Arnold, Gabriel Studer, Tobias Schmidt, Florian Kiefer, Tiziano Gallo Cassarino, Martino Bertoni, Lorenza Bordoli, Torsten Schwede. (2014). SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Research; (1 July 2014) 42 (W1): W252-W258; doi: 10.1093/nar/gku340. Arnold K., Bordoli L., Kopp J., and Schwede T. (2006). The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling. Bioinformatics, 22,195-201. Kiefer F, Arnold K, Künzli M, Bordoli L, Schwede T (2009). The SWISS-MODEL Repository and associated resources. Nucleic Acids Research. 37, D387-D392. Guex, N., Peitsch, M.C., Schwede, T. (2009). Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: A historical perspective. Electrophoresis, 30(S1), S162-S173.
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From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world. With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively. The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage. Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience. See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers See the original article on Forbes here: http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
5 Public speaking tips from TED - Visualized summary
5 Public speaking tips from TED - Visualized summary
SpeakerHub
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world. Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance. For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age. Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
ChatGPT and the Future of Work - Clark Boyd
ChatGPT and the Future of Work - Clark Boyd
Clark Boyd
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The six step guide to practical project management If you think managing projects is too difficult, think again. We’ve stripped back project management processes to the basics – to make it quicker and easier, without sacrificing the vital ingredients for success. “If you’re looking for some real-world guidance, then The Six Step Guide to Practical Project Management will help.” Dr Andrew Makar, Tactical Project Management
The six step guide to practical project management
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MindGenius
A presentation for absolute beginners who have never touched TikTok and may be a bit scared of it!
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Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
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Trends In Paid Search: Navigating The Digital Landscape In 2024
Trends In Paid Search: Navigating The Digital Landscape In 2024
5 Public speaking tips from TED - Visualized summary
5 Public speaking tips from TED - Visualized summary
ChatGPT and the Future of Work - Clark Boyd
ChatGPT and the Future of Work - Clark Boyd
Getting into the tech field. what next
Getting into the tech field. what next
Google's Just Not That Into You: Understanding Core Updates & Search Intent
Google's Just Not That Into You: Understanding Core Updates & Search Intent
How to have difficult conversations
How to have difficult conversations
Introduction to Data Science
Introduction to Data Science
Time Management & Productivity - Best Practices
Time Management & Productivity - Best Practices
The six step guide to practical project management
The six step guide to practical project management
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Cloudera Wrangle 2015
1.
Condense Fact from
the Vapor of Nuance Mike Conover, Ph.D. Staff Data Scientist, LinkedIn
2.
Good Problems
3.
Unambiguous Concrete Response Variables Ad
Clicks Real Estate Pricing Flower Species
4.
Qualitative Multidimensional Non-Euclidean Expertise Nebulous TrustSerendipity Sentiment
5.
High-Fidelity Proxy Variables Crowdsourcing
/ In-House Evaluation Tip of the Spear Operationalize “All models are wrong, but some are useful.” George Box
6.
Execution
7.
Quality Control
8.
Scale is the
Premise
9.
Spectrum
10.
Qualitative Evaluation Heteroscedasticity Stratified Sampling Temporal Factors
11.
Confirmation Bias
12.
Fit to Print
13.
Lightweight Narrative Frame Toy Examples Propaganda “Given
enough eyeballs, all bugs are shallow.” Eric S. Raymond
14.
Infrastructure Notebooks Model Viewers Soup to
Nuts
15.
Ship It!
16.
Devil’s Bargain Feature Transformations Transcription
Errors Model Specification
17.
Power Tools Singularity Something’s Wrong.
Go. Spark & MLLib + Notebooks
18.
Condense Fact from
the Vapor of Nuance Mike Conover, Ph.D. Staff Data Scientist, LinkedIn
19.
Biomorphs Iterating on Response
Variables
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