Oreilly - ODSC East 2018 (Open Data Science Conference)

Oreilly - ODSC East 2018 (Open Data Science Conference)

by Open Data Science Conference | Released September 2018 | ISBN: 0135432790

https://www.oreilly.com/library/view/odsc-east-2018/9780135432792/

ODSCThe Open Data Science Conference has established itself as the leading conference in the field of applied data science. Each ODSC event offers a unique opportunity to learn directly from the core contributors, experts, academics and renowned instructors helping shape the field of data science and artificial intelligencePresentations cover not only data science modeling but also the languages and tools needed to deploy these models in the real world such as TensorFlow, MXNet, scikit-learn, Kubernetes, and many more.Our conferences are organized around focus areas to ensure our attendees are at the forefront of this fast emerging field and current with the latest data science languages, tools, and models. You‚Äôll find in our East 2018 video catalog some of our most popular focus areas including:Deep Learning and Machine LearningOver the last 5 years, we have seen incredible advances in the field of data scientist thanks to breakthroughs in neural networks, transfer learning, reinforcement learning, and generative adversarial networks (GANs) to name a few. With the advent of Google Voice, Alexa, and other voice assistants, presentations on enabling technologies like NLP, RNNs, and LSTM are popular. Some session to note:Distributed TensorFlow using KubernetesDeep Learning Pipelines for Big ImagesMachine Learning and Natural Language Processing for Detecting Fake NewsLong-Term Time Series Forecasting with Recurrent Neural NetworksA Breakthrough for Natural LanguageTransfer Learning: Applications for natural language understandingChallenges and Opportunities in Applying Machine LearningEffective Transfer Learning for NLPDeveloping Machine Learning Solutions with Plugin Machine Intelligence for PDIDistributed Tensorflow: Scaling Your Model TrainingCore Data Science and Data VisualizationAs data science advances at a rapid pace, core skills are more important than ever. Our sessions range from beginner to advanced level for core topics. Additionally, data and models need to be actionable and data visualization remains a key skill in any data scientist's toolkit. Some session of note include:Bayesian Statistics Made SimpleGradient Descent, DemystifiedComparing Models Using Resampling and Bayesian MethodsNext Generation Indexes For Big Data EngineeringProbabilistic Programming with PyMC3Racial Bias in Facial Recognition SoftwareVisualization throughout the Data Science Workflow:Datafy All The ThingsData Science, Management, And BusinessData science is permeating every industry as adoption gathers pace. The management and practice of data science will become increasingly strategically important to all industries including finance and healthcare. Hear from leading experts on important topics including:Blockchain and AI: future data systems must be built differentlyMarketing in a Machine Learning WorldThe Adoption of AI in Business: Opportunities and Challenges:Winning with AI: What's working, what needs work?Building an effective AI practiceApplied Finance: The Third CultureMachine Learning Powers Better Decisioning in Financial ServicesAI and Data Science in InvestmentAI and Big data in Medicine: Trust, Transparency, and TransformationAlgorithmic Transparency and Health Care: Where do we go from here?What will you do with democratized health data?A Physician-Data Scientist Grand Vision: A Virtual Medical OracleThought Leadership | KeynotesData science and artificial intelligence are helping shape the future of business and society. Thoughtful leadership is essential and ODSC East was honored to host a few of the field's leading lights including Cathy O‚ÄôNeil, author of Weapons of Math Destruction, Drew Conway, one of the most well-known data scientists in the world, Gary Marcus, an award-winning Professor, and Kirk Borne, data scientist and executive advisor at Booz-Allen Hamilton. Thought-provoking presentations include:Catherine O'Neil | Weapons of Math Destruction: Separating Data Facts and Opinion Drew Conway | Building a Data Science Company Kirk Borne | Current and Future Trends in AI, Machine Learning, and Data Science Gary Marcus | The Social Disease Known as Hype in AI and How to Separate What AI can do and What People Wish it Would doPlease see our table of contents for a full list of videos Show and hide more

Keynotes
- The Social Disease Known as Hype in AI and How to Separate What AI can do and What People Wish it Would do—Gary Marcus 00:29:17
- Current and Future Trends in AI, Machine Learning, and Data Science—Kirk Borne 00:32:10
- Weapons of Math Distraction: Separating Data Facts and Opinion—Catherine O'Neil 00:26:52
- Building a Data Science Company—Drew Conway 00:24:49
Data Science
- Towards Identity Resolution: The Challenge of Name Matching—Gil Irizarry 00:36:42
- Uplift Modeling for Driving Incremental Revenue by Display Remarketing—Jen Wang 1:39:58
- Using Data for Good—Brandon Rohrer 00:43:52
- Collaborative Data science and How to Build a Data science Toolchain Around Notebook Technologies—Moonsoo Lee 00:39:27
- Known Unknowns: Designing uncertainty into AI-powered systems—Sean Kruzel 00:38:27
- Recommending the best hotels at TripAdvisor—Anyi Wang 00:35:06
- The Hamiltonian Monte Carlo Revolution is Open Source: Probabilistic Programming with PyMC3—Austin Rochford 00:44:57
- Comparing Models Using Resampling and Bayesian Methods—Max Kuhn, PhD 00:44:23
- Distributed Tensorflow: Scaling Your Model Training—Neil Tenenholtz 00:33:37
- Racial Bias in Facial Recognition Software—Stephanie Kim, Hillary Green-Lerman 00:33:22
- Making Sense of the Biomedical Literature via Machine Learning and Natural Language Processing—Byron Wallace 00:51:14
- Bayesian Statistics Made Simple—Allen Downey 1:09:41
Data Vizualization
- Visualization throughout the Data Science Workflow: Why it's useful, and how not to lie—Lindsay Brin, PhD 00:46:54
- Analyzing Space: Spatial Data Science...—Andy Eschbacher 00:40:16
- Datafy All The Things—Max Humber 00:32:28
Deep Learning / Machine Learning
- Enter the Matrix: Unsupervised feature learning with matrix decomposition to discover hidden knowledge in high dimensional data—Aedin Culhane, PhD 00:53:20
- Learning-to-learn: an Overview—Jennifer Prendki 00:44:54
- Next Generation Indexes For Big Data Engineering—Daniel Lemire 00:36:13
- Artificial Intelligence ‚Äì a journey to surpass the Turing test—Anisha Baidya, Michael Commons, Mansi Shah 00:37:22
- Deep Learning Pipelines for Big Images—Michael Segala 00:41:34
- Distributed TensorFlow using Kubernetes—Sertac Ozercan, Rita Zhang 00:43:29
- Effective Transfer Learning for NLP—Madison May 00:44:04
- Long Term Time Series Forecasting with Recurrent Neural Networks—Mustafa Kabul 00:49:46
- Developing Machine Learning Solutions with Plugin Machine Intelligence for PDI—Kevin Haas, Dave Huh 00:45:22
- Challenges and Opportunities in Applying Machine Learning—Alex Jaimes 00:55:34
- Deploying your AI/ML investments—Jon Peck 00:29:12
- Gradient Descent, Demystified—Michael Stewart 00:42:04
- How AI-Powered Natural Language Processing of Social Data is Fueling a New Generation of Predictive Analysis—Rob Key, Mark Garrett 00:50:04
- Machine Learning for Mobile Sensing Applications—Michael Bell, PhD 00:41:46
- Try All the Things! Liberate Users while Reducing Risks in Open Source Data Science—Jordan Volz 00:39:36
- Making the Most of Your Time Series: Signal Processing for Machine Learning applications—Keith Santarelli, Eric Schles 00:50:57
- Exploiting Multi-class Probabilities for solving Network Security Anomalies using Supervised and UnSupervised Machine Learning Approaches—Ashrith Barthur, PhD 00:52:27
- Machine Learning and Natural Language Processing for Detecting Fake News—Sihem Romdhani 00:45:35
- Privacy and Machine Learning : Peanut Butter and Jelly—Steve Touw 00:46:09
- TLDR ‚Äì automatic text summarization of documents at scale—Guilherme de Oliveira, PhD 00:43:08
- To Bid or Not To Bid: Machine Learning in Ad Tech—Justin Fortier 00:51:55
- A Breakthrough for Natural Language—Ben Vigoda 00:48:54
Data Science Management
- Data Science State of the Union, 2018—Peter Wang 00:35:38
- How to Go From Data Science to Data Operations—Gil Benghiat 00:41:47
- Making Business More Bayesian—Richard Tibbetts 00:22:33
- DataOps: Enterprise Data that Doesn't Suck—Andrew Palmer 00:35:06
Business and Management
- Blockchain and AI: future data systems must be built differently—Professor Alex Pentland, MIT 00:30:10
- Look Who's Talking: A Deep Dive into the Medium of the Future ‚Äî Natural Language Conversation—Eyal Pfeifel 00:35:16
- Marketing in a Machine Learning World—Drew Casey 00:26:34
- The Adoption of AI in Business: Opportunities and Challenges:—Sam Ransbotham 00:27:14
- Transfer Learning: Applications for natural language understanding—Dr. Catherine Havasi 00:29:46
- Using Unstructured Data and Machine Learning to Understand Loss Events—Niranjan Thomas 00:24:08
- Winning with AI: What's working, what needs work?—Manoj Saxena 00:27:55
- Building an effective AI practice—Ram Ravichandran 00:29:55
- AI and the World of Non-Profits—Rich Palmer 00:26:01
- Building a Big Data Center Of Excellence—The Secret Sauce to your Data Journey ‚Äî The People.—Amy Cloudera 00:40:18
Business and Management: Finance
- A viral model for scalable adoption of data science in a large financial organization?—Antonio Alvarez 00:30:32
- Applied Finance: The Third Culture—Steve Lawrence 00:31:17
- Mission Analytics: Common pitfalls and how to avoid them (A journey in an insurance company)—Delin Shen 00:30:49
- Extracting Embedded Alpha from Social & News Data Using Statistical Arbitrage & Machine Learning—Arun Verma 00:27:26
- Knowledge Graphs in Financial Technology—Future or Hype—Tomasz Adamusiak 00:29:31
- Machine Learning Powers Better Decisioning in Financial Services—Alexander Statnikov 00:21:15
- AI and Data Science in Investment—Kazhuri Shimbo 00:32:46
Business & Management: Healthcare
- Algorithmic Transparency and Health Care: Where do we go from here?—Norma Padr√≥n 00:29:50
- Product-Data Fit: The Lean Startup Methodology and Healthcare Data Products—Daniel Shenfeld, Afik Gal 00:27:46
- AI and Big data in Medicine: Trust, Transparency and Transformation—Lynda Chin 00:32:09
- What will you do with democratized health data?—QuHarrison Terry 00:23:32
- A Physician-Data Scientist Grand Vision: A Virtual Medical Oracle—Anthony Chang 00:26:47
- Harnessing the power of recommender system for drug off-target activity prediction—Ambrish Roy 00:22:09