by ODSC Open Data Science Conference | Released July 2019 | ISBN: 0136526470

Royalties for this video set help fund ODSC community initiatives such as grants to open source projects, our diversity program, student travel grants, and other initiatives.The 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 intelligence Presentations 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'll 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: OS for AI: How Serverless Computing Enables the Next Gen of ML—Jon Peckpomegranate: Fast and Flexible Probabilistic Modeling in Python—Jacob SchreiberData Wrangling to Provide Solar Energy Access Across Africa—Brianna Schuyler, PhDThe past, present, and future of Automated Machine Learning—Randy Olson, PhDMinimizing and Preventing Bias in AI—Frances HaugenDeep Learning on Mobile—Anirudh KoulState of the Art Natural Language Understanding at Scale—David Talby, PhDLatest Developments in GANS—Seth WeidmanHow to Reason About Stateful Streaming Machine Learning Serving—Lessons from Production—Patrick BoueriAn Introduction to Active Learning—Jennifer Prendki, PhDHow to use Satellite Imagery to be a Machine Learning Mantis Shrimp—Sean Patrick Gorman, PhDCore 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: Panel: Visual Search: The Next Frontier of Search—Clayton MellinaVisualizing Vectors: Basics Every Data Scientist Should Know—Jed CrosbyRevolutionizing Visual Commerce—Robinson PiramuthuScaling Interactive Data Science and AI with Ray—Richard LiawThe Platform and Process of Agile Data Science—Sarah Aerni, PhDThe AI Engineer: A Foot in Two Worlds—Guy RoyseData 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: Managing Effective Data Science Teams—Conor JensenA Manager's Guide to Starting a Computer Vision Program—Ali Vanderveld, PhDWord Play: Understanding the Mechanics and Business Value of Speech Technologies—Omar TawakolJust How Much Data Is Required to Make Autonomous Vehicles Truly Road-Ready?—Alexandr WangHow to Democratize Artificial Intelligence in Your Business—Olivier BlaisReality Check: Beyond the Hype. Real Companies Doing Real Business Getting Real Value with AI—Alyssa RochwergerAn Ethical Foundation for the AI-driven Future—Harry GlasserBest Practices for Deploying Machine Learning in the Enterprise—Robbie AllenGreatest hurdles in AI proliferation in Education—Varun Arora10 Things I Learned Deploying AI into Human Environments—Cameron TurnerThought Leadership | KeynotesData 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: Data Science and Open-Source Education for the Enterprise—Zachary Sean BrownTurning Machine Learning Research into Products for Industry—Reza Bosagh ZadehAI—Disruption for the Marketing World—Luc DumontPlease see our table of contents for a full list of videos. Show and hide more

1. Deep Learning and Machine Learning
   • An Introduction to Active Learning—by Jennifer Prendki, PhD 00:46:37
   • Applying Deep Learning to Article Embedding for Fake News Evaluation—by Amit Gupta 00:50:06
   • Collaborative Data science and How to Build a Data science Toolchain Around Notebook Technologies—by Moon soo Lei 00:39:03
   • Continuous Experiment Framework at Uber—by Jeremy Gu 00:51:45
   • CuPy: A NumPy-compatible Library for GPU—by Crissman Loomis 1:11:35
   • Data Wrangling to Provide Solar Energy Access Across Africa—by Brianna Schuyler, PhD 00:52:52
   • Deep Learning for Speech Recognition—by Pranjal Daga 00:40:25
   • Deep learning is not always the best solution: Illustrative examples from educational products—by Josine Verhagen, PhD 00:42:14
   • Deep Learning on Mobile—by Anirudh Koul 1:20:04
   • Dynamic Pricing for Parking—by Maokai Lin 00:27:14
   • Exploring the Deep Learning Framework: PyTorch—by Stephanie Kim 00:39:43
   • Guided Analytics for Machine Learning Automation with KNIME—by Iris Adä 1:21:44
   • How to Reason About Stateful Streaming Machine Learning Serving—Lessons from Production—by Patrick Boueri 00:40:33
   • How to use Satellite Imagery to be a Machine Learning Mantis Shrimp—by Sean Patrick Gorman, PhD 1:29:30
   • Image Recognition Primer: ImageNet AlexNet to Mask R-CNN, R-CNN and Fast R-CNN—by Bhairav Mehta 1:50:14
   • Improving Customer Support through Deep Learning and NLU—by Sami Ghoche 00:42:13
   • Introduction to Technical Financial Evaluation with R—by Ted Kwartler 1:26:56
   • Latest Developments in GANS—by Seth Weidman 1:05:49
   • Law & Disorder: Mathematical Models in a Messy World—by Benjamin Pedrick 00:26:24
   • Machine Learning Algorithms for the Early Detection of Behavioral Health Disorders in Children—by Stuart Liu-Mayo 00:57:05
   • MacroBase: Prioritizing Human Attention in Big Data—by Firas Abuzaid 00:30:03
   • Mastering A/B Testing: From Design to Analysis—by Guillaume Saint-Jacques 1:24:09
   • Mathematical Approaches to Clustering—by Joseph Ross, PhD 00:49:32
   • Minimizing and Preventing Bias in AI—by Frances Haugen 00:43:26
   • ML Operationalization: From What? & Why? to How? & Who?—by Sivan Metzger 00:30:17
   • Model Evaluation in the Land of Deep Learning—by Pramit Choudhary 1:55:13
   • pomegranate: Fast and Flexible Probabilistic Modeling in Python—by Jacob Schreiber 1:13:17
   • Predicting Alzheimer’s: Generating Neural Networks to Detect the Neurodegenerative Disease—by Ayin Vala 00:30:22
   • Raise your own Pandas Cub—by Ted Petrou 1:25:52
   • State of the Art Natural Language Understanding at Scale—by David Talby, PhD 00:49:56
   • The History and Future of Machine Learning at Reddit—by Anand Mariappan 00:36:28
   • The past, present, and future of Automated Machine Learning—by Randy Olson, PhD 00:47:55
   • Tuning the Un-tunable: Lessons for tuning expensive deep learning functions—by Patrick Hayes 00:35:23
   • Unpredictable Predictions of Self-Driving Cars AI—Handling Inference in Anomalous Environment.—by Stepan Pushkarev 00:42:23
2. Core Data Science and Data Visualization
   • How Data Fueled the Birth of Computer Vision—by Michael Gormish 00:40:47
   • Revolutionizing Visual Commerce—by Robinson Piramuthu 00:48:50
   • The AI Engineer: A Foot in Two Worlds—by Guy Royse 00:43:02
   • The Platform and Process of Agile Data Science—by Sarah Aerni, PhD 00:47:59
   • Using Data Science for Good—by David Smith 2:01:56
   • Visualizing Vectors: Basics Every Data Scientist Should Know—by Jed Crosby 00:45:54
3. Data Science, Management, And Business
   • 10 Things I Learned Deploying AI into Human Environments—by Cameron Turner 00:21:20
   • A Manager’s Guide to Starting a Computer Vision Program—by Ali Vanderveld, PhD 00:43:32
   • A Practical Example of Taking Data Science, Machine Learning function from 0 to 10 in your Enterprise.—by Madhura Dudhgaonkar 00:36:48
   • Accelerate AI—AI Gold Rush: Conundrum for Startups—by Divya Jain 00:34:33
   • Agile Experimentation—from ideas to deployment—by John Haller 00:22:31
   • An Ethical Foundation for the AI-driven Future—by Harry Glasser 00:29:20
   • Best Practices for Deploying Machine Learning in the Enterprise—by Robbie Allen 00:27:54
   • Greatest hurdles in AI proliferation in Education—by Varun Arora 00:32:10
   • How to Democratize Artificial Intelligence in Your Business—by Olivier Blais 00:27:38
   • Just How Much Data Is Required to Make Autonomous Vehicles Truly Road-Ready?—by Alexandr Wang 00:27:07
   • Leveraging AI for product and company growth—by Jeremy Karnowski 00:22:16
   • Making Data Great Again—by Julia Lane, PhD 1:07:35
   • Managing Effective Data Science Teams—by Conor Jensen 00:59:35
   • Most Data-Driven Cultures… Aren’t—by Cassie Kozyrkov, PhD 00:47:57
   • Practical Data Science—by Michael Galvin 00:27:18
   • Reality Check: Beyond the Hype. Real Companies Doing Real Business Getting Real Value with AI—by Alyssa Rochwerger 00:29:55
   • Role and placement of data science in the organization—by Eric Colson 00:28:10
   • Why effective and Ethical AI needs human-centered design—by James Guszcza, PhD 00:31:14
4. Thought Leadership | Keynotes
   • AI—Disruption for the Marketing World—by Luc Dumont 00:33:29
   • Data Science and Open-Source Education for the Enterprise—by Zachary Sean Brown 00:27:20
   • Turning Machine Learning Research into Products for Industry—by Reza Bosagh Zadeh 00:34:33
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