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