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Software 2.0 and Snorkel: Beyond Hand-Labeled Data
This ODSC West 2018 talk “Software 2.0 and Snorkel: Beyond Hand-Labeled Data,” presented by Alex Ratner, a Ph.D. student in Computer Science at Stanford University, discusses a new way of effectively programming machine learning systems using what’s called “weaker supervision,” and how it enables domain experts who don’t know... Read more
Visualizing Vectors: Basics Every Data Scientist Should Know
This ODSC West 2018 talk “Visualizing Vectors: Basics Every Data Scientist Should Know,” presented by Jed Crosby, Head of Data Science at Clari, should be a required learning resource for all new data scientists. This is because every data scientist should have a firm grasp of the mathematics behind... Read more
Data Science Literacy and Open Source Education for the Enterprise
As a data scientist, one of your biggest struggles in your organization is probably communicating what you do (and what you can do) to the organization outside your data science team. If the departments around your data science team don’t understand how to frame the questions needed for the... Read more
AI Gold Rush: How to Build a Better AI Startup
We’re in the midst of an AI “gold rush.” It’s not just people working directly with AI, but the entire ecosystem that’s changing. Even fields affected by AI are innovating within the infrastructure, causing new use cases and new ways of thinking about AI. It’s an exciting time to... Read more
Using AI for Dynamic Pricing: The Smarking Example
What do the airlines, hotels, parking, and cloud computing have in common? You invest in assets upfront and render them out as slices of time. While parking assets exist in the physical space, and cloud computing exists in a virtual space, the principle is the same. Dr. Maokai Lin ... Read more
Mastering A/B Testing: From Design to Analysis
A/B testing is a critical tool leveraged by data scientists to estimate the expected outcome of a certain action like updating software, adding new features, or deploying a new web layout. Proper experimental design is crucial to realizing the benefits of A/B testing and avoiding the pitfalls that detract... Read more
Model Evaluation in the Land of Deep Learning
Applications for machine learning and deep learning have become increasingly accessible. For example, Keras provides APIs with TensorFlow backend that enable users to build neural networks without being fluent with TensorFlow. Despite the ease of building and testing models, deep learning has suffered from a lack of interpretability; deep... Read more
The Logistics of Starting Deep Learning
Deep learning models can be intimidating and rightfully so; in their raw form they are highly complex algorithms that need to be engineered with expertise. However, deep learning is very accessible to individuals with a background in technical skills thanks to organizations and individuals that have packaged deep learning... Read more
4 Examples of Businesses Solving Problems with AI
In her talk at ODSC West 2018, “Reality Check: How Businesses are using Human in the Loop Processes to Drive Real Value,” Alyssa Simpson Rochwerger, the VP of Product at Figure Eight, explains how companies can begin solving creating value and solving problems with AI. [Related Article: Problem Solving... Read more
Redefining Robotics: Next Generation Warehouses
People picture robots changing to look more like humans, but in reality, the evolution of robotics involves things you can’t actually see. For Bastiane Huang at Osaro, the development of robots means greater advances in autonomy. Building brains for robots gives them more flexibility for tasks and creates more... Read more