Full Stack Deep Learning
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Full Stack Deep Learning
Course Content
Setting up Machine Learning Projects
Infrastructure and Tooling
Data Management
Overview
Sources
Labeling
Storage
Versioning
Processing
Machine Learning Teams
Training and Debugging
Testing and Deployment
Research Areas
Labs
Where to go next
Guest Lectures
Xavier Amatriain (Curai)
Chip Huyen (Snorkel)
Lukas Biewald (Weights & Biases)
Jeremy Howard (Fast.ai)
Richard Socher (Salesforce)
Raquel Urtasun (Uber ATG)
Yangqing Jia (Alibaba)
Andrej Karpathy (Tesla)
Jai Ranganathan (KeepTruckin)
Franziska Bell (Toyota Research)
Corporate Training and Certification
Corporate Training
Certification
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Overview
Why is data management important?
Overview - Data Management
Summary
Data science has never been as much about machine learning as it has about cleaning, shaping, and moving data from place to place.
Here are the important concepts in data management:
Sources -
how to get training data
Labeling -
how to label proprietary data at scale
Storage -
how to store data and metadata appropriately
Versioning -
how to update data through user activity or additional labeling
Processing -
how to aggregate and convert raw data and metadata
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Summary