Full Stack Deep Learning
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Full Stack Deep Learning
Course Content
Setting up Machine Learning Projects
Infrastructure and Tooling
Data Management
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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Where to go next
Deep Learning has a strong open-source culture. Many great learning resources exist on blogs, lectures, tutorials, newsletters, course websites, and code repositories.
This section is mostly up to you!
Submit a pull request
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Conferences
ScaledML
(by Matroid)
MLOps Conference
(by Iguazio)
Spark + AI Summit
(by Databricks)
Newsletters
The Batch
(by deeplearning.ai)
Machine Learning In Production
(by Luigi Patruno)
Import AI
(by Jack Clark)
The Machine Learning Engineer Newslette
r (by The Institute for Ethical AI & ML)
Projects To Know
(by Amplify Partners)
Personal Blogs
Locally Optimistic
(Data Leaders in NYC)
MLOps Tooling Landscape
(by Chip Huyen)
Three Risks in Building Machine Learning Systems
(by Benjamin Cohen)
How To Serve Models
(by Bugra Akyildiz)
Nitpicking ML Technical Debt
(by Matthew McAteer)
Monitoring ML Models in Production
(by Christopher Samiullah)
Models for integrating data science teams within organizations
(Pardis Noorzad)
Data-as-a-Product vs Data-as-a-Service
(Justin Gage)
Corporate Blogs
The New Business of AI
(by a16z)
Long-Tailed AI Problems
(by a16z)
Rules of ML
(by Google)
Continuous delivery and automation pipelines in machine learning
(by Google)
Tecton: The Data Platform for Machine Learning
(by Tecton)
Why We Need DevOps for ML Data
(by Tecton)
Continuous Delivery for Machine Learning
(by ThoughtWorks)
Dagster: The Data Orchestrator
(by Elementl)
State of Machine Learning Model Servers In Production
(by Anyscale)
Repositories
Awesome Production Machine Learning
(by The Institute for Ethical AI & Machine Learning)
MLOps References
(by InnoQ)
ML Applied in Production
(by Eugene Yan)
Feature Stores for ML
(by KTH Royal Institute of Technology)
Feature Store: The Missing Data Layer in ML Pipelines?
(by Logical Clocks)
Tutorials
Deep Learning for OCR, Document Analysis, Text Recognition, and Language Modeling
(ICDAR 2019)
Image Retrieval in the Wild
(CVPR 2020)
Handwritten Text Recognition (OCR) with MXNet Gluon
(AWS Labs)
Course Content - Previous
Labs
Next - Guest Lectures
Xavier Amatriain (Curai)
Last modified
2yr ago