Check out these event recaps...
Aug 20, 2019 (9:00am - 10:00am PDT)
Learn how pmemkv utilizes Persistent Memory, how it is designed, and what algorithms are being used for engines implementation.
Hot Wings at Hot Chips (Conference)
Palo Alto, CA , United States
Aug 19, 2019 (7:00pm - 9:00pm PST)
Join Intel's chief architect, Raja Koduri, for an informal discussion about his vision for the future of computer architecture.
Aug 13, 2019 (9:00am - 10:00am PST)
This webinar will offer a comprehensive overview of nGraph Compiler deep learning.
What are FPGAs and How Do I Use Them? (Webinar)
Aug 6, 2019 (9:00am - 10:00am PST)
High-level overview of what a field programmable gate array is, why they are important as inference accelerators, and more.
SIGGRAPH 2019 (Conference)
Los Angeles, CA, United States
Jul 29 - Aug 1
Discover how Intel is working with industry luminaries to develop state-of-the-art advancements in content creation.
Cloud Native Open Infra Day (Meetup, Hackathon, Workshop)
San Jose, United States
Jul 25, 2019 (6:00pm - 9:00pm PST)
Join us for a fun evening - and cupcakes! Topics include Edge Computing and Akraino, Running Kubernetes on OpenStack*, Kata Containers and more.
Get Started with NLP Architect (Webinar)
Jul 24, 2019 (9:00am - 10:00am PST)
Learn all about the Natural Language Processing Architect open-source library installation process, end-to-end demos and APIs.
Bangalore Developer Summit: 5G, Edge and More (Meetup, Hackathon, Workshop)
Jul 24, 2019 (8:00am - 4:00pm IST)
Hear talks from Intel, Open Networking Foundation and more on topics including 5G and Edge. Seating is limited, so reserve your spot today.
Distributed Deep Learning Using Horovod (Webinar)
Jul 18, 2019 (11:00am - 12:00pm IST)
This webinar covers different types of parallelism – data, model and hybrid along with scaling aspects.
The PlaidML TensorFlow* Compiler (Webinar)
Jul 17, 2019 (9:00am - 10:00am PST)
Learn about PlaidML, Intel’s open-source TensorFlow compiler. Bypass the challenges of using kernel libraries to optimize ML workloads.