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Upcoming events (2)
Register and attend here:
(Free online AI tech talk event, you can join from anywhere with zoom)
* pre-event networking (20mins)
* community updates, jobs/interns/talents announcements. (5mins)
* tech talk and Q&A (45mins)
Social networking with speakers, global attendees 30mins before/after the event with community update, AI jobs/intern openings, talents available announcements, etc..
Join slack by the invitation: https://bit.ly/3oCMUbW
Feature stores have emerged as critical technologies in a modern ML stack. They aim to solve the full set of data management problems encountered when building and operationalizing data for ML applications.
In this webinar we will dive into the design and concepts of feature stores, where feature stores fit in the ML stack, and the problems they solve. We will then provide a hands on walk through in deploying an end-to-end ML system that leverages a Feast as its feature store, and Kubeflow as the ML platform.
Paid online live course (using zoom), follow instructions below to enroll
4-weeks AI course: Deep Learning for NLP with PyTorch (Cohort 2)
Start date: March 3rd，11AM PST (GM-8), Every Wed/Fri.
** 40% off promotion price will end on 2/19.
In the AICamp online classroom (powered by Zoom), we will meet twice a week and you will interact with instructors and other classmates, listen the lectures, discuss problems; After class, you will work on projects, homework, and get support on private group on slack.
The course include:
* 4 weeks/ 8 sessions/ 16 hours
* 8 lectures / 8 hands-on projects
* Live Sessions, Real time interaction
* Capstone projects, work with peer students globally
* Slack supports to projects and homework
* Earn certificate upon course completion
Natural Language Processing (NLP) is the fastest-growing field of deep learning with interest and funding from top AI companies to solve problems of language, text, and unstructured information. This has resulted in a tremendous focus on model building that combines language, mathematics, and computer science.
This 4-weeks course will focus on problems of text summarization, question answering, and sentiment classification using modern approaches to model-building (GNMT, BERT, and GPT2). We will apply this to real-world problems to create an NLP pipeline on top of the PyTorch framework and spaCy.
The course offers both theoretical and practical, lab-heavy modules. By completing you would be able to:
* Have working knowledge of PyTorch to train your own deep learning models.
* Use OpenVINO to run model optimizer
* Use less compute and memory for deploying model inference in production.
* Build end to end NLP pipeline with everything you learn