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RSVP on meetup is turned off, make sure to register here: https://learn.xnextcon.com/event/eventdetails/W20060110 Start date/time: Jun 1st, 10AM PST /1PM EST Description: "Watching paint dry is faster than training my deep learning model.” “If only I had ten more GPUs, I could train my model in time.” “I want to run my model on a cheap smartphone, but it’s probably too heavy and slow.” If this sounds like you, then you might like this talk. Exploring the landscape of training and inference, we cover a myriad of tricks that step-by-step improve the efficiency of most deep learning pipelines, reduce wasted hardware cycles, and make them cost-effective. We identify and fix inefficiencies across different parts of the pipeline, including data preparation, reading and augmentation, training, and inference. With a data-driven approach and easy-to-replicate TensorFlow examples, finely tune the knobs of your deep learning pipeline to get the best out of your hardware. And with the money you save, demand a raise! Speaker: Anirudh Koul a noted AI expert, UN/TEDx speaker, author of the Practical Deep Learning book and a former scientist at Microsoft AI & Research, where he founded Seeing AI, considered the most used technology among the blind community after the iPhone. He also serves as an ML Lead for NASA FDL and coaches a team for Roborace, the Formula One championship of autonomous driving @200mph.
RSVP on meetup is turned off, make sure to register and attend the event here: https://learn.xnextcon.com/event/eventdetails/W20060310 Start date: June 3rd, 10am PST / 1pm ET. Description: Welcome to the session 5 of the Beam Learning Months! In this session we will learn how eBay builds feature pipelines with Apache Beam. To unify feature extraction and selection in online and offline, to speedup E2E iteration for model training, evaluation and serving, to support different types (streaming, runtime, batch) of features, etc. eBay leverages Apache Beam for their streaming feature SDK as a foundation to integrate with Kafka, Hadoop, Flink, Airflow and others in eBay. Don't forget to sign up other sessions of the series (every Wednesday): * Session 6: Jun 10th, Distributed Processing for Machine Learning Production Pipelines with Tensorflow. Past sessions: * Session 1: May 6th, Interactive Introduction to Apache Beam * Session 2: May 13th, Best practices to a production-ready pipeline * Session 3: May 20th, Introduction to the Spark Runner * Session 4: May 27th, The Best of Both Worlds: Unlocking the Power of Apache Beam with Apache Flink * Session 5: Jun 3rd, Feature Powered by Apache Beam – Beyond Lambda
This is paid online course, please read the instructions below to pay and enroll. price starting from $99 for limited time. Join hundreds of developers from all around the world to learn and practice AI, machine learning with python. This course is online live course. You can listen, watch, interact, Q&A with instructors from anywhere around the world. You work with peer devs on projects. If you miss the live session due to time zone or conflict, you can learn by watching session replay any time and live support on slack. Start date: Jun 9th, 10am PT (US pacific time, check your local time zone). * 3 Weeks / 6 Sessions / 12 hours * 6 lectures / 6 coding exercises * Live Sessions, Real time interaction * Capstone project, Peer students collaboration * Slack supports to projects and homework Enrollment: https://learn.xnextcon.com/course/coursedetails/C20060910 Details: This course covers the key Python skills you will need so you can start using Python for machine learning. The course is ideal for: * Those with some previous coding experience who wants to add Python to their repertoire or level up their basic Python skills. * Aspiring programmers who are learning their first programming language In this course you will learn the fundamentals of Python primarily through a series of coding exercises guided by the instructor. Students will learn about the foundational underpinnings of Python as well as how to put that knowledge to the test with practical exercises. The course takes project-focused approach to teach you Python by building projects. The instructor will walk you through a series of curated projects, and explain the key concepts as they arise. Students will learn the theory and how they work under the hood while writing code