Data Science 251

Deep Learning in the Cloud and at the Edge

3 units

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This course is not currently offered.

Course Description

This hands-on course introduces data scientists to technologies related to building and operating live, high throughput deep learning applications running on powerful servers in the cloud as well on smaller and lower power devices at the edge of the network. The material of the class is a set of practical approaches, code recipes, and lessons learned. It is based on the latest developments in the industry and industry use cases as opposed to pure theory. It is taught by professionals with decades of industry experience.

Skill Sets

Working with Data at Scale

Tools

Cloud / Distributed Storage / Ethereum Blockchain / Apache Spark / Docker / CouchDB / Apache Cassandra / OpenStack Swift / Apache Solr / BVLC Caffe / Nvidia Digits / Keras / IBM Watson / GATK

Current Course Designer

Profile profile for dima

dimarekesh.jpg
Dima Rekesh
Former Lecturer

Original Course Designers

Profile profile for dima

dimarekesh.jpg
Dima Rekesh
Former Lecturer

(Until Fall 2018, this course was titled “Scaling Up! Really Big Data.”)

Previously listed as DATASCI W251.

Prerequisites

201, 203, and 205. MIDS students only. Students should be able to program in C, Python, or Java and/or be able to pick up a new programming language quickly.

Video

datascience@berkeley | Deep Learning in the Cloud and at the Edge

datascience@berkeley | Deep Learning in the Cloud and at the Edge

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Course History

Summer 2017

Spring 2017

Instructor(s): Brad DesAulniers, Ryan DeJana

Fall 2016

Instructor(s): Dima Rekesh

Summer 2016

Spring 2016

Instructor(s): Brad DesAulniers, Ryan DeJana

Fall 2015

Instructor(s): Brad DesAulniers, Michael Dye
Instructor(s): Thanh Pham, Ryan DeJana

Summer 2015

Instructor(s): Brad DesAulniers, Michael Dye
Instructor(s): Jonathan Dye, Thanh Pham

Spring 2015

Instructor(s): Dima Rekesh, Peter Rodriguez
Instructor(s): Dima Rekesh, Peter Rodriguez

Last updated:

June 8, 2023