Coder Social home page Coder Social logo

dlsys's Introduction

CSE599G1: Deep Learning System

Here are my solutions for the assignments of DLSYS.

Followings are the descriptions of the course.

Course Information

Over the past few years, deep learning has become an important technique to successfully solve problems in many different fields, such as vision, NLP, robotics. An important ingredient that is driving this success is the development of deep learning systems that efficiently support the task of learning and inference of complicated models using many devices and possibly using distributed resources. The study of how to build and optimize these deep learning systems is now an active area of research and commercialization, and yet there isn’t a course that covers this topic.

This course is designed to fill this gap. We will be covering various aspects of deep learning systems, including: basics of deep learning, programming models for expressing machine learning models, automatic differentiation, memory optimization, scheduling, distributed learning, hardware acceleration, domain specific languages, and model serving. Many of these topics intersect with existing research directions in databases, systems and networking, architecture and programming languages. The goal is to offer a comprehensive picture on how deep learning systems works, discuss and execute on possible research opportunities, and build open-source software that will have broad appeal.

We will have two classes per week. Each week will have one lecture. Another class will either be lab/discuss session or guest lectures. Each lecture will study a specific aspect of deep learning systems. The lab/discussion session will contain tutorials to implement that specific aspect and will include case studies of existing systems, such as Tensorflow, Caffe, Mxnet, PyTorch, and others.

Instructors

Tianqi Chen

Haichen Shen

Arvind Krishnamurthy

Teaching Assistant

Qiao Zhang

Prerequisites

Proficiency in Python, familar in C/C++

We will mainly be using python for case study the existing systems, and C/C++ for some of the background hacking.

Basic knowledge of machine learning (e.g CSE 546)

Prior knowledge in system (operating system/database) is useful but not required.

Acknowledgement

This course is created by University of Washington.

dlsys's People

Contributors

ziyuehuang avatar

Watchers

 avatar  avatar

Forkers

oliviershi

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.