CMU深度学习课

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“Deep Learning” systems, typified by deep neural networks, are increasingly taking over all AI tasks, ranging from language understanding, and speech and image recognition, to machine translation, planning, and even game playing and autonomous driving. As a result, expertise in deep learning is fast changing from an esoteric desirable to a mandatory prerequisite in many advanced academic settings, and a large advantage in the industrial job market.

In this course we will learn about the basics of deep neural networks, and their applications to various AI tasks. By the end of the course, it is expected that students will have significant familiarity with the subject, and be able to apply Deep Learning to a variety of tasks. They will also be positioned to understand much of the current literature on the topic and extend their knowledge through further study.

Course description from student point of view

The course is well rounded in terms of concepts. It helps us understand the fundamentals of Deep Learning. The course starts off gradually with MLPs and it progresses into the more complicated concepts such as attention and sequence-to-sequence models. We get a complete hands on with PyTorch which is very important to implement Deep Learning models. As a student, you will learn the tools required for building Deep Learning models. The homeworks usually have 2 components which is Autolab and Kaggle. The Kaggle components allow us to explore multiple architectures and understand how to fine-tune and continuously improve models. The task for all the homeworks were similar and it was interesting to learn how the same task can be solved using multiple Deep Learning approaches. Overall, at the end of this course you will be confident enough to build and tune Deep Learning models.

What students say about the previous edition of the course

Instructor: Bhiksha Raj

  • bhiksha@cs.cmu.edu

TAs:

  • Ryan Brigden (rbrigden@andrew.cmu.edu)
  • Raphael Franck Olivier (rolivier@andrew.cmu.edu)
  • Sai Nihar Tadichetty (ntadiche@andrew.cmu.edu)
  • Shubham Tripathi (shubhamt@andrew.cmu.edu)
  • Soham Ghosh (sohamg@andrew.cmu.edu)
  • Madhura Das (madhurad@andrew.cmu.edu)
  • Ipsita Prakash (iprakash@andrew.cmu.edu)
  • Dhruv Shah (ddshah@andrew.cmu.edu)
  • Shaden Shaar (sshaar@andrew.cmu.edu)
  • David Zhang (davidz1@andrew.cmu.edu)
  • Anushree Prasanna Kumar (apkumar@andrew.cmu.edu)
  • Ahmed Shah (mshah1@andrew.cmu.edu)
  • Jiawei Yang (jiaweiy@andrew.cmu.edu)
  • Omar Khattab for Doha (okhattab@cmu.edu)
  • Nebiyou Yismaw for Kigali (nyismaw@andrew.cmu.edu)

Lecture: Monday and Wednesday, 9.00am-10.20am

Location: Gates-Hillman Complex GHC 4102

Recitation: Friday, 9.00AM-10.20AM

Office hours:

  • TBD

Prerequisites

  1. We will be using one of several toolkits (the primary toolkit for recitations/intruction is PyTorch). The toolkits are largely programmed in Python. You will need to be able to program in at least one of these languages. Alternately, you will be responsible for finding and learning a toolkit that requires programming in a language you are comfortable with,
  2. You will need familiarity with basic calculus (differentiation, chain rule), linear algebra and basic probability.

Units

This course is worth 12 units.