Course Description
Welcome to Deep Learning! Over the past few years, Deep Learning has become a popular area, with deep neural network methods obtaining state-of-the-art results on applications in computer vision (Self-Driving Cars), natural language processing (Google Translate), and reinforcement learning (AlphaGo). These technologies are having transformative effects on our society, including some undesirable ones (e.g. deep fakes).
This course is there to give students a practical understanding of how Deep Learning works, how to implement neural networks, and how to apply them ethically. We introduce students to the core concepts of deep neural networks and survey the techniques used to model complex processes within the contexts of computer vision and natural language processing.
Throughout the course, we emphasize and require students to think critically about potential ethical pitfalls that can result from mis-application of these powerful models. The course is taught using the Tensorflow deep learning framework.
Location
Salomon Center 001
Schedule
Monday, Wednesday, and Friday, 12:00 - 12:50 PM
Instructor
Prof. Eric Ewing
Most Recent Lecture
Most Recent Assignment
Lectures
Weeks 1-4
- 2025-01-22Welcome to Deep Learning
- 2025-01-24Intro to Machine Learning
- 2025-01-27Perceptron and MNIST
- 2025-01-29Perceptrons (continued), MNIST, and MLPs
- 2025-01-31Loss Functions and Gradient Descent
- 2025-02-3Backpropagation and SGD
- 2025-02-5Gradients and Backprop
- 2025-02-7TensorFlow and Autodiff
- 2025-02-10Hyperparameter Tuning and Practical Advice for Training
- 2025-02-12Convolutions Day 1
- 2025-02-14Convolutions Day 2
- 2025-02-19CNN Architectures
- 2025-02-21CNN Architectures
- 2025-02-24ResNet and Regularization
- 2025-02-26Adversarial Learning
- 2025-02-28Graph Neural Networks
- 2025-03-3Language Modelling
- 2025-03-5Introduction to RNNs
- 2025-03-7RNNs and LSTMs
- 2025-03-10LSTMs and seq2seq
- 2025-03-12seq2seq and Attention
- 2025-03-14Attention
- 2025-03-17Guest Lecture: Jason Liu, Language Grounding for Robotics
- 2025-03-19Transformers
- 2025-03-21LLMs
- 2025-03-31Image Generation
- 2025-04-2VAEs
- 2025-04-4GANs
- 2025-04-7Diffusion Models
- 2025-04-9Conditional Generative Models and Intro to RL
- 2025-04-11Q-Learning
- 2025-04-14Q-Learning and Policy Gradient Methods
- 2025-04-16Actor-Critic and Friends
- 2025-04-18PPO, Chat-GPT, and AGI
- 2025-04-21The Future
- 2025-04-21What Comes Next?
Assignments
Mini-Project 1: Deep Learning with Tensorflow and Optimizers
Assignment 2: BERAS
Assignment 3: CNNS
Assignment 4: Image Captioning
Assignment 5: Reinforcement Learning
Course Timeline
Resources
Guides and Tutorials
Working Remotely
Department Resources
Final Project
The final research project is aimed to give you an idea of what a deep learning research project entails, and hopefully, get you excited about doing research in this field. It requires critical thinking that you will develop by learning the material and doing assignments during the semester.
Please read the final project handout in its entirety. It contains all the information, forms, and deadlines you'll need to know about!
Key Deadlines
Mission Control
Do not email sensitive information, including Health Services & Dean's Notes, to any HTAs, UTAs, or STAs.
Commander
Eric Ewing
he/him
Flight Directors
Dave Lubawski
he/him
Yuyang Luo
he/him
Sissy Han
she/her
Winston Li
he/him
Mission Specialists
Adam Lalani
he/him
Johnny Elias
he/him
Navya Sahay
she/her
Marcel Mateos Salles
he/him
Nathan DePiero
he/him
Armaan Patankar
he/him
Bentzi Gitig
he/him
Dhruv Raghavan
he/him
Yujin Chung
he/him