Welcome to
Deep Learning

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.

ProfessorRitambhara Singh
TimeMWF 12:00-12:50pm

The Latest

Lectures

Monday, Wednesday, and Friday at 12:00-12:50pm in Salomon Center DECI Course offered in-person with recordings made available for reviewing. This schedule is subject to change.

Week 1-4 Deep Learning Basics
1/24 Welcome to Deep Learning
1/26 Intro to Machine Learning
1/29 Perceptron and MNIST
1/31 Perceptron (continued) and Loss Functions
2/2 Optimization and Backpropagation
2/5 Backpropagation (continued)
2/7 Autodiff
2/9 Matrix representation of NNs + GPUs + Intro to Tensorflow
2/12 Multi-layer NNs and Activation Functions
2/14 Multi-layer NNs (contd.) + Intro to CNNs
Week 4-6 CNNs
2/16 CNNs
2/21 Multi-layer CNNs
2/23 Overfitting and regularization
2/26 Language Models and Word Embeddings
Week 6-9 Language Models
2/28 Feedforward language models
3/1 Recurrent neural networks
3/4 LSTMs + GRUs
3/6 Machine Translation
3/8 Attention
3/11 Transformers
3/13 Transformers (continued) and scaling deep learning systems
3/15 Scaling deep learning systems
3/18 Model Interpretability
Week 9 Interpretation
3/20 Interpretability and Unsupervised Learning
Week 10-11 Probabilistic Models
4/1 Unsupervised learning, Autoencoders
4/3 CLIP (guest lecture by Michal)
4/5 VAEs contd. and Generative adversarial networks
4/8 VAE and GANs contd + Deepfakes
4/10 Diffusion (guest lecture by Calvin Luo)
4/12 Diffusion continued (guest lecture by Calvin Luo)
Week 12-13 Reinforcement Learning
4/15 Introduction to reinforcement learning
4/17 Value Iteration
4/19 Deep Q learning
4/22 RL: Deep Q Learning + REINFORCE
4/24 Actor-critic methods
Week 13 GNNs
4/26 Graph neural networks

Assignments

Assignments will be released at noon and due at 6:00pm U.S. Eastern Time. This schedule is subject to change.

Assignment Out Due
0C Math Review Wednesday 1/24 Friday 2/2
0P Setup Wednesday 1/24 Friday 2/2
1C Beras Part 1: Conceptual Wednesday 1/31 Friday 2/9
1P Beras Part 1: Programming Wednesday 1/31 Wednesday 2/14
2C Beras Part 2: Conceptual Wednesday 2/14 Friday 2/23
2P Beras Part 2: Programming Wednesday 2/14 Wednesday 2/28
3C CNNs: Conceptual Wednesday 2/28 Monday 3/4
3P CNNs: Programming Wednesday 2/28 Friday 3/8
4C LMs: Conceptual Monday 3/11 Monday 3/18
4P LMs: Programming Monday 3/11 Friday 3/22
5C Image Captioning: Conceptual Monday 4/1 Monday 4/8
5P Image Captioning: Programming Wednesday 4/3 Sunday 4/14
6C Variational Autoencoders: Conceptual Friday 4/12 Friday 4/19
6P Variational Autoencoders: Programming Friday 4/12 Friday 4/26

Final Project

See the handout for full details

Deliverable Date/Due
Project Check-in 1 Week beginning 3/4
Project Proposal Friday 3/15 6:00pm ET
Project Check-in 2 Week beginning 4/8
Project Check-in 3 Week beginning 4/22
Final Check-in (Optional) Week beginning 4/29
Deep Learning Day Monday 5/6 & Tuesday 5/7
Final Projects Due Friday 5/10 6:00pm ET

Labs

Check out this guide on opening labs and using Google Colaboratory. This schedule is subject to change.

Lab From Until
0 Introduction to NumPy - No Checkoff Wednesday 1/24 Monday 1/29
1 Introduction to Machine Learning Wednesday 1/31 Tuesday 2/6
2 Optimizers Wednesday 2/7 Tuesday 2/13
3 TensorFlow Wednesday 2/14 Friday 2/16 or Tuesday 2/27*
4 CNNs Wednesday 2/21 Tuesday 2/27
5 Debiasing Wednesday 3/6 Tuesday 3/12
6 LIME Wednesday 3/20 Tuesday 4/2 or Sunday 4/7**
7 Autoencoders Wednesday 4/3 Tuesday 4/9
8 GANs Wednesday 4/10 Tuesday 4/16
9 Reinforcement Learning Wednesday 4/17 Tuesday 4/23
*Lab 3 (Tensorflow): Those with lab sections on Wednesday, Thursday, and Friday will complete the lab during the week of 2/14. Those with lab sections on Saturday, Sunday, Monday, and Tuesday will complete the lab asynchronously and get it checked off the following week (the week of 2/25). **Lab 6 (LIME): Those with lab sections on Wednesday and Thursday will complete the lab during the week of 3/20. Those with lab sections on Monday and Tuesday will complete the lab on 4/1 and 4/2, respectively. Those with lab sections on Friday, Saturday, and Sunday will complete the lab asynchronously and get it checked off by 4/7.

Hours

Resources

Staff

Professor and HTAs: cs1470headtas@lists.brown.edu
Do not email sensitive information, including Health Services & Dean's Notes, to any HTAs, UTAs, or STAs.

Professor

Ritambhara Singh
Ritambhara Singh
she/her
Paracanthurus hepatus

Graduate TA

Michal Golovanevsky
Michal Golovanevsky
she/her
Octopus

Mascot

Jelly
any
Jellyfish

HTAs

Raymond Dai
Raymond Dai
he/him
Manta Ray
Erica Song
Erica Song
she/her
Sea Otter
Joe Dodson
Joe Dodson
he/him
Orca
Karan Kashyap
Karan Kashyap
he/him
Octopus
Pranav Mahableshwarkar
Pranav Mahableshwarkar
he/him
Blobfish
Earth Mokkamakkul
Earth Mokkamakkul
he/him
Manatee

UTAs

Julian Dai
Julian Dai
he/him
Crab
Calvin Eng
Calvin Eng
he/him
Whale
Taj Gillin
Taj Gillin
he/him
Starfish
Spandan Goel
Spandan Goel
he/him
Dolphin
Naicheng (Arnie) He
Naicheng (Arnie) He
he/him
Sea Turtle
Amanda Hernandez Sandate
Amanda Hernandez Sandate
she/her
Walrus
Woody Hulse
Woody Hulse
he/him
Anglerfish
Kelvin Jiang
Kelvin Jiang
he/him
Penguin
Bumjin Joo
Bumjin Joo
he/him
Sea Otter
Preetish Juneja
Preetish Juneja
he/him
Dolphin
Mohammed Khan
Mohammed Khan
he/him
Octopus
Philip LaDuca
Philip LaDuca
he/him
Oyster
Kyle Lam
Kyle Lam
he/him
Jellyfish
Jennifer Li
Jennifer Li
she/her
Blue Whale
Alyssa Loo
Alyssa Loo
she/her
Narwhal
Michael Lu
Michael Lu
he/him
Sea Otter
Ben Maizes
Ben Maizes
he/him
Narwhal
Ken Ngamprasertsith
Ken Ngamprasertsith
he/him
Elephant Seal
Sophia Fang
Sophia Fang
she/her
Seahorse
Aayush Setty
Aayush Setty
he/him
Whale Shark
Jason Silva
Jason Silva
he/him
Penguin
Aryan Singh
Aryan Singh
he/him
Blue Whale
Quinn Straus
Quinn Straus
he/him
Octopus
Torsten Ullrich
Torsten Ullrich
he/him
Otter
Mikayla Walsh
Mikayla Walsh
she/her
Squid
Emily Wang
Emily Wang
she/her
Beluga Whale
Xilin (Rice) Wang
Xilin (Rice) Wang
he/him
Manta
Ray Xu
Ray Xu
he/him
Dolphin
Enyan Zhang
Enyan Zhang
he/him
Flapjack Octopus
Alex Zheng
Alex Zheng
he/him
Jellyfish
Alex Zhou
Alex Zhou
he/him
Penguin

STAs & UTA-STAs

Naphat Permpredanun
Naphat Permpredanun
he/him • STA
Orca
Sameer Sinha
Sameer Sinha
he/him • STA
Otter
Kyle Yeh
Kyle Yeh
he/him • UTA-STA
Marlin
Lingze Zhang
Lingze Zhang
he/him • UTA-STA
Dolphin