Lectures

Date Topic Slides
Week 1 Introduction
Wed, 9/4 Introduction to deep learning pdf | pptx
Fri, 9/6 Perceptrons pdf | pptx
Lab No Lab. - Sign Up for Lab Sections! "What are beginnings but endings of chapters past?" 🤔
Week 2 Deep Learning Fundamentals
Mon, 9/9 Loss functions, cross entropy loss, backprop pdf | pptx
Wed, 9/11 Backprop continued
Fri, 9/13 Automatic differentiation pdf | pptx
Lab 0 (Optional) Intro to Python + Numpy
Week 3 Feed-Forward Neural Networks + Tensorflow
Mon, 9/16 Feedforward nets as matrices
The Life Cycle of Algorithms
pdf | pptx
pdf | pptx
Wed, 9/18 Intro to Tensorflow pdf | pptx
Fri, 9/20 Class cancelled
Lab 1 Automatic differentiation
Week 4 Convolutional Neural Networks 1
Mon, 9/23 Activation functions and multi-layer nets pdf | pptx
Wed, 9/25 Practical neural net miscellanea pdf | pptx
Fri, 9/27 Intro to convolution pdf | pptx
Lab 2 Tensorflow setup + Intro
Week 5 Convolutional Neural Networks 2
Mon, 9/30 Convolution: details and implementation in Tensorflow pdf | pptx
Wed, 10/2 Convolutional neural networks: multi-layer convolution, pooling/downsampling pdf | pptx
Fri, 10/4 Deep learning at Brown/PVD: Project pitch day (external guest speakers describe their work and pitch projects to the class)
Lab 3 Visualizing CNNs
Week 6 Language Modeling
Mon, 10/7 Higher-order tensors
Language models and word embeddings
pdf | pptx
pdf | pptx
Weds, 10/9 Overfitting and regularization pdf | pptx
Fri, 10/11 Feedforward language models pdf | pptx
Lab 4 Word2Vec + Language Modeling
Week 7 Recurrent Neural Networks
Mon, 10/14 No class (Indigenous Peoples' Day)
Wed, 10/16 Recurrent neural networks pdf | pptx
Fri, 10/18 Long Short Term Memory (LSTM) pdf | pptx
Lab 5 De-biasing language models
Week 8 Sequence-to-Sequence Learning
Mon, 10/21 Machine Translation and Sequence-to-Sequence models pdf | pptx
Wed, 10/23 Attention, Transformers pdf | pptx
Fri, 10/25 Transformers (continued)
Environmental Impact of Deep Learning
pdf | pptx
Lab 6 Google Cloud Platform Setup
Week 9 Deep Learning on Structured Data
Mon, 10/28 Deep learning on trees: recursive neural networks (RvNNs) pdf | pptx
Wed, 10/30 Deep learning on graphs: message passing neural networks (MPNNs) pdf | pptx
Fri, 11/1 MPNNs continued
Model Interpretability
pdf | pptx
Lab 7 Optimizers (AdaGrad, Adam)
Week 10 Reinforcement Learning 1
Mon, 11/4 Intro to RL: MDPs, Value Iteration pdf | pptx
Wed, 11/6 Tabular Q learning pdf | pptx
Fri, 11/8 Deep Q learning pdf | pptx
Lab No Lab. Contemplate the statement "Is free will reconcilable with a purely physical world?" 🤔
Week 11 Reinforcement Learning 2
Mon, 11/11 Policy gradient methods, REINFORCE pdf | pptx
Wed, 11/13 Actor-critic methods, A2C pdf | pptx
Fri, 11/15 RL conclusion pdf | pptx
Lab Final project progress checks
Week 12 Unsupervised Learning
Mon, 11/18 Autoencoders pdf | pptx
Wed, 11/20 Autoencoders continued; deep generative models pdf | pptx
Fri, 11/22 Generative Adversarial Networks (GANs) pdf | pptx
Lab 9 Convolutional autoencoders
Week 13 Thanksgiving Week
Mon, 11/25 Variational Autoencoders (VAEs) pdf | pptx
Wed, 11/27 No class (Thanksgiving) 🦃🦃🦃
Fri, 11/29 No class (Thanksgiving) 🦃🦃🦃
Lab No Lab. Consider, most people love themselves more than they hate you... 🤔
Week 14 Conclusion + Extra Topics
Mon, 12/2 Deepfakes pdf | pptx
Wed, 12/4 [Slack day] I wonder how many of the friends I know don't know me yet... 🤔
Fri, 12/6 [Slack day] You may never get to know the people you really miss... 🤔
Lab 10 Generative Adversarial Nets
Week 15 Reading Period
Thu, 12/12 Deep Learning Day! (Final project presentations / mini conference)