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🦍 Stanford CS236 : Deep Generative Models

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CS236 Fall 2018, The "IAN" class of Stanford. Generative Models or "GANS" in the spotlight, here I begin my CS236 journey. Though I didn't enroll in the class, I used my stanford email to set up my lab (Google cloud coupons). The course is new, "first taught" this quarter, lets keep learning.

Course

Generative models are widely used in many subfields of AI and Machine Learning. Recent advances in parameterizing these models using deep neural networks, combined with progress in stochastic optimization methods, have enabled scalable modeling of complex, high-dimensional data including images, text, and speech. In this course, we will study the probabilistic foundations and learning algorithms for deep generative models, including Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), autoregressive models, and normalizing flow models. The course will also discuss application areas that have benefitted from deep generative models, including computer vision, speech and natural language processing, graph mining, and reinforcement learning.

  • Grading : Homeworks (15% x 3 = 45%) + Midterm: 15% + Course Project 40%

⌘ Book - Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville. | [pdf]

πŸŽ„ Homeworks

β˜ƒ Homework 1 : Starter Zip : Solution
β˜ƒ Homework 2 : Starter Zip : Solution
β˜ƒ Homework 3 : Starter Zip : Solution

Course :

𓁅 Introduction and Background (slides 1, slides 2)

𓁅 Autoregressive Models (slides 3, slides 4)

𓁅 Variational Autoencoders (slides 5, slides 6)

𓁅 Normalizing Flow Models (slides 7, slides 8)

𓁅 Generative Adversarial Networks (slides 9, slides 10)

Additional Reading: Surveys and tutorials

✑ Exam : Fall@2018-Mid | Collected from public resources

FINAL PROJECT

The Final Project is important and here are the resources - Project Guidelines, Project Proposal Guidelines, Final Report Guidelines, Project Examples and the Nips format to write the Final Project Paper in LaTeX. I ended up doing " ".

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