Relating to my IFT6266 course project, I detail the implementation of a Deep Convolutional Generative Adversarial Network (DCGAN), where I hope to get a strong model that understands (or at least gets close to) the distribution behind the real images. The goal as detailed in this post, is to have a competent model in order to be able to use it effectively for reconstructing images.Read More »
My original implementation for extracting a batch of data for training is not the most efficient to say the least as it greatly affects training time. Below I will detail the changes I have made to preprocess the images to accelerate training time. I’d like to thank Francis Dutil for discussing his approach with me and providing his code for inspiration.Read More »
Jupyter Notebook submitted as an assignment for the Reinforcement Learning class at McGill analyzing the theoretical and empirical performance of Expected Sarsa.
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This post details broadly my plan with regards to the deep learning class project for IFT6266. It will be updated as the project evolves.Read More »
Summary and notes on On-Policy First-Visit Monte Carlo method, based on course offered at McGill, in addition to chapter 5 of Richard S. Sutton and Andrew G. Barto, “Reinforcement learning: An introduction”, Second Edition, MIT Press.
Summary and notes of the first class of the Reinforcement Learning (RL) course offered at McGill held on January 6th, in addition to chapter 2 of Richard S. Sutton and Andrew G. Barto, “Reinforcement learning: An introduction”, Second Edition, MIT Press.