Topic 8: Artificial Intelligence 1664


Title (link) Author Date Votes Error
A SURVEY IN ADVANCED REINFORCEMENT LEARNING Joyce Xu 2019
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Neural Architecture search Thomas Elskin 2019
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How to Root Out Hidden Biases in AI Cynthia Dwork 2017
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Algorithms for Reinforcement Learning Csaba Szepesvari 2015
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Computer Vision by Andrew Ng_- 11 Lessons Learned Ryan Shrott 2017
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A Year In Computer Vision Benjamin F. Duffy, Daniel R. Flynn 2017
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Artificial Intelligence and Games Georgios N. Yannakakis and Julian Togelius 2018
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Reinforcement Learning: An Introduction Richard S. Sutton, Andrew G. Barto 2018
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Deep Reinforcement Learning: An Overview Yuxi Li 2017
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Algorithms for Reinforcement Learning Csaba Szepesvari 2009
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Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review Sergey Levine 2017
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Reinforcement Learning: An Introduction (2nd ed) Richard S. Sutton and Andrew G. Barto 2017
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Theory of Active Learning Steve Hanneke 2014
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A Survey of Monte Carlo Tree Search Methods Cameron Browne, Edward Powley, Daniel Whitehouse, Simon Lucas, Peter I. Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, Simon Colton 2012
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Reinforcement Learning: A Tutorial. Harmon, Mance E and Harmon, Stephanie S 1997
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Deep Reinforcement Learning, HW#3: Q-Learning on Atari UC Berkeley 2017
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TADAM: Task dependent adaptive metric for improved few-shot learning Boris N. Oreshkin, Pau Rodriguez, Alexandre Lacoste 2018
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A Brief Survey of Deep Reinforcement Learning Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath 2017
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A Survey on Deep Transfer Learning Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu 1333
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Meta-Learning Joaquin Vanschoren 1333
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A Survey on Multi-Task Learning Yu Zhang, Qiang Yang 2018
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The Long Arc of History: Neural Network Approaches to Diachronic Linguistic Change Eun Seo Jo, Mark Algee-Hewitt 2018
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Deep Reinforcement Learning Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath 2017
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A Survey on Multi-Task Learning Yu Zhang and Qiang Yang 2017
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Uncertainty: A Tutorial Eric Jang 2018
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Title (link) Author Date Votes Error
Pomegranate Author Unknown 2018
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Fairseq Facebook 2018
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Horizon facebookresearch 2019
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TRFL deepmind 2018
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Dopamine google 2019
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DeepReinforcementLearning AppliedDataSciencePartners 2018
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r2c rowanz 2019
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Catalyst "Kolesnikov, Sergey" 2018
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Stable Baselines "Hill, Ashley and Raffin, Antonin and Ernestus, Maximilian and Gleave, Adam and Traore, Rene and Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and 2018
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Reinforcement Learning Coach "Caspi, Itai and Leibovich, Gal and Novik, Gal and Endrawis, Shadi" 2017
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Garage "Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel" 2019
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Spinning Up jachaim 2018
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"ELF: An Extensive, Lightweight and Flexible Platform for Game Research" Yuandong Tian and Qucheng Gong and Wenling Shang and Yuxin Wu and C. Lawrence Zitnick 2017
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Deep Reinforcement Learning for Keras Matthias Plappert 2016
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pytorch-a2c-ppo-acktr "Kostrikov, Ilya" 2018
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Rainbow: Combining Improvements in Deep Reinforcement Learning ahundt 2017
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Velocity in deep-learning research Jerry Tworek 2018
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Tensorforce: a TensorFlow library for applied reinforcement learning "Kuhnle, Alexander and Schaarschmidt, Michael and Fricke, Kai" 2017
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SLM Lab "Wah Loon Keng, Laura Graesser" 2017
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Surreal "Fan, Linxi and Zhu, Yuke and Zhu, Jiren and Liu, Zihua and Zeng, Orien and Gupta, Anchit and Creus-Costa, Joan and Savarese, Silvio and Fei-Fei, Li" 2018
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Lagom "Zuo, Xingdong" 2018
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Python MARL framework Mikayel Samvelyan and Tabish Rashid and Christian Schroeder de Witt and Gregory Farquhar and Nantas Nardelli and Tim G. J. Rudner and Chia-Man Hung and Philiph H. S. Torr and Jakob Foerster and Shimon 2019
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RLlab "Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel." 2016
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Reinforcement Learning Agents in Javascript Andrej Karpathy 2015
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Brown-UMBC Reinforcement Learning and Planning (BURLAP) James MacGlashan 2016
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Lua/Torch implementation of DQN Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, 2015
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pycolab: a highly-customisable gridworld game engine DeepMind 2017
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3D Face Recognition from a Single Image Aaron S. Jackson, Adrian Bulat, Vasileios Argyriou, Georgios Tzimiropoulos 2017
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Chess reinforcement learning by AlphaGo Zero methods Samuel Gravan 2018
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DeepPavlov deepmipt 2018
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Reinforcement-Learning-Notebooks Pulkit Khandelwal 2018
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OpenAI Gym Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba 2018
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Transfer Learning for NLP: Sentiment Analysis on Amazon Reviews feedly 2018
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Conceptual Captions Dataset Piyush Sharma, Nan Ding, Sebastian Goodman, Radu Soricut 2018
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Explosion Matthew Honnibal, Ines Montani 2018
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DeepMind Open-Sources RL Library TRFL Synced 2018
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PyTorch Geometry E. Riba, M. Fathollahi, W. Chaney, E. Rublee, G. Bradski 2019
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ArviZ: Exploratory analysis of Bayesian models ArviZ devs 2018
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TRFL: TensorFlow Reinforcement Learning DeepMind Research Engineering team 2018
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Natural Language for Visual Reasoning clic-lab 2018
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Neural Network to colorize grayscale images pavelgonchar 2016
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Bayesian neural network using Pyro and PyTorch on MNIST dataset paraschopra 2018
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Universe Author Unknown 2017
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tf-agent Andrej Karpathy 2017
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JgibbLDA: A Java Implementation of Latent Dirichlet Allocation (LDA) Xuan-Hieu Phan and Cam-Tu Nguyen 2008
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TorchMoji Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan and Sune Lehmann 2017
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Competitive Multi-Agent Environments OpenAI 2017
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BayesPy Jaakko Luttinen 2017
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BayesPy Introduction Jaakko Luttinen 2017
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BayesPy - Bayesian Python Jaakko Luttinen 2017
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Bayesian Python: Bayesian inference tools for Python Jaakko Luttinen 2017
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libpgm Charles Cabot 2012
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Gym - 32 levels of original Super Mario Bros Philip Paquette 2017
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PyTorch implementation of Advantage Actor Critic (A2C), PPO, ACKTR Ilya Kostrikov 2017
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Multi-Agent Particle Environment Igor Mordatch, Pieter Abbeel 2017
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AVA Dataset Chunhui Gu 2017
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Neural Adaptive Machine Translation Marcello Federico 2017
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Pong Game Reinforcement Learning Jules Verny 2017
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Deep Reinforcement Learning Agents Awjuliani 2017
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Sign-Recognition Kashyap Raval 2017
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Mycroft AI Open Source Artificial Intelligence Joshua Montgomery 2017
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OpenAI Baselines Szymon Sidor, Matthias Plappert 2017
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Visdom Facebook 2017
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RL Lab Berkeley RL Lab 2016
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Reinforce.js Andrej Karpathy 2016
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GridWorld: Dynamic Programming Demo Andrej Karpathy 2016
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Temporal Difference Learning Gridworld Demo Andrej Karpathy 2016
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PuckWorld: Deep Q Learning Andrej Karpathy 2016
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Waterworld Demo Andrej Karpathy 2016
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A Hierarchical Approach for Generating Descriptive Image Paragraphs Jonathan Krause, Justin Johnson, Ranjay Krishna, Fei-Fei Li 2017
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Title (link) Author Date Votes Error
The AI Podcast NVIDIA 2017
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Imagination-augmented agent plays Sokoban DeepMind 2017
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Reinforcement Learning Part 3 ? Challenges & Considerations Bill Vorhies 2017
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Under the Hood with Reinforcement Learning ? Understanding Basic RL Models Bill Vorhies 2017
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Reinforcement Learning and AI Bill Vorhies 2017
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DeepRLHacks William Falcon 2017
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Etymo: A visual search engine for AI research Etymo 2017
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Blog Post (Part III): Deep Reinforcement Learning with OpenAI Gym JD Co-Reyes 2016
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Guest Post (Part I): Demystifying Deep Reinforcement Learning Tambet Matiisen 2015
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Guest Post (Part II): Deep Reinforcement Learning with Neon Tambet Matiisen 2015
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Proximal Policy Optimization John Schulman, Oleg Klimov, Filip Wolski, Prafulla Dhariwal & Alec Radford. 2017
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OpenAI Gym Beta John Schulman, Oleg Klimov, Filip Wolski, Prafulla Dhariwal & Alec Radford. 2016
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Deep Reinforcement Learning, HW#3: Q-Learning on Atari UC Berkeley 2017
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OpenAI Gym Documentation OpenAI 2017
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Introducing: Unity Machine Learning Agents Arthur Juliani 2017
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Explained Simply: How DeepMind taught AI to play video games Aman Agarwal 2017
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Easy21 Author Unknown 2017
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The Dawn of Artificial Intelligence Author Unknown 2015
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DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving na 2015
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Getting started with Deep Learning for Computer Vision with Python Adrian Rosebrock 2017
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Deep dream: Visualizing every layer of GoogLeNet Adrian Rosebrock 2015
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Meta-Learning: Learning to Learn Fast Lilian Weng 2018
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First TextWorld Problems—Microsoft Research Montreal’s latest AI competition is really cooking Wendy Tay, Adam Trischler 2018
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TextWorld N/A 2019
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Genetic Algorithm Implementation in Python Ahmed Gad 2018
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Facebook’s Open-Source Reinforcement Learning Platform — A Deep Dive Xavier Geerinck 2018
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Visual Commonsense Reasoning Rowan Zellers, Yonatan Bisk, Ali Farhadi, Yejin Choi 2018
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Essentials of Deep Learning: Exploring Unsupervised Deep Learning Algorithms for Computer Vision Faizan Shaikh 2018
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Evolutionary Algorithm – The Surprising and Incredibly Useful Alternative to Neural Networks Pranav Dar 2018
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Using Object Detection for Complex Image Classification Scenarios Part 1: Aaron (Ari) Bornstein 2018
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Using Object Detection for Complex Image Classification Scenarios Part 2: Aaron (Ari) Bornstein 2018
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RL — Model-based Reinforcement Learning Jonathan Hui 2018
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Applications of Reinforcement Learning in Real World Garychl 2018
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Reinforcement learning N/A 2019
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Learning and performing in the real world Eugenio Culurciello 2017
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Learning and performing in the real world — part 2 Eugenio Culurciello 2018
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Inverse Reinforcement Learning Alexandre Gonfalonieri 2018
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Reinforcement learning fundamentals narjes karmani 2019
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Refresh: Ranking Sentences for Extractive Summarization with Reinforcement Learning Shashi Narayan, Shay B. Cohen and Mirella Lapata 2018
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Naive Bayes classification from Scratch in Python pavan kalyan urandur 2018
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Reinforcement learning (RL) 101 with Python Gerard Martínez 2018
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RL— Introduction to Deep Reinforcement Learning Jonathan Hui 2018
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Introduction to Reinforcement Learning Anubhav Singh 2018
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Reinforcement Learning (1): Q-Learning basics Irene Li 2018
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Reinforcement learning without gradients: evolving agents using Genetic Algorithms Paras Chopra 2019
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RL in NMT: The Good, the Bad and the Ugly Julia Kreutzer 2019
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RL — Policy Gradient Explained Jonathan Hui 2018
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Reinforcement Learning: Introduction to Monte Carlo Learning using the OpenAI Gym Toolkit Ankit Choudhary 2018
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Baby AI platform Maxime Chevalier-Boisvert and Dzmitry Bahdanau and Salem Lahlou and Lucas Willems and Chitwan Saharia and Thien Huu Nguyen and Yoshua Bengio 2019
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State-of-the-Art Conversational AI with Transfer Learning Thomas Wolf and Victor Sanh and Julien Chaumond and Clement Delangue 2019
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MONTRAL.AI ACADEMY: ARTIFICIAL INTELLIGENCE 101 FIRST WORLD-CLASS OVERVIEW OF AI FOR ALL Vincent Bouchet 2019
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My Journey to Reinforcement Learning???Part 0: Introduction Jae Duk Seo 2018
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Introduction to Naive Bayes Classification Devin Soni 2018
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Introductory Guide to Artificial Intelligence Egor Dezhic 2018
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AI-Playbook a16z 2017
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Reinforcement Learning MDPs Ray Zhang 2018
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Understanding Reinforcement Learning Egor Dezhic 2018
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Reinforcement Learning???Part 1 Prakhar Mishra 2017
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Reinforcement Learning???Part 2 Prakhar Mishra 2017
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Reinforcement Learning???Part 3 Prakhar Mishra 2017
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Reinforcement Learning???Part 4 Prakhar Mishra 2017
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Reinforcement Learning???Part 5 Prakhar Mishra 2018
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Reinforcement Learning???Part 6 Prakhar Mishra 2018
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Bayesian Thinking for Toddlers Eric-Jan Wagenmakers 2020
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Artificial intelligence, revealed Yann LeCun, Joaquin Quiñonero Candela 2016
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Artificial Intelligence?s Next Big Step: Reinforcement Learning Mary Branscombe 2017
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Awesome Reinforcement Learning Hyunsoo Kim, Jiwon Kim 2015
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Artificial intelligence Ted Playlist TED 2017
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Artificial intelligence, revealed Yann LeCun, Joaquin Quinonero Candela 2016
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List of the best resources to learn the foundations of Artificial Intelligence Ray Alez 2015
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AI, Deep Learning, and Machine Learning: A Primer Frank Chen 2017
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Googles Great AI Awakening: We didnt even know we hired the best AI scientists in Google Eric Schmidt 2017
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A DARPA Perspective on Artificial Intelligence John Launchbury 2017
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AlphaGo and Reinforcement Learning: How the Machine Thinks Under the Hood Tom 2017
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Neural Networks for NLP Code Examples Graham Neubig, Daniel Clothiaux, Zhengzhong Liu, and Xuezhe Ma 2017
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Some Highlights of MILA Deep Learning and Reinforcement Learning Summer Schools 2017 Mostafa Dehghani 2017
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VAE = EM David McAllester 2017
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Teachable Machine Google 2017
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Catch Keras RL Eder Santana 2017
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The Biggest Challenges in Implementing AI Sunil Kappal 2017
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Hello, World: Building an AI that understands the world through video Twenty Billion Neurons 2017
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How do I Build an NLG System: Tools? Ehud Reiter 2017
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How to Train your Self-Driving Car to Steer Norman Di Palo 2017
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Deep Reinforcement Learning David Silver 2016
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AlphaGo, in context Andrej Karpathy 2017
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Faster Physics in Python Jonas Schneider, Peter Welinder, Alex Ray, Jonathan Ho & Wojciech Zaremba 2017
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Finding Lane Lines on the Road David Clark 2017
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The Basics of Video Object Segmentation Eddie Smolyansky 2017
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AI Tool Aims to Transform Radiology Elaine Sanchez Wilson 2015
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Deep RL Bootcamp Author Unknown 2017
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DeepMind has a bigger plan for its newest Go-playing AI Dave Gershgorn 2017
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AI Planning Historical Developments Ryan Shrott 2017
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Welcome to Deep Reinforcement Learning Part 1: DQN Takuma Seno 2017
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DeepMind and Blizzard open StarCraft II as an AI research environment Oriol Vinyals, Stephen Gaffney, Timo Ewalds 2017
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How AI Detectives are Cracking Open the Black Box of Deep Learning Paul Voosen 2017
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Computer Vision Andreessen Horowitz 2017
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Training Your Own Models Andreessen Horowitz 2017
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Reinforcement Learning Methods and Tutorials Morvan Zhou 2017
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Reinforcement Learning Part 1: Q-Learning and Exploration Travis DeWolf 2012
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Recurrent vs Recursive Neural Networs: Which is better for NLP? crscardellino, gung 2015
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Deep Reinforcement Learning Demystified (Episode 0) Moustafa Alzantot 2017
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Episode 1 - Genetic Algorithm for Reinforcement Learning Moustafa Alzantot 2017
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Deep Reinforcement Learning Demystifed (Episode 2) - Policy Iteration, Value Iteration and Q-Learning Moustafa Alzantot 2017
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Gans Awesome Applications Minchul Shin 2017
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A Dozen Times Artificial Intelligence Startled the World Sumeet Agrawal 2017
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FigureQA: an annotated figure dataset for visual reasoning Author Unknown 2017
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New Theory Cracks Open the Black Box of Deep Learning Natalie Wolchover 2017
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An On-device Deep Neural Network for Face Detection Computer Vision Machine Learning Team 2017
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Reinforcement Learning Part 3 - Challenges & Considerations William Vorhies 2017
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Under the Hood with Reinforcement Learning - Understanding Basic RL Models William Vorhies 2017
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Reinforcement Learning and AI William Vorhies 2017
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Deep learning revolutionizes conversational AI Yishay Carmiel 2017
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Gathering Human Feedback Tom Brown, Dario Amodei, Paul Christiano 2017
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New API for pretrained NLP models and datasets in Gensim Chaitali Saini 2017
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Deep Reinforcement Learning: An Overview Yuxi Li 2017
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Deep Learning Cars Samuel Artz 2016
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Implementation of various Reinforcement Learning Algorithms Alok Bishoyi 2018
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Going beyond average for reinforcement learning Marc G. Bellemare, Will Dabney, R?mi Munos 2017
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Markov Decision Processes and Reinforcement Learning Daniel Seita 2015
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Going Deeper Into Reinforcement Learning: Understanding Deep-Q-Networks Daniel Seita 2016
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Going Deeper Into Reinforcement Learning: Understanding Q-Learning and Linear Function Approximation Daniel Seita 2016
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Language modeling using Recurrent Neural Networks Part - 1 Tushar Pawar 2017
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Using Artificial Intelligence to Augment Human Intelligence Shan Carter, Michael Nielsen 2017
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Introduction to Altair - A Declaritive Visualization Library in Python Shubham Jain 2017
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Learning about the world through video Moritz Mueller-Freitag 2017
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Artificial Intelligence Demystified Guest Blog 2016
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Is Learning Rate Useful in Artificial Neural Networks Ahmed Gad 2017
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IBM Code Patterns and the Future of AI Tom Smith 2017
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AI in 2018 for researchers Alex Honchar 2017
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Shallow Neural Network from scratch (deeplearning.ai assignment) The Rickest Ricky 2017
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Introduction to A* Red Blog Games 2016
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Exploring Recurrent Neural Networks Packtpub 2018
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Training Recurrent Networks Prakhar Mishra 2018
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Reinforcement Learning Cheat Sheet Francesco Zuppichini 2018
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Decision Trees???Understanding Explainable AI Grant Holtes 2018
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Introduction to Learning to Trade with Reinforcement Learning Denny Britz 2018
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Neural network AI is simple. So... Stop pretending you are a genius. Brandon Wirtz 2018
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Reinforcement learning tutorial using Python and Keras Andy 2018
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What is Artificial Intelligence? Part 1 Rob Guinness 2018
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Beat Atari with Deep Reinforcement Learning! (Part 0: Intro to RL) Adrien Lucas Ecoffet 2017
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Beat Atari with Deep Reinforcement Learning! (Part 1: DQN) Adrien Lucas Ecoffet 2017
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Beat Atari with Deep Reinforcement Learning! (Part 2: DQN improvements) Adrien Lucas Ecoffet 2017
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Reinforcement Learning???Part 1 Prakhar Mishra 2017
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Reinforcement Learning???Part 2 Prakhar Mishra 2017
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Reinforcement Learning???Part 3 Prakhar Mishra 2017
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Reinforcement Learning???Part 4 Prakhar Mishra 2017
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10 Applications of Artificial Neural Networks in Natural Language Processing Olga Davydova 2017
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An introduction to Reinforcement Learning Thomas Simonini 2018
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Introduction to Various Reinforcement Learning Algorithms. Part II (TRPO, PPO) Steeve Huang 2018
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Reinforcement Learning w/ Keras + OpenAI: DQNs Yash Patel 2017
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How to build a Neural Network with Keras Niklas Donges 2018
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Reinforcement Learning w/ Keras + OpenAI: Actor-Critic Models Yash Patel 2017
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A (Long) Peek into Reinforcement Learning Lilian Weng 2018
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ANN Visualizer: A python library for visualizing Artificial Neural Networks (ANN) Kirti Bakshi 2018
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What?s hot in AI: Deep reinforcement learning Sam Charrington 2018
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Multinomial Naive Bayes Classifier for Text Analysis (Python) Syed Sadat Nazrul 2018
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Diving deeper into Reinforcement Learning with Q-Learning Thomas Simonini 2018
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Reinforcement Learning Demystified: A Gentle Introduction Mohammed Ashraf 2018
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Reinforcement Learning Demystified: Markov Decision Processes (Part 1) Mohammed Ashraf 2018
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Reinforcement Learning Demystified: Markov Decision Processes (Part 2) Mohammed Ashraf 2018
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High-Level Explanation of Variational Inference Jason Eisner 2011
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Comprehensive & Practical Inferential Statistics Guide for data science NSS 2017
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Write an AI to win at Pong from scratch with Reinforcement Learning Dhruv Parthasarathy 2016
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TD Learning???Richard Sutton Presentation at Reinforcement Learning Summer School, Montreal 2017 Ranko Mosic 2017
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Deep Reinforcement Learning Lex Fridman 2018
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Temporal-Difference Learning Rich Sutton 2017
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Must know Information Theory concepts in Deep Learning (AI) Abhishek Parbhakar 2018
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AI Safety via Debate Geoffrey Irving, Dario Amodei 2018
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Reinforcement Learning from scratch Emmanuel Ameisen 2018
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Introduction to Bayesian Networks Devin Soni 2018
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Explain yourself, machine. Producing simple text descriptions for AI interpretability. Luke Oakden-Rayner 2018
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Graphs & paths: A*, getting out of a maze. David Pynes 2018
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Tag2Image and Image2Tag???Joint representations for images and text Amine Aoullay 2018
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An Introductory Example of Bayesian Optimization in Python with Hyperopt William Koehrsen 2018
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Applying transfer learning in NLP and CV Lars Hulstaert 2018
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Building an image search service from scratch Emmanuel Ameisen 2018
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Getting started with Elasticsearch in Python Adnan Siddiqi 2018
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Grokking Deep Learning Andrew W. Trask 2016
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Beginners Ask ?How Many Hidden Layers/Neurons to Use in Artificial Neural Networks?? Ahmed Gad 2018
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NLP's ImageNet moment has arrived Sebastian Ruder 2018
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Explaining the 68-95-99.7 rule for a Normal Distribution Michael Galarnyk 2018
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Machine Learning for Humans, Part 5: Reinforcement Learning Vishal Maini 2017
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Face recognition with OpenCV, Python, and deep learning Adrian Rosebrock 2018
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Normalizing Flows Tutorial, Part 1: Distributions and Determinants Eric Jang 2018
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Normalizing Flows Tutorial, Part 2: Modern Normalizing Flows Eric Jang 2018
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Learning to Learn Chelsea Finn 2017
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Multi-task learning Sebastian Ruder 2018
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Multimodal Sebastian Ruder 2018
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10 Mind-Blowing TED Talks on Artificial Intelligence Every Data Scientist & Business Leader Must Watch Pranav Dar 2018
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An Overview of Multi-Task Learning in Deep Neural Networks Sebastian Ruder 2017
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Practical 4: Reinforcement Learning Deep Learning Indaba 2018
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decaNLP salesforce 2018
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SARSA: On-Policy TD control Lilian Weng 2018
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Self Learning AI-Agents Part I: Markov Decision Processes Artem Oppermann 2018
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Policy Gradient Algorithms Lilian Weng 2018
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Variational Autoencoder in Tensorflow – facial expression low dimensional embedding Int8 2016
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Open sourcing TRFL: a library of reinforcement learning building blocks DeepMind Research Engineering team 2018
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Top 10 Pretrained Models to get you Started with Deep Learning (Part 1 – Computer Vision) Pranav Dar 2018
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Self Learning AI-Agents Part II: Deep Q-Learning Artem Oppermann 2018
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Understanding Genetic Algorithms in the Artificial Intelligence Spectrum Manish Kumar 2018
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Introduction to Image Caption Generation using the Avenger’s Infinity War Characters Mohammad Shahebaz 2018
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Horizon: The first open source reinforcement learning platform for large-scale products and services Jason Gauci, Edoardo Conti, Kittipat Virochsiri 2018
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What’s New in Deep Learning Research: How Google Uses Reinforcement Learning to Ask All the Right Questions Jesus Rodriguez 2018
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Multiagent RL Michael Bowling 2018
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Report
Temporal Abstraction Doina Precup 2018
- 0 +
Report
Introduction to Reinforcement Learning with Function Approximation Rich Sutton 2018
- 0 +
Report
Off-Policy Learning Martha White 2018
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Reinforcement Learning and Optimal Control Dimitri P. Bertsekas 2018
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Reinforcement Learning and Optimal Control Chapter 1: Exact Dynamic Programming Dimitri P. Bertsekas 2018
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Reinforcement Learning and Optimal Control Chapter 2: Approximation in Value Space Dimitri P. Bertsekas 2018
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Reinforcement Learning and Optimal Control Chapter 3: Parametric Approximation Dimitri P. Bertsekas 2018
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Reinforcement Learning and Optimal Control Chapter 4: Infinite Horizon Reinforcement Learning Dimitri P. Bertsekas 2018
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Reinforcement Learning and Optimal Control Chapter 5: Aggregation Dimitri P. Bertsekas 2018
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Search I Moses Charikar and Dorsa Sadigh 2019
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Search II Moses Charikar and Dorsa Sadigh 2019
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Search III Moses Charikar and Dorsa Sadigh 2019
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MDPs I Moses Charikar and Dorsa Sadigh 2019
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MDPs II Moses Charikar and Dorsa Sadigh 2019
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Deep Reinforcement Learning Moses Charikar and Dorsa Sadigh 2019
- 0 +
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Games I Moses Charikar and Dorsa Sadigh 2019
- 0 +
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Problem Solving:Search Ansaf Salleb-Aouissi' 2019
- 0 +
Report
Introduction to Artificial Intelligence George Konidaris' 2019
- 0 +
Report
Probabilistic Planning George Konidaris' 2019
- 0 +
Report
Bayesian Networks George Konidaris' 2019
- 0 +
Report
Markov Decision Processes Dan Klein and Pieter Abbeel' 2019
- 0 +
Report
Search Dan Klein and Pieter Abbeel' 2019
- 0 +
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Particle Filters and Applications of HMMs Dan Klein and Pieter Abbeel' 2019
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Uncertainty and Utilities Dan Klein and Pieter Abbeel' 2019
- 0 +
Report
Bayes Nets: Independence Dan Klein and Pieter Abbeel' 2019
- 0 +
Report
Introduction Dan Klein and Pieter Abbeel' 2019
- 0 +
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Bayes Nets: Sampling Dan Klein and Pieter Abbeel' 2019
- 0 +
Report
Uncertainty Chuck Dyer' 2019
- 0 +
Report
Introduction Chuck Dyer' 2019
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Game Playing Chuck Dyer' 2019
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Bayesian Networks Chuck Dyer' 2019
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Face Detection and Recognition II Chuck Dyer' 2019
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Report
Constraint Satisfaction Problems Chuck Dyer' 2019
- 0 +
Report
Genetic Algorithms Chuck Dyer' 2019
- 0 +
Report
Uninformed Search Chuck Dyer' 2019
- 0 +
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Deep Graphical Models III David McAllester' 2019
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Report
AlphaZero David McAllester' 2019
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In Search of AGI David McAllester' 2019
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Regularization David McAllester' 2019
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Computer vision Hugo Larochelle' 2019
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Computer vision - data set expansion Hugo Larochelle' 2019
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Computer vision - convolutional RBM Hugo Larochelle' 2019
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Computer vision - parameter sharing Hugo Larochelle' 2019
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Computer vision - object recognition Hugo Larochelle' 2019
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Computer vision - local connectivity Hugo Larochelle' 2019
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Learning+Reasoning Alexander Amini' 2019
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Deep Reinforcement Learning Alexander Amini' 2019
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Bayesian Decision Theory Ethem Alpaydın' 2019
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Report
Reinforcement Learning Ethem Alpaydın' 2019
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Introduction to Bayesian methods continued 2 David Sontag' 2019
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Report
Learning theory and Decision trees David Sontag' 2019
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Report
Introduction to Bayesian methods David Sontag' 2019
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Report
Introduction to Bayesian methods continued 1 David Sontag' 2019
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Introduction to Learning David Sontag' 2019
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Bayesian networks David Sontag' 2019
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Perceptron and Kernels Matt Gormley' 2019
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Report
MLE/MAP and Naïve Bayes Matt Gormley' 2019
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Report
Twitter and Twitter API Tutorial Wei Xu 2019
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Data Preprocessing and Machine Learning with Scikit-Learn Sebastian Raschka 2019
- 0 +
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Image Classification Roger Grosse and Jimmy Ba 2019
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Report
Policy Gradient Roger Grosse and Jimmy Ba 2019
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Report
Go Roger Grosse and Jimmy Ba 2019
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Report
Group L1-Regularization Mark Schmidt 2017
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Report
Empirical Bayes Mark Schmidt 2017
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Report
Loopy Graphical Models Greg Durrett 2017
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Report
Introduction to Causal Inference from a Machine Learning Perspective Brady Neal 2020
- 0 +
Report
Introduction Sergey Levine 2018
- 0 +
Report
Optimal Control and Planning Sergey Levine 2018
- 0 +
Report
Model-Based Reinforcement Learning Sergey Levine 2018
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Report
Advanced Model-Based Reinforcement Learning Sergey Levine 2018
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Report
Model-Based RL and Policy Learning Sergey Levine 2018
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Report
Variational Inference and Generative Models Sergey Levine 2018
- 0 +
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Reframing Control as an Inference Problem Sergey Levine 2018
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Inverse Reinforcement Learning Sergey Levine 2018
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Exploration (Part 1) Sergey Levine 2018
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Exploration: Part 2 Sergey Levine 2018
- 0 +
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Transfer and Multi-Task Learning Sergey Levine 2018
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Supervised Learning of Behaviors Sergey Levine 2018
- 0 +
Report
Meta Reinforcement Learning Sergey Levine 2018
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Distributed RL Sergey Levine 2018
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Towards a Virtual Stuntman Sergey Levine 2018
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Lecture 24 Sergey Levine 2018
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Lecture 25 Sergey Levine 2018
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Tensorflow Overview Sergey Levine 2018
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Introduction to Reinforcement Learning Sergey Levine 2018
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Policy Gradients Sergey Levine 2018
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Actor-Critic Algorithms Sergey Levine 2018
- 0 +
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Value Function Methods Sergey Levine 2018
- 0 +
Report
Deep RL with Q-Functions Sergey Levine 2018
- 0 +
Report
Advanced Policy Gradients Sergey Levine 2018
- 0 +
Report
Introduction to Logic Caiming Xiong 2017
- 0 +
Report
Towards a Theory of Generalization in Reinforcement Learning Sham M. Kakade 2021
- 0 +
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Towards a Theory of Generalization in Reinforcement Learning Sham M. Kakade 2021
- 0 +
Report
Linear Bandits Sham M. Kakade 2021
- 0 +
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Linear Bandits Sham M. Kakade 2021
- 0 +
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Knowlege Bases Graham Neubig 2021
- 0 +
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Margin-Based Methods and Reinforcement Learning for Structured Prediction Graham Neubig 2021
- 0 +
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Adversarial Methods Graham Neubig 2021
- 0 +
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Margin-based and Reinforcement Learning for Structured Prediction Graham Neubig 2021
- 0 +
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Knowledge Bases Graham Neubig 2021
- 0 +
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Advanced Search Algorithms Graham Neubig 2021
- 0 +
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Adversarial Methods Graham Neubig 2021
- 0 +
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Models w/ Latent Random Variables Graham Neubig 2021
- 0 +
Report
Data and Visualization Jin Guo 2021
- 0 +
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Decision Making Jin Guo 2021
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Design for Learning Jin Guo 2021
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Design for Creativity Jin Guo 2021
- 0 +
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Inclusion Design Jin Guo 2021
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AI Safety Jin Guo 2021
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Privacy Jin Guo 2021
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Privacy Jin Guo 2021
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Accountability Jin Guo 2021
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Introduction to Software Engineering Jin Guo 2021
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AI and ML Jin Guo 2021
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Model Quality Jin Guo 2021
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Requirements and Intelligent Systems Jin Guo 2021
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Requirements and Intelligent Systems Jin Guo 2021
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Team and Collaboration Jin Guo 2021
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Data Quality Jin Guo 2021
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Requirements Engineering for AI Jin Guo 2021
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Privacy Jin Guo 2021
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Privacy in the Age of AI Jin Guo 2021
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An Investigation of Bias Through the Lens of Social Data Jin Guo 2021
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Data Accquisition and Management Jin Guo 2021
- 0 +
Report
Intelligent System Security Jin Guo 2021
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Report
Building Safe Systems Jin Guo 2021
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AI Accountability Jin Guo 2021
- 0 +
Report
Image Filtering Noah Snavely 2021
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Edge Detection Noah Snavely 2021
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Image Resampling Noah Snavely 2021
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Feature Detection Noah Snavely 2021
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Feature Invarance Noah Snavely 2021
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Feature Descriptors Noah Snavely 2021
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Feature Matching Noah Snavely 2021
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Image Transformations Noah Snavely 2021
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Image Alignment Noah Snavely 2021
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Ransac Noah Snavely 2021
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Camera Noah Snavely 2021
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Panoramas Noah Snavely 2021
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Single View Modeling Noah Snavely 2021
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Stereo Noah Snavely 2021
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Multiview Stereo Noah Snavely 2021
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Illumination Noah Snavely 2021
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Photometric Stereo Noah Snavely 2021
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Two-view Geometry Noah Snavely 2021
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Structure from Motion Noah Snavely 2021
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Introduction to Recognition Noah Snavely 2021
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Image Classification Noah Snavely 2021
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Generative Adversarial Networks Noah Snavely 2021
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Ethics in Computer Vision Noah Snavely 2021
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Geometry Noah Snavely 2021
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Course wrapup & review Noah Snavely 2021
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Reinforcement Learning Angelica Sun 2021
- 0 +
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Reinforcement Learning 2 Angelica Sun 2021
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Report
Aspect tutorial Jean Mark Gawron 2013
- 0 +
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Logical Translations Jean Mark Gawron 2013
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Logical form tutorial Jean Mark Gawron 2013
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Logical Translations Jean Mark Gawron 2013
- 0 +
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Simple English to Logic Jean Mark Gawron 2013
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Logical Translations Jean Mark Gawron 2013
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Predicate Logic Jean Mark Gawron 2013
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Presupposition Jean Mark Gawron 2013
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Russell’s Analysis Jean Mark Gawron 2013
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Statement logic/predicates Jean Mark Gawron 2013
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Logic trees & Truth Tables Jean Mark Gawron 2013
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Aspectual types of events Jean Mark Gawron 2013
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Introduction to Reinforcement Learning Katherine Keith 2020
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The Learning Problem Malik Magdon-Ismail 2020
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Three Learning Principles Malik Magdon-Ismail 2020
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Three Learning Principles Malik Magdon-Ismail 2020
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Reflecting on Our Path - Epilogue to Part I Malik Magdon-Ismail 2020
- 0 +
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Reflecting on Our Path - Epilogue to Part I Malik Magdon-Ismail 2020
- 0 +
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Mathematical Proofs Keith Schwarz 2021
- 0 +
Report
Indirect Proofs Keith Schwarz 2021
- 0 +
Report
Propositional Logic Keith Schwarz 2021
- 0 +
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First-Order Logic Keith Schwarz 2021
- 0 +
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First-Order Logic Keith Schwarz 2021
- 0 +
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Binary Relations Keith Schwarz 2021
- 0 +
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Binary Relations Keith Schwarz 2021
- 0 +
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Functions Keith Schwarz 2021
- 0 +
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Cardinality Keith Schwarz 2021
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Mathematical Induction Keith Schwarz 2021
- 0 +
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Mathematical Induction Keith Schwarz 2021
- 0 +
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Finite Automata Keith Schwarz 2021
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Finite Automata Keith Schwarz 2021
- 0 +
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Finite Automata Keith Schwarz 2021
- 0 +
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Turing Machines Keith Schwarz 2021
- 0 +
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Turing Machines Keith Schwarz 2021
- 0 +
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Turing Machines Keith Schwarz 2021
- 0 +
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Unsolvable Problems Keith Schwarz 2021
- 0 +
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Unsolvable Problems Keith Schwarz 2021
- 0 +
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Complexity Theory Keith Schwarz 2021
- 0 +
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Complexity Theory Keith Schwarz 2021
- 0 +
Report
Bayesian networks Pedro Domingos 2017
- 0 +
Report
Dynamic Bayesian networks Pedro Domingos 2017
- 0 +
Report
Rational preferences Pedro Domingos 2017
- 0 +
Report
Belief Propagation Pedro Domingos 2017
- 0 +
Report
Kalman filters Pedro Domingos 2017
- 0 +
Report
Learning Bayesian Networks Pedro Domingos 2017
- 0 +
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Markov Networks Pedro Domingos 2017
- 0 +
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Markov Decision Processes Pedro Domingos 2017
- 0 +
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Mixture Models Pedro Domingos 2017
- 0 +
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Markov Networks Pedro Domingos 2017
- 0 +
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Relational Models Pedro Domingos 2017
- 0 +
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Sampling Pedro Domingos 2017
- 0 +
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Statistical Estimation Pedro Domingos 2017
- 0 +
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Inference by variable Pedro Domingos 2017
- 0 +
Report
Artificial Intelligence as Statistical Learning Alejandro Ribeiro 2020
- 0 +
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Artificial Intelligence as Statistical Learning Alejandro Ribeiro 2020
- 0 +
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Problem Solving:Search Ansaf Salleb-Aouissi' 2019
- 0 +
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NLP Tasks and Linear Classification Lili Mou 2019
- 0 +
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Deep Neural Networks Lili Mou 2019
- 0 +
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Word Embeddings & Language Modeling Lili Mou 2019
- 0 +
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Convolutional Neural Networks & Recurrent Neural Networks Lili Mou 2019
- 0 +
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Seq2Seq Models & Attention Mechanism Lili Mou 2019
- 0 +
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Hidden Markov Model Lili Mou 2019
- 0 +
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em.hmm Lili Mou 2019
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Markov Networks Lili Mou 2019
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Sentence Generation Lili Mou 2019
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Report
Uninformed Search Anca Dragan 2020
- 0 +
Report
Constraint Satisfaction Problems I Anca Dragan 2020
- 0 +
Report
Search with Other Agents I Anca Dragan 2020
- 0 +
Report
Markov Decision Processes I Anca Dragan 2020
- 0 +
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Reinforcement Learning I Anca Dragan 2020
- 0 +
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Probability + Bayes Nets: Intro Anca Dragan 2020
- 0 +
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Decision Networks / The Value of Perfect Information Anca Dragan 2020
- 0 +
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Hidden Markov Models Anca Dragan 2020
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Machine Learning: Naive Bayes Anca Dragan 2020
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Machine Learning: Optimization and Neural Networks Anca Dragan 2020
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Particle Filtering Anca Dragan 2020
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Particle Filtering Anca Dragan 2020
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Machine Learning: Optimization and Neural Networks Anca Dragan 2020
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Machine Learning: Optimization and Neural Networks Anca Dragan 2020
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Machine Learning: Neural Networks II and IRL Anca Dragan 2020
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Machine Learning: Neural Networks II and IRL Anca Dragan 2020
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Advanced Topics Anca Dragan 2020
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Advanced Topics Anca Dragan 2020
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Reinforcement Learning I Anca Dragan 2020
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Reinforcement Learning I Anca Dragan 2020
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Reinforcement Learning II Anca Dragan 2020
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Reinforcement Learning II Anca Dragan 2020
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Reinforcement Learning III Anca Dragan 2020
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Reinforcement Learning III Anca Dragan 2020
- 0 +
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Probability + Bayes Nets: Intro Anca Dragan 2020
- 0 +
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Probability + Bayes Nets: Intro Anca Dragan 2020
- 0 +
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Bayes Nets: Representation Anca Dragan 2020
- 0 +
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Bayes Nets: Representation Anca Dragan 2020
- 0 +
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Bayes Nets: Inference Anca Dragan 2020
- 0 +
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Bayes Nets: Inference Anca Dragan 2020
- 0 +
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Bayes Nets: Independence Anca Dragan 2020
- 0 +
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Bayes Nets: Independence Anca Dragan 2020
- 0 +
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Bayes Nets: Sampling Anca Dragan 2020
- 0 +
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Bayes Nets: Sampling Anca Dragan 2020
- 0 +
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Decision Networks / The Value of Perfect Information Anca Dragan 2020
- 0 +
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Decision Networks / The Value of Perfect Information Anca Dragan 2020
- 0 +
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Hidden Markov Models Anca Dragan 2020
- 0 +
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Intro to AI Anca Dragan 2020
- 0 +
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Intro to AI Anca Dragan 2020
- 0 +
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ML: Naive Bayes Anca Dragan 2020
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ML: Naive Bayes Anca Dragan 2020
- 0 +
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ML: Perceptrons and Logistic Regressio Anca Dragan 2020
- 0 +
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ML: Perceptrons and Logistic Regressio Anca Dragan 2020
- 0 +
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Uninformed Search Anca Dragan 2020
- 0 +
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Uninformed Search Anca Dragan 2020
- 0 +
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A* Search and Heuristics Anca Dragan 2020
- 0 +
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A* Search and Heuristics Anca Dragan 2020
- 0 +
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Constraint Satisfaction Problems I Anca Dragan 2020
- 0 +
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Constraint Satisfaction Problems I Anca Dragan 2020
- 0 +
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Constraint Satisfaction Problems II Anca Dragan 2020
- 0 +
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Constraint Satisfaction Problems II Anca Dragan 2020
- 0 +
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Search with Other Agents I Anca Dragan 2020
- 0 +
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Search with Other Agents I Anca Dragan 2020
- 0 +
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Search with Other Agents II Anca Dragan 2020
- 0 +
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Search with Other Agents II Anca Dragan 2020
- 0 +
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Markov Decision Processes I Anca Dragan 2020
- 0 +
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Markov Decision Processes I Anca Dragan 2020
- 0 +
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Markov Decision Processes II Anca Dragan 2020
- 0 +
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Markov Decision Processes II Anca Dragan 2020
- 0 +
Report
Guaranteed Learning of Latent Variable Models through Spectral and Tensor Methods Part 1 Anima Anandkumar 2019
- 0 +
Report
Guaranteed Learning of Latent Variable Models through Spectral and Tensor Methods Part 2 Anima Anandkumar 2019
- 0 +
Report
Guaranteed Learning of Latent Variable Models through Spectral and Tensor Methods Part 3 Anima Anandkumar 2019
- 0 +
Report
Advances in Trinity of AI: Data, Algorithms & Compute Anima Anandkumar 2018
- 0 +
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Trinity of AI Anima Anandkumar 2018
- 0 +
Report
Introduction to Reinforcement Learning and Representation Learning Katerina Fragkiadaki 2020
- 0 +
Report
Monte Carlo Learning Katerina Fragkiadaki 2020
- 0 +
Report
Temporal Difference Learning Katerina Fragkiadaki 2020
- 0 +
Report
Function Approximation for Prediction and Control Katerina Fragkiadaki 2020
- 0 +
Report
Policy gradients Katerina Fragkiadaki 2020
- 0 +
Report
Policy gradients, Actor-Critic Methods Katerina Fragkiadaki 2020
- 0 +
Report
Deep deterministic policy gradient, Pathwise derivatives Katerina Fragkiadaki 2020
- 0 +
Report
Natural Policy Gradients Katerina Fragkiadaki 2020
- 0 +
Report
Multigoal RL, Goal Relabeling Katerina Fragkiadaki 2020
- 0 +
Report
Monte Carlo Tree Search Katerina Fragkiadaki 2020
- 0 +
Report
Monte Carlo Tree Search with Prior Knowledge Katerina Fragkiadaki 2020
- 0 +
Report
Model-based RL in low dimensional state space Katerina Fragkiadaki 2020
- 0 +
Report
Model Learning and Model-based RL in high dimensional sensory space Katerina Fragkiadaki 2020
- 0 +
Report
Model Learning and Model-based RL in sensory space Katerina Fragkiadaki 2020
- 0 +
Report
Exploration guided by Curiosity and External Memory Katerina Fragkiadaki 2020
- 0 +
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Exploration guided by Curiosity and External Memory Katerina Fragkiadaki 2020
- 0 +
Report
The Dance of Data and Doers: A Trajectory-Oriented Perspective on RL Ben Eysenbach 2020
- 0 +
Report
Search on the Replay Buffer: Bridging Planning & RL Ben Eysenbach 2020
- 0 +
Report
Learning and Planning: Temporal abstraction in model based control Katerina Fragkiadaki 2020
- 0 +
Report
Sim2Real transfer Katerina Fragkiadaki 2020
- 0 +
Report
Learning from demonstra/ons and task rewards Katerina Fragkiadaki 2020
- 0 +
Report
Visual Imitation Learning Katerina Fragkiadaki 2020
- 0 +
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Exploration-exploitation in multi-armed bandits Katerina Fragkiadaki 2020
- 0 +
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Exploration-exploitation in experiment design Katerina Fragkiadaki 2020
- 0 +
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Exploration-exploitation in experiment design, Bayesian optimization Katerina Fragkiadaki 2020
- 0 +
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Evolutionary methods for policy search Katerina Fragkiadaki 2020
- 0 +
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Imitation Learning Katerina Fragkiadaki 2020
- 0 +
Report
Markov Decision Processes, Value Iteration, Policy Iteration Katerina Fragkiadaki 2020
- 0 +
Report
Introduction to Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks Katerina Fragkiadaki 2020
- 0 +
Report
OpenAI Gym, Tensorflow Katerina Fragkiadaki 2020
- 0 +
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TensorFlow & Keras Katerina Fragkiadaki 2020
- 0 +
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Imitation Learning Katerina Fragkiadaki 2020
- 0 +
Report
Policy & Value Iteration Katerina Fragkiadaki 2020
- 0 +
Report
Computer vision overview Justin Johnson 2020
- 0 +
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Cameras I Justin Johnson 2020
- 0 +
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Cameras II Justin Johnson 2020
- 0 +
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Light and Shading Justin Johnson 2020
- 0 +
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Math Review I Justin Johnson 2020
- 0 +
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Math Review II Justin Johnson 2020
- 0 +
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Math III + Image Filtering Justin Johnson 2020
- 0 +
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Image Filtering II Justin Johnson 2020
- 0 +
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Edge and Corner Detection Justin Johnson 2020
- 0 +
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Image Descriptors Justin Johnson 2020
- 0 +
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Transformations I Justin Johnson 2020
- 0 +
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Transformations II Justin Johnson 2020
- 0 +
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Intro to Machine Learning Justin Johnson 2020
- 0 +
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Linear Models Justin Johnson 2020
- 0 +
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Optimization Justin Johnson 2020
- 0 +
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Neural Networks Justin Johnson 2020
- 0 +
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Deep Learning for Computer Vision: Introduction Justin Johnson 2019
- 0 +
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Image Classification Justin Johnson 2019
- 0 +
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Linear Classifiers Justin Johnson 2019
- 0 +
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Optimization Justin Johnson 2019
- 0 +
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Neural Networks Justin Johnson 2019
- 0 +
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Backpropagation Justin Johnson 2019
- 0 +
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Convolutional Networks Justin Johnson 2019
- 0 +
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CNN Architectures Justin Johnson 2019
- 0 +
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Hardware and Software Justin Johnson 2019
- 0 +
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Training Neural Networks I Justin Johnson 2019
- 0 +
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Training Neural Networks II Justin Johnson 2019
- 0 +
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Recurrent Networks Justin Johnson 2019
- 0 +
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Attention Justin Johnson 2019
- 0 +
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Visualizing and Understanding Justin Johnson 2019
- 0 +
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Object Detection Justin Johnson 2019
- 0 +
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Image Segmentation Justin Johnson 2019
- 0 +
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3D vision Justin Johnson 2019
- 0 +
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Videos Justin Johnson 2019
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Generative Models I Justin Johnson 2019
- 0 +
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Generative Models II Justin Johnson 2019
- 0 +
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Reinforcement Learning Justin Johnson 2019
- 0 +
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Deep Learning for Computer Vision: Conclusion Justin Johnson 2019
- 0 +
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Saturation-Based Proof Search Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Making it Work Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Beyond (and Beneath) FOL Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Introduction to the Second Part Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Knowledge Representation Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Symbolic Artificial Intelligence: Introduction Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Modelling in Datalog Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Reasoning in Datalog Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Introducing Prolog Giles Reger, Martin Riener, Andre Freitas 2019
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More Prolog Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Even More Prolog Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Revising First-Order Logic Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Models and Resolution Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Translation to Clausal Form Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
Report
Symbolic Artificial Intelligence Mock Exam Solutions Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Symbolic Artificial Intelligence Mock Exam Questions Giles Reger, Martin Riener, Andre Freitas 2019
- 0 +
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Midterm Exam (Solutions) Zico Kolter, Ariel Procaccia 2007
- 0 +
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Midterm Exam Zico Kolter, Ariel Procaccia 2007
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Practice Midterm Exam (Solutions) Zico Kolter, Ariel Procaccia 2007
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Practice Midterm Exam Zico Kolter, Ariel Procaccia 2007
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Homework 0: A* Search Zico Kolter, Ariel Procaccia 2007
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Homework 1: Motion Planning and Optimization Zico Kolter, Ariel Procaccia 2007
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Homework 2: Machine Learning Zico Kolter, Ariel Procaccia 2007
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Homework 3: Probabilistic Modeling and Game Theory Zico Kolter, Ariel Procaccia 2007
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Homework 4: Game Theory and Social Choice Zico Kolter, Ariel Procaccia 2007
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Lecture 1: Introduction Zico Kolter, Ariel Procaccia 2007
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Lecture 10: Learning Theory Zico Kolter, Ariel Procaccia 2007
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Lecture 18: Game Theory I Zico Kolter, Ariel Procaccia 2007
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Lecture 19: Game Theory II Zico Kolter, Ariel Procaccia 2007
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Lecture 2: Search I Zico Kolter, Ariel Procaccia 2007
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Lecture 20: Game Theory III Zico Kolter, Ariel Procaccia 2007
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Lecture 21: Game Theory IV Zico Kolter, Ariel Procaccia 2007
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Lecture 23: Social choice II Zico Kolter, Ariel Procaccia 2007
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Lecture 24: Social choice III Zico Kolter, Ariel Procaccia 2007
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Lecture 25: ai and education I Zico Kolter, Ariel Procaccia 2007
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Lecture 26: ai and education II Zico Kolter, Ariel Procaccia 2007
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Lecture 27: ai and education III Zico Kolter, Ariel Procaccia 2007
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Lecture 3: Search II Zico Kolter, Ariel Procaccia 2007
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Lecture 7: IP Applications Zico Kolter, Ariel Procaccia 2007
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Convolutional and recurrent networks Zico Kolter, Ariel Procaccia 2007
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Deep learning Zico Kolter, Ariel Procaccia 2007
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Integer programming Zico Kolter, Ariel Procaccia 2007
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Linear programming Zico Kolter, Ariel Procaccia 2007
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Machine learning Zico Kolter, Ariel Procaccia 2007
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Optimization Zico Kolter, Ariel Procaccia 2007
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Probabilistic inference Zico Kolter, Ariel Procaccia 2007
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Probabilistic modeling Zico Kolter, Ariel Procaccia 2007
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Lecture 13: Bayesian networks I Moses Charikar, Dorsa Sadigh 2019
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Lecture 14: Bayesian networks II Moses Charikar, Dorsa Sadigh 2019
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Lecture 15: Bayesian networks III Moses Charikar, Dorsa Sadigh 2019
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Lecture 19: Conclusion Moses Charikar, Dorsa Sadigh 2019
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Lecture 11: CSPs I Moses Charikar, Dorsa Sadigh 2019
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Lecture 12: CSPs II Moses Charikar, Dorsa Sadigh 2019
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Lecture 18: Deep Learning Moses Charikar, Dorsa Sadigh 2019
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Lecture 9: Games I Moses Charikar, Dorsa Sadigh 2019
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Lecture 10: Games II Moses Charikar, Dorsa Sadigh 2019
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Lecture 2: Machine learning I Moses Charikar, Dorsa Sadigh 2019
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Lecture 3: Machine learning II Moses Charikar, Dorsa Sadigh 2019
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Lecture 4: Machine learning III Moses Charikar, Dorsa Sadigh 2019
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Lecture 16: Logic I Moses Charikar, Dorsa Sadigh 2019
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Lecture 17: Logic II Moses Charikar, Dorsa Sadigh 2019
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Lecture 7: MDPs I Moses Charikar, Dorsa Sadigh 2019
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Lecture 8: MDPs II Moses Charikar, Dorsa Sadigh 2019
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Lecture 1: Overview Moses Charikar, Dorsa Sadigh 2019
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Lecture 5: Search I Moses Charikar, Dorsa Sadigh 2019
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Lecture 6: Search II Moses Charikar, Dorsa Sadigh 2019
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CS11-747 Neural Networks for NLP Reinforcement Learning for NLP' Graham Neubig' 2017
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Introduction Varada Kolhatkar 2019
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CSPs: Arc Consistency and Domain Splitting Varada Kolhatkar 2019
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CSPs: Domain Splitting and Local Search Varada Kolhatkar 2019
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CSPs: Stochastic Local Search Varada Kolhatkar 2019
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CSPs: Stochastic Local Search Variants Varada Kolhatkar 2019
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Planning: Representation and Forward Search Varada Kolhatkar 2019
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