Topic 1: Introduction and Linguistics 828


Title (link) Author Date Votes Error Reviews
Dual Decomposition for Natural Language Processing Alexander M. Rush and Michael Collins 2011
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Integer Linear Programming in NLP Constrained Conditional Models Ming-Wei Chang, Nick Rizzolo, Dan Roth 2010
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100 Things You Always Wanted to Know about Linguistics But Were Afraid to Ask Emily M. Bender 2012
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Linear Programming Decoders in NLP: Integer Programming, Message Passing, Dual Decomposition Andre Martins 2014
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Spectral Learning Techniques for Weighted Automata, Transducers, and Grammars Borja Balle, Ariadna Quattoni, Xavier Carreras 2014
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Spectral Algorithms for Latent Variable Models Part II: Dynamical Systems Byron Boots 2012
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Languages of the World Kevin Duh 2014
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Spectral Learning Algorithms for Natural Language Processing Shay Cohen, Michael Collins, Dean Foster, Karl Stratos, and Lyle Ungar 2013
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Spectral Algorithms for Latent Variable Models Part III: Latent Tree Models Le Song 2012
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Natural Language Understanding John F. Sowa, Arun K. Majumdar 2015
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Machine Learning Exercises In Python, Part 2 John Wittenauer 2014
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Python - Tutorial Tutorialspoint 2017
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Matrix Algebra Tutorial Harvey Berman 2017
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Natural Language Processing (NLP) for Computational Social Science Cristian Danescu-Niculescu-Mizil, Lillian Lee 2015
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An Introduction to Machine Learning in Python Sebastian Raschka 2016
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Machine Learning with scikitlearn Sebastian Raschka, Andreas Mueller 2016
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How Bayesian inference Works Brandon Rohrer 2016
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Topics, Trends, and Resources in NLP Mohit Bansal 2015
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A Beginner's Guide to Eigenvectors, PCA, Covariance and Entropy Skymind 2015
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Running Jupyter notebooks on GPU on AWS: a starter guide Francois Chollet 2017
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Machine Learning Cheat Sheet (for scikit-learn) Andreas Mueller 2013
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Morphological language classification Gerhard Jaeger 2016
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Why Python? Liang Huang 2014
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Natural Language Processing: Algorithms and Applications, Old and New Noah Smith 2015
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Real-time Twitter analysis JustGlowing 2013
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IntroNLP Dragomir Radev 2015
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Comprehensive Guide to Learning Python for Data Analysis and Data Science Martijn Theuwissen 2016
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A Pocket Guide to Data Science Brandon Rohrer 2016
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An Example Machine Learning Notebook Randal S. Olson 2017
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Cheatsheet – Python & R codes for common Machine Learning Algorithms Analytics Vidhya 2015
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Dive into Machine Learning Michael Floering 2015
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Machine Learning Exercises In Python, Part 1 John Wittenauer 2014
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Machine Learning Exercises In Python, Part 3 John Wittenauer 2014
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Machine Learning Exercises In Python, Part 4 John Wittenauer 2014
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Machine Learning Exercises In Python, Part 5 John Wittenauer 2014
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Machine Learning Exercises In Python, Part 6 John Wittenauer 2014
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Machine Learning Exercises In Python, Part 7 John Wittenauer 2014
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Machine Learning Exercises In Python, Part 8 John Wittenauer 2014
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AI’s Language Problem Will Knight 2013
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Modern ML for NLP: Large Scale Techniques for Shallow and Deep Learning Dan Pressel 2016
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A Complete Tutorial to Learn Data Science with Python from Scratch Kunal Jain 2016
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12 Useful Pandas Techniques in Python for Data Manipulation Aarshay Jain 2016
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Tutorial – Getting Started with GraphLab For Machine Learning in Python Sunil Ray 2015
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Python tools for NLP Françoise Provencher 2014
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Pandas-Tutorial Adit Deshpande 2017
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Jupyter Notebook Best Practices for Data Science Jonathan Whitmore 2016
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Natural Language Processing Key Terms, Explained Matthew Mayo 2017
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Georgia Tech NLP Notes Jacob Eisenstein 2017
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Patterns for Research in Machine Learning Ali Eslami 2012
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ACL 2015 Tutorial on Matrix and Tensor Factorization Methods for Natural Langauge Processing Jason Naradowsky 2015
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Running Code in Ipython notebook Fernando Pérez 2015
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Ipython Kernel Tutorial Fernando Pérez 2015
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Custom Display Logic: Ipython Fernando Pérez 2015
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scientific-python-lectures Robert Johansson 2012
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Examples from the IPython mini-book Cyrille Rossant 2013
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Python-Lectures Rajath Kumarmp 2015
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matplotlib - 2D and 3D plotting in Python J.R. Johansson 2015
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Introduction to Python Eric Matthes 2013
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Python for Developers Ricardo Duarte 2013
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Introduction to computing with Python for engineering and scientific applications Garth N. Wells 2016
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Python Data Science Handbook Jake Vanderplas 2016
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Key differences between Python 2.7.x and Python 3.x Sebastian Raschka 2013
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A Crash Course in Python for Scientists Rick Muller 2013
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Python for Data Science Joe McCarthy 2015
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Numerical Computing in Python Steve Phelps 2016
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Python Numpy Tutorial Justin Johnson 2016
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Eigenvalues and Eigenvectors, Imaginary and Real Eugene Khutoryansky 2016
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Linear Algebra - Matrix Transformations Eugene Khutoryansky 2015
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An Introduction to Scientific Python (and a Bit of the Maths Behind It) – NumPy Jamal Moir 2016
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Voice Input App in Python – Code Release and Overview Cristi Vlad 2017
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alexafsm, A Finite-State Machine Python Library for Building Complex Alexa Skills Vu Ha 2017
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Getting Started with Pattern Author Unknown 2014
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Dive Into NLTK, Part I: Getting Started with NLTK Author Unknown 2014
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Dive Into NLTK, Part II: Sentence Tokenize and Word Tokenize Author Unknown 2014
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Dive Into NLTK, Part III: Part-Of-Speech Tagging and POS Tagger Author Unknown 2014
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Dive Into NLTK, Part IV: Stemming and Lemmatization Author Unknown 2014
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Dive Into NLTK, Part V: Using Stanford Text Analysis Tools in Python Author Unknown 2014
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Dive Into NLTK, Part VI: Add Stanford Word Segmenter Interface for Python NLTK Author Unknown 2014
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Dive Into NLTK, Part VII: A Preliminary Study on Text Classification Author Unknown 2014
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Dive Into NLTK, Part VIII: Using External Maximum Entropy Modeling Libraries for Text Classification Author Unknown 2014
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Dive Into NLTK, Part IX: From Text Classification to Sentiment Analysis Author Unknown 2014
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Dive Into NLTK, Part X: Play with Word2Vec Models based on NLTK Corpus Author Unknown 2014
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Gradients, Batch Normalization, and Layer Normalization Goku Mohandas 2016
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Question Answering from Unstructured Text by Retrieval and Comprehension Goku Mohandas 2017
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A comprehensive beginners guide to Linear Algebra for Data Scientists Vikas Kumar Yadav 2017
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Apache Spark in Python: Beginners Guide Karlijn Willems 2017
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Tensor Methods in Machine Learning Rong Ge 2015
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Introduction to Natural Language Processing (NLP) 2016 Matt Kiser 2016
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Tutorial: Natural Language Processing in Python Marco Bonzanini, Miguel Martinez-Alvarez 2016
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Natural Language Processing Made Easy – using SpaCy (in Python) Shivam Bansal 2017
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How to read most commonly used file formats in Data Science (using Python)? Ankit Gupta 2017
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Introduction to Gradient Descent Algorithm (along with variants) in Machine Learning Faizan Shaikh 2017
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Extracting information from reports using Regular Expressions Library in Python Yogesh Kulkarni 2017
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Ultimate Guide to Understand & Implement Natural Language Processing (with codes in Python) Shivam Bansal 2017
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The Definitive Guide to Natural Language Processing Javier Couto 2015
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How to get started in NLP Melanie Tosik 2017
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Boost Your Jupyter Notebook Productivity Nazif Berat 2017
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Statistical Data Analysis in Python Christopher Fonnesbeck 2013
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Learning AI if You Suck at Math?—?P7?—?The Magic of Natural Language Processing Daniel Jeffries 2017
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Types of Optimization Algorithms used in Neural Networks and Ways to Optimize Gradient Descent Anish Singh Walia 2017
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How to Handle Missing Timesteps in Sequence Prediction Problems with Python Jason Brownlee 2017
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Data Preparation for Variable Length Input Sequences Jason Brownlee 2017
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Time Series Data Visualization with Python Jason Brownlee 2017
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Linear Algebra for Machine Learning Jason Brownlee 2014
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An overview of gradient descent optimization algorithms Sebastian Ruder 2017
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An Introduction to Scientific Python – Matplotlib Jamal Moir 2016
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An Introduction to Scientific Python (and a Bit of the Maths Behind It) – Matplotlib Jamal Moir 2016
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An Intuitive Guide to Linear Algebra Kalid Azad 2017
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Linear algebra cheat sheet for deep learning Brendan Fortuner 2017
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From Natural Language Processing to Ar4ficial Intelligence Jonathan Mugan 2017
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Linear Algebra for Machine Learning Sargur N. Srihari 2017
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Optimization for Machine Learning (Lecture 1) Suvrit Sra 2017
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Optimization for Machine Learning (Lecture 2) Suvrit Sra 2017
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Optimization for Machine Learning (Lecture 3a) Suvrit Sra 2017
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Optimization for Machine Learning (Lecture 3b) Suvrit Sra 2017
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Tutorial on Optimization methods for machine learning Jorge Nocedal 2012
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Sparse and Regularized Optimization Stephen Wright 2012
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Some Relevant Topics in Optimization Stephen Wright 2012
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Sparse and Regularized Optimization Part 2 Stephen Wright 2012
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Optimization methods for machine learning Part 1 Jorge Nocedal 2012
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ConvNetJS Trainer demo on MNIST Andrej Karpathy 2013
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Lightning tour of spaCy usage Matthew Honnibal 2015
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Gentle Introduction to the Adam Optimization Algorithm for Deep Learning Jason Brownlee 2017
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IPython Tutorial Justin Johnson 2016
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How to read most commonly used file formats in Data Science (using Python)? Ankit Gupta 2017
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Tutorial on Automated Machine Learning using MLBox NSS 2017
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Natural Language Processing in Artificial Intelligence is almost human-level accurate. Worse yet, it gets smart! Rafal 2017
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Getting Started with Python for Data Analysis Zak Jost 2017
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Keep it simple! How to understand Gradient Descent algorithm Jahnavi Mahanta 2017
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How can I tokenize a sentence with Python? Jonathan Mugan 2017
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Ideas on interpreting machine learning Patrick Hall, Wen Phan, SriSatish Ambati 2017
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Data Science Simplified: Principles and Process Pradeep Menon 2017
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Data Science Simplified: Key Concepts of Statistical Learning Pradeep Menon 2017
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How to Scrape Data From Facebook Page Posts for Statistical Analysis Max Woolf 2016
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scikit-learn-interactive-tutorial Andreas Mueller 2017
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Populating GRAKN.AI with the World Jo Stichbury 2016
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Supervised learning is great — it’s data collection that’s broken Ines Montani, Matthew Honnibal 2017
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Supervised similarity: Learning symmetric relations from duplicate question data Matthew Honnibal 2017
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Deep text-pair classification with Quora’s 2017 question dataset Matthew Honnibal 2017
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spaCy v1.0: Deep Learning with custom pipelines and Keras Matthew Honnibal 2016
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The Guide to Learning Python for Data Science Vincent Granville 2016
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Introduction to Python Luis Pedro Coelho 2013
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Modeling Morphologically Rich Languages Chris Dyer 2013
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Introduction to Python Luis Pedro Coelho 2015
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Machine Learning Crash Course: Part 4 - The Bias-Variance Dilemma Daniel Geng, Shannon Shih 2017
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Key Machine Learning PreReq: Viewing Linear Algebra through the right lenses Vincent Granville 2017
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Why Machine Learning Is A Metaphor For Life Adit Deshpande 2017
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How time-series databases help make sense of sensors Justin W. Flory 2017
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Natural Language Processing (NLP) NLP for Computational Social Science Cristian Danescu-Niculescu-Mizil, Lillian Lee 2017
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Stochastic Gradient Methods for Large-Scale Machine Learning Leon Bottou, Frank E. Curtis, Jorge Nocedal 2016
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Optimization Methods for Machine Learning Part II – The theory of SG Leon Bottou, Frank E. Curtis, Jorge Nocedal 2016
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Beyond SG: Noise Reduction and Second-Order Methods Leon Bottou, Frank E. Curtis, Jorge Nocedal 2016
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representations of relationships in text (and images) ...and some other stuff Hal Daume III 2017
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Natural Language Understanding: Foundations and State-of-the-Art Percy Liang 2017
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Tutorial: PART 1 Optimization for machine learning Elad Hazan 2017
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Tutorial: PART 2 Optimization for machine learning Elad Hazan 2017
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Your First Text Mining Project with Python in 3 steps Will Gannon 2017
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Data in computational historical linguistics Gerhard Jäger 2016
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Logical foundations of databases Diego Figueira, Gabriele Puppis 2016
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Logical foundations of databases Diego Figueira, Gabriele Puppis 2016
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Logical foundations of databases Diego Figueira, Gabriele Puppis 2016
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Logical foundations of databases Diego Figueira, Gabriele Puppis 2016
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Logical foundations of databases Diego Figueira, Gabriele Puppis 2016
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Phylogenetic Trees II: Estimating distances, estimating trees from distances Gerhard Jäger 2016
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Learning from Data for Linguists: Lecture 1: Introduction and Setup Malvina Nissim and Johannes Bjerva 2016
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Learning from Data for Linguists: Lecture 2: Evaluation and Naive Bayes Malvina Nissim and Johannes Bjerva 2016
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Learning from Data for Linguists: Lecture 3: Decision Trees Malvina Nissim and Johannes Bjerva 2016
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Learning from Data for Linguists: Lecture 4 Malvina Nissim and Johannes Bjerva 2016
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Learning from Data for Linguists: Lecture 5 Malvina Nissim and Johannes Bjerva 2016
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Phylogenetic Trees IV: Maximum Likelihood Gerhard Jäger 2016
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Phylogenetic Trees III: Maximum Parsimony Gerhard Jäger 2016
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Phylogenetic Trees I Gerhard Jäger 2016
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Sequence Alignment Gerhard Jäger 2016
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How to Learn Python for Data Science in 2017 Author Unknown 2017
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Best Practices for Feature Engineering Author Unknown 2017
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How to Handle Imbalanced Classes in Machine Learning Author Unknown 2017
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The Ultimate Python Seaborn Tutorial: Gotta Catch 'Em All Author Unknown 2017
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Python Machine Learning Tutorial, Scikit-Learn: Wine Snob Edition Author Unknown 2017
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How to Learn Math for Data Science, The Self-Starter Way Author Unknown 2017
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Exploratory Data Analysis in Python Chloe Mawer, Jonathon Whitmore 2017
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Using Scrapy to Build your Own Dataset Michael Galarnyk 2017
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Learning Python: From Zero to Hero TK 2017
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An Interactive Guide To The Fourier Transform Kalid Azad 2017
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Particle filters with Python Alexey Abramov 2015
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Introduction to Dynamic Programming Jesse Farmer 2008
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Squashing Computational Linguistics Noah A. Smith 2017
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Monte Carlo Inference Methods Iain Murray 2017
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Monte Carlo Methods Don M. Dini 2017
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High-Performance Hardware for Machine Learning William Dally 2015
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Artificial Neural Networks I Shubhendu Trivedi 2015
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Artificial Neural Networks II Shubhendu Trivedi 2015
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Optimization for Deep Learning Sebastian Ruder 2017
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MLlib: Scalable Machine Learning on Spark Xiangrui Meng 2017
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A* Heuristic Search Andrew W. Moore 2001
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Bayesian Networks: Independencies and Inference Scott Davies and Andrew Moore 2005
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Bayes Nets for representing and reasoning about uncertainty Andrew W. Moore 2001
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Bayes Net Structure learning Andrew W. Moore 2001
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Machine Learning algorithms Andrew W. Moore 2001
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Bayesian Models for Machine Learning John Paisley 2016
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Maximum Likelihood, Expectation Maximization Pieter Abbeel 2015
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Machine Learning for Humans Vishal Maini, Samer Sabri 2017
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Topic Models David Blei 2009
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Probability: Review Pieter Abbeel 2015
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Just Machine Learning Tina Eliassi-Rad 2017
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Entraînement : Optimisation continue Nicolas Le Roux 2018
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Modality and Negation in Natural Language Processing Roser Morante 2011
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What Every Computational Linguist Should Know About Type-Token Distributions and Zipf’s Law Stefan Evert 2018
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What Every Computational Linguist Should Know About Type-Token Distributions and Zipf’s Law Stefan Evert 2018
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Welcome and Introduction Philip Schulz, Wilker Aziz 2018
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Variational Inference: The Basics Philip Schulz, Wilker Aziz 2018
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Deep Generative Models: Continous Latent Variables Philip Schulz, Wilker Aziz 2018
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Deep Generative Models: Discrete Latent Variables Philip Schulz, Wilker Aziz 2018
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Warm-up: Probability Theory Refresher (and a few more things) Mario A. T. Figueiredo 2018
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Probabilistic Models in Computational Linguistics Christopher Manning 2000
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Texts are Knowledge Christopher Manning 2013
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Learning Language by Grounding Language Karl Moritz Hermann 2018
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Title (link) Lecturer Date Votes Error Reviews
History of NLP Dragomir Radev 2017
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Introduction Dragomir Radev 2017
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Why is NLP Hard? Dragomir Radev 2017
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Methods used in NLP Dragomir Radev 2016
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Probabilities 1 Dragomir Radev 2016
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Probabilities 2 Dragomir Radev 2017
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Bayes Theorem Dragomir Radev 2017
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Linguistics Basics Dragomir Radev 2017
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Parts of Speech Dragomir Radev 2017
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Morphology and Lexicon Dragomir Radev 2017
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Word Distributions Dragomir Radev 2017
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NLP Tasks 1 Dragomir Radev 2016
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NLP Tasks 2 Dragomir Radev 2016
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Preprocessing Dragomir Radev 2017
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Language and Complexity Dragomir Radev 2017
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Natural Language Processing Lecture 1 Noah Smith 2017
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Differential Equations and Linear Algebra Mathworks 2017
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NLP Resources Dragomir Radev 2018
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Python Basics Dragomir Radev 2018
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Class Logistics Dragomir Radev 2018
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NLP Tasks 2 Dragomir Radev 2018
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Preprocessing Dragomir Radev 2018
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Regular Expressions Dragomir Radev 2018
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Why is NLP Hard? Dragomir Radev 2018
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Linguistics Basics Dragomir Radev 2018
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Finite State Transducers Dragomir Radev 2018
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NLP Tasks 1 Dragomir Radev 2018
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Word Distributions Dragomir Radev 2018
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Methods used in NLP Dragomir Radev 2018
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Bayes Theorem Dragomir Radev 2018
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Morphology and Lexicon Dragomir Radev 2018
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Probabilities 2 Dragomir Radev 2018
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Language and Complexity Dragomir Radev 2018
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Linear Algebra 2 Dragomir Radev 2018
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Parts of Speech Dragomir Radev 2018
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NLTK Dragomir Radev 2018
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History of NLP Dragomir Radev 2018
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Probabilities 1 Dragomir Radev 2018
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Machine Learning Resources Dragomir Radev 2018
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Introduction Dragomir Radev 2018
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Machine learning I Percy Liang 2017
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Machine learning II Percy Liang 2017
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Machine learning III Percy Liang 2017
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Introduction to Machine Learning Percy Liang 2017
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Overview and review Percy Liang 2017
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Conclusion Percy Liang 2017
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Overview Percy Liang 2017
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Machine Learning Ansaf Salleb-Aouissi 2014
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Introduction Ansaf Salleb-Aouissi 2014
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Machine Learning George Konidaris 2016
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Python George Konidaris 2016
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Machine Learning George Konidaris 2016
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Naive Bayes Dan Klein and Pieter Abbeel 2014
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Machine Learning: Introduction and Unsupervised Learning Chuck Dyer 2018
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Invariant Theory David McAllester 2017
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Stochastic Gradient Descent David McAllester 2017
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Deep Learning on Graph-Structured Data Efstratios Gavves 2018
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Deeper into Deep Learning and Optimizations Efstratios Gavves 2018
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Training neural networks - optimization Hugo Larochelle 2017
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Training neural networks - backpropagation algorithm Hugo Larochelle 2017
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Natural language processing - preprocessing Hugo Larochelle 2017
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Sparse coding Hugo Larochelle 2017
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Sparse coding - feature extraction Hugo Larochelle 2017
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Neural networks Conditional random elds - motivation Hugo Larochelle 2017
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Restricted Boltzmann Machines Hugo Larochelle 2017
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Training neural networks - model selection Hugo Larochelle 2017
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Neural networks Conditional random elds - factors, sufcient statistics and linear CRF Hugo Larochelle 2017
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Introduction to Machine Learning Furong Huang 2018
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Review and Practice Problems Furong Huang 2018
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(Sub)gradient Descent Furong Huang 2018
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Introduction to Machine Learning Furong Huang 2018
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Decision Trees & Limits of Learning Furong Huang 2018
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A Probabilistic View of Machine Learning I Furong Huang 2018
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Practical Issues: Features, Evaluation, Debugging Furong Huang 2018
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Bias and Fairness Furong Huang 2018
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Decision Trees Furong Huang 2018
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A Probabilistic View of Machine Learning II Furong Huang 2018
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Bias-Variance Tradeoff John Paisley 2017
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Decision Trees John Paisley 2017
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Logistic Regression John Paisley 2017
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Machine Learning for Data Science John Paisley 2017
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Sequential Monte Carlo (Continued) and Conditional Linear Gaussian Models Nicholas Zabaras 2017
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Importance Sampling Prof. Nicholas Zabaras Nicholas Zabaras 2017
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Robbins-Monro for Sequential MLE, Curse of Dimensionality, Conditional and Marginal Gaussian Distributions Nicholas Zabaras 2017
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Introduction to Bayesian Statistics, Exponential Family of Distributions Nicholas Zabaras 2017
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Markov Chain Monte Carlo and Metropolis-Hasting Algorithm Nicholas Zabaras 2017
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Decision trees David Sontag 2016
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Gradient, Subgradient and how they may affect your grade(ient) David Sontag 2016
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Introduction To Machine Learning David Sontag 2016
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Introduction to Machine Learning David Sontag 2016
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Machine Learning David Sontag 2016
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Maximum A Posteriori and Expectation Maximization Ansaf Salleb-Aouissi 2014
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EM for GMM Ansaf Salleb-Aouissi 2014
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Basic Concepts K-nearest neighbors Ansaf Salleb-Aouissi 2014
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Python Roger Grosse 2018
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Bayes Rule and Estimating parameters Tom M. Mitchell 2015
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The Boosting Approach to Machine Learning Maria-Florina Balcan 2015
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Machine Learning Theory II Tom M. Mitchell 2015
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Never-Ending Language Learning Maria-Florina Balcan 2015
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Probability and Bayes Theorem Dan Roth 2018
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Greedy Algorithm Yejin Choi 2018
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Basic Text Processing Regular Expressions Nathan Schneider 2018
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Natural Language Processing Introduction Nathan Schneider 2018
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Project discussion Brendan O’Connor 2015
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Introduction to Natural Language Processing Brendan O’Connor 2015
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Deep Learning for Natural Language Processing: Introductioin Richard Socher 2016
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Midterm Review Richard Socher 2016
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Computational Linguistics Marine Carpuat 2017
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Introduction to Natural Language Processing Jacob Eisenstein 2014
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Morphology Jacob Eisenstein 2014
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Anaphora and coreference Jacob Eisenstein 2014
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Beyond Context-Free Grammars Jacob Eisenstein 2014
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Hidden Markov Models and the Forward-Backward Algorithm Jason Eisner 2012
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Observe some values of a function Learning in the Limit Jason Eisner 2012
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Finite-State Programming Some Examples Jason Eisner 2012
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Natural Language Processing Jason Eisner 2012
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Bayes Theorem Jason Eisner 2012
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Phonology [These slides are missing most examples and discussion from class ] Jason Eisner 2012
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Discovering Morphological Paradigms from Plain Text Using a Dirichlet Process Mixture Model Julia Hockenmaier 2013
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Correlated Topic Models Julia Hockenmaier 2013
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Unsupervised HMM POS Tagger Comparison Julia Hockenmaier 2013
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Probability Lecture #7 Andrew McCallum 2007
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Theory of Automata and Formal Languages ETHZ 2013
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Speech and Language Processing ETHZ 2013
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Introduction to Natural Language Processing ETHZ 2013
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Introduction Raymond J. Mooney 2017
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Data Intensive Linguistics Lecture 16 Philipp Koehn 2008
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Empirical Methods in Natural Language Processing Lecture 15 Philipp Koehn 2008
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CS11-747 Neural Networks for NLP Conditioned Generation Graham Neubig Graham Neubig 2017
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CS11-747 Neural Networks for NLP Intro Graham Neubig 2017
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Basic Text Processing Regular Expressions Dan Jurafsky 2017
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Finite state morphology and phonology Susan W. Brown 2015
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Natural Language Processing Lecture 15 Susan W. Brown 2015
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PREPROCESSING Trevor Cohn 2018
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Media Frames Corpus v2.0 Dallas Card, Amber E. Boydstun, Justin H. Gross, Philip Resnik, Noah A. Smith 2015
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Build It, Break It The Language Edition Emily M. Bender, Hal Daume III, Allyson Ettinger, Harita Kannan, Sudha Rao, Ephraim Rothschild 2017
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DEAPdataset: a dataset for emotion analysis using eeg, physiological and video signals S. Koelstra, C. Muehl, M. Soleymani, J.-S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, I. Patras 2011
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An Update to Open Images - Now with Bounding-Boxes Vittorio Ferrari 2017
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Stanford CoreNLP – Core natural language software Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky 2014
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spaCy: Industrial-strength NLP Matthew Honnibal 2014
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Industrial-Strength Natural Language Processing in Python Matthew Honnibal 2014
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EasyCCG is a CCG parser Mike Lewis 2014
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OpenCCG: The OpenNLP CCG Library Jason Baldridge 2015
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OpenCCG: The OpenNLP CCG Library Jason baldridge 2015
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Pattern CLiPS at University of Antwerp 2017
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CS224u: Natural Language Understanding Code Bill MacCartney and Christopher Potts 2016
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PyBrain Justin Bayer, Martin Felder, Thomas Ruckstieb, Tom Schaul, Frank Sehnke, Daan Wierstra 2010
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nbviewer Author Unknown 2017
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nameparser 0.3.9 Derek Gulbranson 2015
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quepy 0.2 Rafael Carrascosa, Gonzalo Garcia Berrotaran 2013
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OpenFst Library Michael Riley 2007
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python-sqlparse - Parse SQL statements Andi Albrecht 2017
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ScalaNLP David Hall 2017
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Pattern: a web mining module for the Python programming language De Smedt, T. & Daelemans, W. 2012
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Pattern De Smedt, T. & Daelemans, W. 2012
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ReVerb Michael Schmitz, Tony Fader, Niranjan Balasubramanian 2011
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PyNLPl - Python Natural Language Processing Library Maarten van Gompel 2010
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Ucto for Python Maarten van Gompel 2014
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Pandas: Python Data Analysis Library Wes McKinney, Jeff Reback, Joris Van den Bossche, Phillip Cloud, Adam Klein 2009
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PyMC: Bayesian Stochastic Modelling in Python Chris Fonnesbeck, Anand Patil, Thomas Kluyver, John Salvatier 2006
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TextBlob: Simplified Text Processing Steven Loria 2013
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KoNLPy: Korean NLP in Python Eunjeong L. Park, Sungzoon Cho 2014
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Natural language Understanding Toolkit Peter Prettenhofer 2010
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stanfordcorenlp Lynten Guo 2017
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Sounder API Ujjwal Gupta 2017
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Stephanie Ujjwal Gupta 2017
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Japanese NLP Library Pulkit Kathuria 2012
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DEAP documentation Author Unknown 2017
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PythonForArtificialIntelligence Author Unknown 2016
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Altair: Declarative Visualization in Python Brian Granger, Jake VanderPlas 2016
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SciPy - Library of scientific algorithms for Python Tutorial Robert Johansson 2017
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An Introduction to Text Analysis with Python, Part 1 Neal Caren 2017
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An Introduction to Text Analysis with Python, Part 3 Neal Caren 2017
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AllenNLP Matt Gardner, Mark Neumann, Joel Grus, Michael Schmitz 2017
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polyglot Rami Al-Rfou 2014
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Apache OpenNLP Joern Kottmann, William Daniel Colen de Moura Silva, Suneel Marthi, Rodrigo Agerri 2017
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MALLET: MAchine Learning for LanguagE Toolkit Andrew Kachites McCallum 2002
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Facets: Visualizations for machine learning datasets James Wexler 2017
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CatBoost: an open-source gradient boosting library exprmntr 2017
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Bokeh Sarah Bird, Luke Canavan, Maggie Mari, Mateusz Paprocki, Philipp Rudiger, Claire Tang, Bryan Van de Ven 2012
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Manifold learning Scikit-Learn Team 2016
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RethinkDB: The open-source database for the realtime web Sam Hughes 2017
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WikiSQL Victor Zhong, Caiming Ziong, Richard Socher 2017
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Academic Torrents Joseph Paul Cohen, Henry Z Lo, Jonathan Nogueira 2016
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Textacy: Higher-Level NLP built on spaCy Burton DeWilde 2017
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tSNEJS MIT 2017
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Global Neural CCG Parsing with Optimality Guarantees University of Washington NLP 2017
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Eigen: C++ template library for linear algebra Author Unknown 2017
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PPO2 John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov 2017
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Migrating to Python 3 with pleasure Alex Rogozhnikov 2018
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Python Introduction Luis Pedro Coelho 2018
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decaNLP Salesforce 2018
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Tracking Progress in Natural Language Processing Sebastian Ruder 2018
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Introduction to Linguistic Annotation and Text Analytics Graham Wilcock 2009
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Advanced Data Analysis from an Elementary Point of View Cosma Rohilla Shalizi 2016
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Natural language processing: an introduction Prakash M Nadkarni, Lucila Ohno-Machado, Wendy W Chapman 2011
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Spectral Learning Part I Geoff Gordon 2012
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Notes on Probability Peter J. Cameron 2014
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Sample Space and Probability Dimitri P. Bertsekas, John N. Tsitsiklis 2008
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Statistics is Easy! Dennis Shasha, Manda Wilson 2008
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A Technical Introduction to Statistical Natural Language Processing Jacob Eisenstein 2016
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Linear Algebra Ian Goodfellow 2016
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Linear Algebra Review and Reference Zico Kolter, Chuong Do 2015
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