Prof. David Blei - Probabilistic Topic Models and User Behavior
David Blei, Professor of Statistics and Computer Science at Columbia University, delivered a lecture entitled 'Probabilistic Topic Models and User Behavior' on ...
The School of Informatics at the University of Edinburgh
LM.2 What is a language model?
Victor Lavrenko
JuliaCon 2019 | Gen: A General-Purpose Probabilistic Programming System
Gen: A General-Purpose Probabilistic Programming System with Programmable Inference Built on Julia This talk introduces a new flexible and extensible ...
The Julia Programming Language
RNN W1L06 : Language Model and sequence generation
Watch the Reinforcement Learning course on Skillshare: https://skl.sh/2WHyoVG Join Skillshare using this link to get 2 months free Premium Membership: ...
Knowledge Center
Template Models: Hidden Markov Models - Stanford University
One simple yet extraordinarily class of probabilistic temporal models is the class of hidden Markov models. Although these are models can be viewed as a ...
Machine Learning TV
Lecture 12: End-to-End Models for Speech Processing
Lecture 12 looks at traditional speech recognition systems and motivation for end-to-end models. Also covered are Connectionist Temporal Classification (CTC) ...
Stanford University School of Engineering
"An Overview of Probabilistic Programming" by Vikash K. Mansinghka
Probabilistic inference is a widely-used, rigorous approach for processing ambiguous information based on models that are uncertain or incomplete. However ...
Strange Loop
Lecture 2.1: Josh Tenenbaum - Computational Cognitive Science Part 1
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Josh ...
MIT OpenCourseWare
Bayesian nonparametrics in document and language modeling
Google Tech Talks August 28, 2008 ABSTRACT Bayesian nonparametric models have garnered significant attention in recent years in both the machine ...
GoogleTechTalks
Characteristics of Model Based Systems Engineering
The rise of model-based systems engineering (MBSE) has greatly reduced the risk and cost of building complex systems at the organizations that have ...
VitechCorp
Probabilistic Programming in the Real World - Zach Anglin
PyData DC 2018 Probabilistic programming frameworks get a lot of press, but relatively little attention is paid to the indicators that a problem is a good fit for a ...
PyData
Probability - The Science of Uncertainty and Data | MITx on edX
Take this course for free on edx.org: https://www.edx.org/course/probability-the-science-of-uncertainty-and-data Build foundational knowledge of data science ...
edX
Natural Language Processing 101 + Dialogflow Chatbot
Learn the basics of natural language processing: the components of NLP (entities, relations, concepts, semantic roles…), enterprise applications of NLP, and ...
Data Science Dojo
Language Acquisition and Universal Grammar
How do babies get so good at language so quickly? Because they already know a lot from the beginning about how language works. In this week's episode of ...
The Ling Space
Deep Learning 8: Unsupervised learning and generative models
Shakir Mohamed, Research Scientist, discusses unsupervised learning and generative models as part of the Advanced Deep Learning & Reinforcement ...
DeepMind
John Salvatier: Bayesian inference with PyMC 3
PyData Seattle 2015 PyMC 3 (https://github.com/pymc-devs/pymc3), a total rewrite of PyMC 2, provides a powerful yet easy-to-use language for specifying ...
PyData
Lecture 9: Machine Translation and Advanced Recurrent LSTMs and GRUs
Lecture 9 recaps the most important concepts and equations covered so far followed by machine translation and fancy RNN models tackling MT. Key phrases: ...
Stanford University School of Engineering
Intro to Probability - The Science of Uncertainty | MITx on edX | About Video
Introduction to Probability - The Science of Uncertainty An introduction to probabilistic models, including random processes and the basic elements of statistical ...
edX
Deep Learning for Speech Recognition (Adam Coates, Baidu)
The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to ...
Lex Fridman
Сергей Ключников: как работает психосинтез? (NEW!)
Психосинтез - один из классических методов психотерапии, автором которого является Роберто Ассаджиоли....
Андрей Ермошин
Dynamic Social Network Analysis: Model, Algorithm, Theory, & Application CMU Research Speaker Series
Across the sciences, a fundamental setting for representing and interpreting information about entities, the structure and organization of communities, and ...
Microsoft Research
Sabine Hossenfelder - Why the ‘Unreasonable Effectiveness’ of Mathematics?
What is it about #mathematics that it can describe so accurately the world around us? From quantum physics, the very smallest features and forces of the ...
Closer To Truth
Deep Learning with Tensorflow - Applying Recurrent Networks to Language Modelling
Enroll in the course for free at: https://bigdatauniversity.com/courses/deep-learning-tensorflow/ Deep Learning with TensorFlow Introduction The majority of data ...
Cognitive Class
From Deep Learning of Disentangled Representations to Higher-level Cognition
One of the main challenges for AI remains unsupervised learning, at which humans are much better than machines, and which we link to another challenge: ...
Microsoft Research
JuliaCon 2019 | The Unreasonable Effectiveness of Multiple Dispatch | Stefan Karpinski
If you're familiar with Julia and its ecosystem, you may have noticed something lovely but a bit puzzling: there seems to be an unusually large amount of code ...
The Julia Programming Language
What's Strong Emergence? | Episode 1905 | Closer To Truth
What is Strong Emergence? Here's the claim: each level of the scientific hierarchy — physics, chemistry, biology, psychology — has its own special laws that can ...
Closer To Truth
Temporal Action Detection Using a Statistical Language Model
This video is about Temporal Action Detection Using a Statistical Language Model.
ComputerVisionFoundation Videos
Intuition behind Latent Dirichlet Allocation (LDA) for Topic Modeling
LDA Topic Models is a powerful tool for extracting meaning from text. In this video I talk about the idea behind the LDA itself, why does it work. If you do have any ...
Bhavesh Bhatt
Lecture 14: Tree Recursive Neural Networks and Constituency Parsing
Lecture 14 looks at compositionality and recursion followed by structure prediction with simple Tree RNN: Parsing. Research highlight ""Deep Reinforcement ...
Stanford University School of Engineering
MIA: Dustin Tran and Chris Suter, What might machine learners learn from probabilistic programming?
Models, Inference and Algorithms Broad Institute of MIT and Harvard October 3, 2018 MIA Meeting: ...
Broad Institute
Brain Mapping for Glioma Surgery - Rich Everson, MD | UCLA Neurosurgery
Dr. Rich Everson presents Brain Mapping for Glioma Surgery at the UCLA Neurosurgery Update 2019 on March 1-2, 2019. UCLA Neurosurgery at the David ...
UCLA Health
How Healthcare Can Become Higher in Quality, Lower in Cost & Widely Accessible - Clay Christensen
Clay Christensen at the second Faculty Perspectives on Healthcare event. February 8, 2012.
Harvard Business School
Mixture Models 5: how many Gaussians?
Full lecture: http://bit.ly/EM-alg How many components should we use in our mixture model? We can cross-validate to optimise the likelihood (or some other ...
Victor Lavrenko
Quantum Wave Functions: What's Actually Waving?
The most mysterious aspect of quantum mechanics is the wave function. What does it have to do with probability and statistics? Let's find out. Also, check out ...
The Science Asylum
MLJ - Machine Learning for Julia
MLJ, an open-source machine learning toolbox written in Julia, has evolved from an early proof of concept, to a functioning well-featured prototype. Features ...
The Julia Programming Language
Fractional Calculus: A New Language for Explaining Complex Crowd Behavior
Read the article: http://dx.doi.org/10.1109/JAS.2016.7508801 Cao et al. "A Fractional Micro-Macro Model for Crowds of Pedestrians Based on Fractional Mean ...
Research Square
Pandera: Statistical Data Validation of Pandas Dataframes |SciPy 2020| Niels Bantilan
Pandas is an essential tool in the data scientist's toolkit for modern data engineering, analysis, and modeling in the Python ecosystem. However, dataframes can ...
Enthought
Natural language processing - n gram model - bi gram example using counts from a table
Natural language processing - n gram model - bi gram example using counts from a table.
Online Courses
23. Model Merging, Cross-Modal Coupling, Course Summary
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston This lecture begins with a brief ...
MIT OpenCourseWare
Relationship Extraction from Unstructured Text Based on Stanford NLP with Spark
Spark Summit
Neural Network Language Model, Bing Liu, Yingrui Zhang
Introduction to Machine Learning 10-701 CMU 2015 Project presentation.
Alex Smola
Timothy Hopper: Understanding Probabilistic Topic Models By Simulation
PyData NYC 2015 Latent Dirichlet Allocation and related topic models are often presented in the form of complicated equations and confusing diagrams.
PyData
