Matti Lyra - Evaluating Topic Models
Description Unsupervised models in natural language processing (NLP) have become very popular recently. Word2vec, GloVe and LDA provide powerful ...
PyData
DeepMind x UCL | Deep Learning Lectures | 10/12 | Unsupervised Representation Learning
Unsupervised learning is one of the three major branches of machine learning (along with supervised learning and reinforcement learning). It is also arguably ...
DeepMind
Automatic Speech Recognition - An Overview
An overview of how Automatic Speech Recognition systems work and some of the challenges. See more on this video at ...
Microsoft Research
Beyond word2vec: GloVe, fastText, StarSpace - Konstantinos Perifanos
PyData London 2018 Word embeddings is a very convenient and efficient way to extract semantic information from large collections of textual or textual-like data ...
PyData
21. Probabilistic Inference I
Please note: Lecture 20, which focuses on the AI business, is not available. MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ...
MIT OpenCourseWare
N-Gram Language Model, Exercises using, Bi-Gram, Tri-gram & Four-gram, Natural Language Processing
This video explains N-gram model, Chain rule, Formulation, Derivation for various N-grams. Exercises/ Problems Solved using various models like Birgram, ...
Varsha's engineering stuff
Language Model Evaluation and Perplexity
Course Link: https://www.coursera.org/lecture/probabilistic-models-in-nlp/language-model-evaluation-SEO4T Transcript: In this video I'll show you how to ...
Machine Learning TV
Сергей Ключников: как работает психосинтез? (NEW!)
Психосинтез - один из классических методов психотерапии, автором которого является Роберто Ассаджиоли. Сергей Ключников, лидер этого ...
Андрей Ермошин
David Poeppel - What Language Processing in the Brain Tells Us About the Structure of the Mind
Session 1: NEURAL AND COGNITIVE BASES OF LEARNING What Language Processing in the Brain Tells Us About the Structure of the Mind Presented by ...
Johns Hopkins University
Steven Piantadosi
One Model for Learning of Language Português: https://www.youtube.com/watch?v=FckFxjzJPsA ___ Abralin ao Vivo - Linguists Online is an initiative of Abralin ...
Abralin
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 3 - Optimization-Based Meta-Learning
Assistant Professor Chelsea Finn, Stanford University http://cs330.stanford.edu/ To get the latest news on Stanford's upcoming professional programs in Artificial ...
stanfordonline
"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
Implementing and Training Predictive Customer Lifetime Value Models in Python
Implementing and Training Predictive Customer Lifetime Value Models in Python by Jean-Rene Gauthier, Ben Van Dyke Customer lifetime value models (CLVs) ...
PyData
MIA: Alp Kucukelbir, Automated inference & probabilistic programming; Rajesh Ranganath, PGMs
Models, Inference and Algorithms Broad Institute of MIT and Harvard September 21, 2016 Alp Kucukelbir Columbia CS MIA Meeting: ...
Broad Institute
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
TensorFlow Probability (TensorFlow @ O’Reilly AI Conference, San Francisco '18)
Tensorflow Probability (TFP) is a TF/Python library offering a modern take on both emerging & traditional probability/statistical tools. Statisticians/data scientists ...
TensorFlow
Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python
This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines. - Natural ...
Alice Zhao
Deep Learning New Frontiers | MIT 6.S191
MIT Introduction to Deep Learning 6.S191: Lecture 6 *New 2020 Edition* Deep Learning Limitations and New Frontiers Lecturer: Ava Soleimany January 2020 ...
Alexander Amini
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
Lecture 2 – Word Vectors 1 | Stanford CS224U: Natural Language Understanding | Spring 2019
Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. Learn more at: https://stanford.io/2rf9OO3 Professor ...
stanfordonline
DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Models
This lecture, by DeepMind Research Scientist Andriy Mnih, explores latent variable models, a powerful and flexible framework for generative modelling.
DeepMind
Tutorial: Probabilistic Programming
Probabilistic programming is a general-purpose means of expressing and automatically performing model-based inference. A key characteristic of many ...
Microsoft Research
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
Lambda World 2018 - Introduction to the Unison programming language - Rúnar Bjarnason
This presentation by Rúnar Bjarnason took place at Lambda World Seattle on September 18th, 2018 at the Living Computers Museum in Washington.
Lambda World
Self-Supervised Learning
PAISS 2019 Yann LeCun New York University Facebook AI Research http://yann.lecun.com.
InriaChannel
Turing Machines Explained - Computerphile
Turing Machines are the basis of modern computing, but what actually is a Turing Machine? Assistant Professor Mark Jago explains. Turing & The Halting ...
Computerphile
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
LM.2 What is a language model?
Victor Lavrenko
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
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
Probabilistic Graphical Models in Python
Aileen Nielsen https://2016.pygotham.org/talks/368/probabilistic-graphical-models-in-python This talk will give a high level overview of the theories of graphical ...
Next Day Video
11 การประยุกต์ Language Model ในรูปแบบอื่นๆ
Language Model สรุปรวบบริบทในแบบต่างๆ ของคำในภาษา จึงเป็นโมเดลที่สามารถเอาไปใช้ใน Applications ที่ต้องการใช้ข้อมูลเกี่ยวกับบริบทในการใช้คำในแต่ละคำ คลิปดูกันว่า ...
ภาษาศาสตร์คอมพิวเตอร์ Thai NLP
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
Ilya Sutskever: OpenAI Meta-Learning and Self-Play | MIT Artificial General Intelligence (AGI)
This is a talk by Ilya Sutskever for course 6.S099: Artificial General Intelligence. He is the Co-Founder of OpenAI. This class is free and open to everyone.
Lex Fridman
Can a Chess Piece Explain Markov Chains? | Infinite Series
Viewers like you help make PBS (Thank you ) . Support your local PBS Member Station here: https://to.pbs.org/donateinfi In this episode probability ...
PBS Infinite Series
Relevance model 6: cross-language estimation
http://bit.ly/RModel] Can we "translate" an English query into a Chinese relevance model? Yes, we just need to have access to a parallel corpus.
Victor Lavrenko
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
JuliaCon 2020 | Interactive data dashboards with Julia and Stipple | Adrian Salceanu
Abstract: Learn how to build interactive, web based data dashboards in pure Julia with the Stipple Reactive UI library. The participants will learn the ...
The Julia Programming Language
JuliaCon 2020 | Rocket.jl: A Julia package for reactive programming | Dmitry Bagaev
Rocket.jl is a native Julia implementation of reactive programming concepts. The package uses the observable sequence and actor model to make it easier to ...
The Julia Programming Language
Introduction to Bayesian Data Analysis and Stan with Andrew Gelman
Stan is a free and open-source probabilistic programming language and Bayesian inference engine. In this talk, we will demonstrate the use of Stan for some ...
Generable
Deep Learning and Language Model - Part-2
This tutorial Explains the Encoder-Decoder RNN and the Language Model with Encoder-Decoder RNN. References used: 1. Cho, Kyunghyun, Aaron Courville, ...
Dr. Niraj R Kumar
Lecture 22 — Smoothing of Language Model -- Part 1 | UIUC
Artificial Intelligence - All in One
