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
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
4 1 Introduction to N grams
Mausam Jain
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
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
Lecture 22 — Smoothing of Language Model -- Part 1 | UIUC
Artificial Intelligence - All in One
Lecture 13 | Generative Models
In Lecture 13 we move beyond supervised learning, and discuss generative modeling as a form of unsupervised learning. We cover the autoregressive ...
Stanford University School of Engineering
Lecture 17 — The Vector Space Model - Natural Language Processing | Michigan
Artificial Intelligence - All in One
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
mod08lec65-Noisy Channel Model, Bayes Rule, Language Model
NPTEL-NOC IITM
Andrew Rowan - Bayesian Deep Learning with Edward (and a trick using Dropout)
Filmed at PyData London 2017 Description Bayesian neural networks have seen a resurgence of interest as a way of generating model uncertainty estimates.
PyData
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
[Language Modeling - NLP] 2 N-gram Language Model
คลิปสำหรับวิชา Computational Linguistics คณะอักษรศาสตร์ จุฬาลงกรณ์มหาวิทยาลัย ปี 2561 โดย...
ภาษาศาสตร์คอมพิวเตอร์ Thai NLP
"Finding bugs without running or even looking at code" by Jay Parlar
What if you could find complex bugs in systems without ever having looked at any of the code, without running the code, without cloning the code, or even ...
Strange Loop
LM.2 What is a language model?
Victor Lavrenko
Hidden Markov Model ( HMMs) in Hindi | Machine Leaning Tutorials
HMMs #Machinelearning #LMT #lastmomenttuitions Machine Learning Full course :- https://bit.ly/2Xp4dmH Engineering Mathematics 03 (VIdeos + Handmade ...
Last moment tuitions
MIA: Debora Marks, Alignment-free models for protein and antibody design; Aaron Kollasch
May 22, 2019 MIA Meeting: https://youtu.be/GUt9NQcll5c?t=2743&list=PLlMMtlgw6qNjROoMNTBQjAcdx53kV50cS&index=96&t=0s Aaron Kollasch Marks Lab ...
Broad Institute
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
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
RNN LM #1/4: n-gram Language Model
Moch Arif Bijaksana
Junpeng Lao: A Hitchhiker's Guide to designing a Bayesian library in Python | PyData Córdoba
With modern automatic differentiation libraries like Tensorflow, Jax, autograd, Pytorch, Theano, and more (insert your favorite autograd library here), writing a ...
PyData
PyData Tel Aviv Meetup: Probabilistic Cross Device Matching - Netta Shachar
Bridging the cross-device gap is probably the biggest challenge the ad-tech industry is facing today. Users spend more than 50% of their time on mobile devices, ...
PyData
Mod-05 Lec-01 Introduction to Model Checking
Design Verification and Test of Digital VLSI Circuits by Prof. Jatindra Kumar Deka, Dr. Santosh Biswas, Department of Computer Science and Engineering, ...
nptelhrd
From LSI to Probabilistic Topic Models: An introduction to Topic Models
Topic models attempt to discover themes, or Topics, from large collection of documents. Discovering themes from a document corpus is an important problem ...
Microsoft Research
Bayesian Deep Learning and Black Box Variational Inference
Scientists and scholars across many fields seek to answer questions in their respective disciplines using large data sets. One approach to answering such ...
Microsoft Research
LM.13 Language model ranking formula
Victor Lavrenko
Finite State Machine (Finite Automata)
TOC: Finite State Machine (Finite Automata) in Theory of Computation. Topics discussed: 1. The Basics of Finite State Machine. 2. Finite Automata. 3. Types of ...
Neso Academy
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein Deep learning has achieved a remarkable ...
ComputerVisionFoundation Videos
Tutorial 5.1: Tomer Ullman - Church Programming Language 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: Tomer ...
MIT OpenCourseWare
Lecture 6 - Trend Detection In Twitter Social Data (Analyzing Big Data With Twitter)
http://blogs.ischool.berkeley.edu/i290-abdt-s12/ Lecture 6: Kostas Tsioutsiouliklis on Twitter Trends and how to compute them Lecture notes: ...
Berkeley School of Information
Введение в машинное обучение. О курсе.
Машинное обучение преподается в ШАДе с самого его основания, и это не случайно. Сегодня оно используется...
Компьютерные науки
Deep Learning Fundamentals: Forward Model, Differentiable Loss Function & Optimization | SciPy 2019
Does deep learning feel like a mystical topic with a myriad of jargon? If so, then this tutorial is for you. We will dive deeply into the foundational ideas that power ...
Enthought
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
mod03lec26-Bigram and Trigram Language models -peeking indide the model building
NPTEL-NOC IITM
Intro to Language Modeling (NLP video 8)
We will revisit the problem of sentiment classification for movie reviews-- only this time we will use transfer learning and neural networks. We begin by using a ...
Rachel Thomas
Mod-01 Lec-24 IR Models: Boolean Vector
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit ...
nptelhrd
Hidden Markov Models
Virginia Tech Machine Learning Fall 2015.
Bert Huang
Learning Machine Learning with .NET, PyTorch and the ONNX Runtime
ONNX is a open format to represent deep learning models that is supported by various frameworks and tools. This format makes it easier to interoperate ...
Microsoft Developer
Neural Network Language Model, Bing Liu, Yingrui Zhang
Introduction to Machine Learning 10-701 CMU 2015 Project presentation.
Alex Smola
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
Human-Robot Collaboration
In order for robots to collaborate with humans, they must infer helpful actions in the physical world by observing the human's language, gesture, and actions.
Microsoft Research
Christopher Fonnesbeck Probabilistic Programming with PyMC3 PyCon 2017
"Speaker: Christopher Fonnesbeck Bayesian statistics offers robust and flexible methods for data analysis that, because they are based on probability models, ...
PyCon 2017
