Deep learning is a machine learning technique that learns features and tasks directly from data. This data can include images, text, or sound. The video uses an example image recognition problem to illustrate how deep learning algorithms learn to classify input images into appropriate ca
From playlist Introduction to Deep Learning
Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)
Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Fac
From playlist Deep Learning SIMPLIFIED
Ellie Pavlick: "Should we care about linguistics?"
New Deep Learning Techniques 2018 "Should we care about linguistics?" Ellie Pavlick, University of Pennsylvania Abstract: There are countless examples of how deep learning has shattered previously state-of-the-art results on language processing tasks, including machine translation, quest
From playlist New Deep Learning Techniques 2018
Stanford Seminar - Recent Advances in Deep Learning
"Recent advances in deep learning" - Oriol Vinyals of Google About the talk: Deep learning has been a popular research area due to major successes in perception tasks such as speech recognition and object classification. In the first part of my talk, I will give a brief overview of the ma
From playlist Engineering
This video is part of the Udacity course "Deep Learning". Watch the full course at https://www.udacity.com/course/ud730
From playlist Deep Learning | Udacity
Solving Problems - Big and Small
This video is part of the Udacity course "Deep Learning". Watch the full course at https://www.udacity.com/course/ud730
From playlist Deep Learning | Udacity
Deep Learning with R for Beginners
Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. #Deep_learning architectures such as deep neural ne
From playlist Deep Learning
Deep Learning Course Purdue University Fall 2016 https://docs.google.com/document/d/1_p4Y_9Y79uBiMB8ENvJ0Uy8JGqhMQILIFrLrAgBXw60
From playlist Deep-Learning-Course
Lecture 1 | Natural Language Processing with Deep Learning
Lecture 1 introduces the concept of Natural Language Processing (NLP) and the problems NLP faces today. The concept of representing words as numeric vectors is then introduced, and popular approaches to designing word vectors are discussed. Key phrases: Natural Language Processing. Word
From playlist Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017)
Evaluating Performance of Large Language Models with Linguistics - Deep Random Talks S2E5
Listen to Amir Feizpour, Serena McDonnell and Bai Li speak about Natural Language Processing on DRT's this episode.
From playlist Deep Random Talks- Season 2
Noam Chomsky on Theories of Linguistics (Part 2) | Closer To Truth Chats
Closer To Truth has just launched a new website! We can't wait for you to see what we've been working on. New seasons (including the web debut of Season 21), interviews, topic guides, curated playlists, candid conversations, book excerpts, essays, and announcements. Visit today: www.Closer
From playlist Closer To Truth Chats
Introduction to Computational Linguistics
http://users.umiacs.umd.edu/~jbg/teaching/CMSC_723/
From playlist Computational Linguistics I
An overview of the field of Natural Language Processing (NLP), including key areas, commonly used tools and Python libraries, debate within the field as more ML methods are adapted, and ethical issues. Jupyter notebook: https://github.com/fastai/course-nlp/blob/master/1-what-is-nlp.ipynb
From playlist fast.ai Code-First Intro to Natural Language Processing
Emergent linguistic structure in deep contextual neural word representations - Chris Manning
Workshop on Theory of Deep Learning: Where next? Topic: Emergent linguistic structure in deep contextual neural word representations Speaker: Chris Manning Affiliation: Stanford University Date: October 15, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Automatically Generating Different Re-Tellings of Stories
From the Interactive Media & Games Seminar Series; Marilyn Walker, Professor of Computer Science and head of the Natural Language and Dialogue Systems Lab at the University of California, Santa Cruz, discusses her work on storytelling agents who retell existing stories, such as personal n
From playlist Interactive Media & Games Seminars WINTER 2016
Morphology: Crash Course Linguistics #2
What even is a word? Well… linguists don’t really know! But that’s ok! When linguists want to get super specific, we don’t even talk about words, we talk about morphemes instead. In this episode of Crash Course Linguistics, we’re diving into the topic of morphology and what makes up these
From playlist Linguistics
Noam Chomsky on the Big Questions (Part 4) | Closer To Truth Chats
Closer To Truth is proud to present this four-part miniseries with distinguished theoretical linguist, analytic philosopher, and cognitive scientist Noam Chomsky. In our final part, Chomsky offers his thoughts and opinions on Closer To Truth's "big questions": Does God exist? Why is there
From playlist Closer To Truth Chats
Lecture 18: Tackling the Limits of Deep Learning for NLP
Lecture 18 looks at tackling the limits of deep learning for NLP followed by a few presentations. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural languag
From playlist Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017)