Computational Semantics: How Computers Know what Words Mean [Lecture]
This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check out the whole course: https://boydgraber.org/teaching/CMSC_723/ (Including homeworks and reading.) Music: https://soundcloud.com/alvin-grissom-ii/review
From playlist Computational Linguistics I
The role of the hard drive buffer and interrupts when a file is transferred from primary memory (RAM) to a secondary storage device.
From playlist Computer Hardware and Architecture
Intro To Linux Memory Management
This is an introduction to Linux memory management. It covers the basics of paging and memory allocation. Understanding basic hardware memory management and the difference between virtual, physical and swap memory. How do determine what memory is installed and determine how processes use t
From playlist Linux
Logic: The Structure of Reason
As a tool for characterizing rational thought, logic cuts across many philosophical disciplines and lies at the core of mathematics and computer science. Drawing on Aristotle’s Organon, Russell’s Principia Mathematica, and other central works, this program tracks the evolution of logic, be
From playlist Logic & Philosophy of Mathematics
Machine learning describes computer systems that are able to automatically perform tasks based on data. A machine learning system takes data as input and produces an approach or solution to a task as output, without the need for human intervention. Machine learning is closely tied to th
From playlist Data Science Dictionary
Optimal transport for machine learning - Gabriel Peyre, Ecole Normale Superieure
This workshop - organised under the auspices of the Isaac Newton Institute on “Approximation, sampling and compression in data science” — brings together leading researchers in the general fields of mathematics, statistics, computer science and engineering. About the event The workshop ai
From playlist Mathematics of data: Structured representations for sensing, approximation and learning
Algorithms Explained: Memory Complexity
An overview of memory (space) complexity including the basics of big O notation and common space complexities with examples of each. Understanding memory complexity is vital to understanding algorithms and why certain constructions or implementations are better than others. Even if you do
From playlist Algorithms Explained
Vintage 1962 "Digital Computer Techniques" - core memory, magnetic storage, etc.
Original un-edited 1962 film. A “somewhat dry” Army/Navy film of basic computer concepts. Detailed descriptions & diagrams of computing “input, store, control, arithmetic, output”, etc. Machine peripherals shown briefly. Film quality starts poor, but gets better towards the end. Nice d
From playlist Computers of the 1960's
Peter Sewell: Underpinning mainstream engineering with mathematical semantics
LMS/BCS-FACS Evening Seminar November 2021
From playlist LMS/BCS-FACS Evening Seminars
Lec 5 | MIT 6.033 Computer System Engineering, Spring 2005
Fault Isolation with Clients and Servers View the complete course at: http://ocw.mit.edu/6-033S05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.033 Computer System Engineering, Spring 2005
MIT 6.0001 Introduction to Computer Science and Programming in Python, Fall 2016 Instructor: Dr. Ana Bell View the complete course: https://ocw.mit.edu/6-0001F16 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63WbdFxL8giv4yhgdMGaZNA In this lecture, Dr. Bell introduces
From playlist 6.0001 Introduction to Computer Science and Programming in Python. Fall 2016
Lecture 15D : Semantic hashing
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 15D : Semantic hashing
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
ICCV19: Oral Session 4.2A - Segmentation, Detection, 3D Scene Understanding
Link to indexed video: https://conftube.com/video/2ntDYowHbZs 1. YOLACT: Real-Time Instance Segmentation Daniel Bolya, Chong Zhou, Fanyi Xiao, Yong Jae Lee https://conftube.com/video/2ntDYowHbZs?tocitem=2 2. Expectation-Maximization Attention Networks for Semantic Segmentation Xia Li, Z
From playlist AI Research
Deep Learning - Visual Recognition | by Konstantin Lopuhin | Kaggle Days Warsaw
"Deep Learning - Visual Recognition" Konstantin Lopuhin Kaggle Days Warsaw was held May 2018, and gathered over 100 participants to meet, learn and code with Kaggle Grandmasters, and compete in our traditional offline competition. Kaggle Days are a global series of offline events for se
From playlist Kaggle Days Warsaw Edition | by LogicAI + Kaggle
EVE - Explainable Vector Embeddings - DRT S2E12
00:53 Intro to the topic of explainable vector embeddings 2:30 GDPR as the initial motivation to work on explainable embeddings 3:26 How do you introduce semantics into decision making 4:40 How can strcutured knowledge (eg. taxonomies) interact with free text in analysis 7:00 Right to expl
From playlist Deep Random Talks- Season 2
Lecture 15.4 — Semantic Hashing [Neural Networks for Machine Learning]
Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-2012-001
From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton
Lecture 15/16 : Modeling hierarchical structure with neural nets
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 15A From Principal Components Analysis to Autoencoders 15B Deep Autoencoders 15C Deep autoencoders for document retrieval and visualization 15D Semantic hashing 15E Learning binary codes for image retrieval 15F Shallo
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
