Stateflow Overview (Previous Version: R2013a )
Design and simulate state charts using Stateflow. For an updated version of this video, visit: https://youtu.be/TuL8cFqDu6A
From playlist Event-Based Modeling
Model-Based Design for Predictive Maintenance, Part 5: Development of a Predictive Model
See the full playlist: https://www.youtube.com/playlist?list=PLn8PRpmsu08qe_LVgUHtDrSXiNz6XFcS0 After performing real-time tests and validating your algorithm, you can use it to detect whether there are any mechanical or electrical issues in your system. However, you can also use condition
From playlist Model-Based Design for Predictive Maintenance
Introduction to Random Signal Representation
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Introduction to the concept of a random signal, then review of probability density functions, mean, and variance for scalar quantities.
From playlist Random Signal Characterization
From playlist COMP0168 (2020/21)
Predictive Maintenance with MATLAB and Simulink
Companies that make industrial equipment are storing large amounts of machine data, with the notion that they will be able to extract value from it in the future. However, using this data to build accurate and robust models for prediction requires a rare combination of equipment, expertise
From playlist Predictive maintenance
Random Processes and Stationarity
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Introduction to describing random processes using first and second moments (mean and autocorrelation/autocovariance). Definition of a stationa
From playlist Random Signal Characterization
Model-Based Design for Predictive Maintenance, Part 6: Deployment of a Predictive Model
See the full playlist: https://www.youtube.com/playlist?list=PLn8PRpmsu08qe_LVgUHtDrSXiNz6XFcS0 This video shows how prognostics models work, how they perform, and how you can deploy them. You’ll learn how to deploy a remaining useful life estimation model either as a standalone applicatio
From playlist Model-Based Design for Predictive Maintenance
DeepMind x UCL RL Lecture Series - Deep Reinforcement Learning #2 [13/13]
Research Engineer Matteo Hessel covers general value functions, GVFs as auxiliary tasks, and explains how to deal with scaling issues in algorithms. Slides: https://dpmd.ai/deeprl2 Full video lecture series: https://dpmd.ai/DeepMindxUCL21
From playlist Learning resources
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 8 - Model-Based Reinforcement Learning
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Assistant Professor Chelsea Finn, Stanford University http://cs330.stanford.edu/
From playlist Stanford CS330: Deep Multi-Task and Meta Learning
JEPA - A Path Towards Autonomous Machine Intelligence (Paper Explained)
#jepa #ai #machinelearning Yann LeCun's position paper on a path towards machine intelligence combines Self-Supervised Learning, Energy-Based Models, and hierarchical predictive embedding models to arrive at a system that can teach itself to learn useful abstractions at multiple levels a
From playlist Papers Explained
Using Deep Reinforcement Learning to Uncover the Decision-Making Mechanisms - L. Cross - 10/25/2019
"Using Deep Reinforcement Learning to Uncover the Decision-Making Mechanisms of the Brain." AI-4-Science Workshop, October 25, 2019 at Bechtel Residence Dining Hall, Caltech. Learn more about: - AI-4-science: https://www.ist.caltech.edu/ai4science/ - Events: https://www.ist.caltech.edu/
From playlist AI-4-Science Workshop
End-to-End Differentiable Proving: Tim Rocktäschel, University of Oxford
We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specific
From playlist Logic and learning workshop
A Deep Dive into NLP with PyTorch
In this tutorial, we will give you some deeper insights into recent developments in the field of Deep Learning NLP. The first part of the workshop will be an introduction into the dynamic deep learning library PyTorch. We will explain the key steps for building a basic model. In the second
From playlist Machine Learning
The video explains MuZero! MuZero makes AlphaZero more general by constructing representation and dynamics models such that it can play games without a perfect model of the environment. This dynamics function is unique because of the way it's hidden state is tied into the policy and value
From playlist Game Playing AI: From AlphaGo to MuZero
Speakers | Stanford CS224U Natural Language Understanding | Spring 2021
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai To learn more about this course visit: https://online.stanford.edu/courses/cs224u-natural-language-understanding To follow along with the course schedule and s
From playlist Stanford CS224U: Natural Language Understanding | Spring 2021
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 13 – Contextual Word Embeddings
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/30j472S Professor Christopher Manning, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Lear
From playlist Stanford CS224N: Natural Language Processing with Deep Learning Course | Winter 2019
Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Christopher Potts & Consulting Assistant Professor Bill MacCartney, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Potts Pr
From playlist Stanford CS224U: Natural Language Understanding | Spring 2019
Model-Based Design for Predictive Maintenance, Part 2: Feature Extraction
See the full playlist: https://www.youtube.com/playlist?list=PLn8PRpmsu08qe_LVgUHtDrSXiNz6XFcS0 Learn how to extract useful condition indicators of your system. Condition indicators are important, as they can help you build both a classification model and a prognostic model. This video wal
From playlist Model-Based Design for Predictive Maintenance