What Makes P vs. NP So Hard? (P ≠ EXPTIME, Time Hierarchy, Baker-Gill-Solovay)
There are a lot of unsolved problems in complexity theory, but there are a few things we do know. We look at the Time Hierarchy Theorem, and also why the proof techniques don't transfer to P vs NP. Created by: Cory Chang Produced by: Vivian Liu Script Editor: Justin Chen, Zachary Greenber
From playlist P vs NP
Turing Centennial Conference: Turing, Church, Gödel, Computability, Complexity and Randomization
Turing, Church, Gödel, Computability, Complexity and Randomization Presented by Prof. Michael Rabin, Turing Award laureate, Hebrew University & Harvard University Alan M. Turing Centennial Conference - Israel April 4, 2012 The Wohl Centre Bar-Ilan University Ramat-Gan, Israel For more in
From playlist Alan M. Turing Centennial Conference - Israel
A viral film we created for Lenovo featuring futuristic technology from their R&D labs.
From playlist Lenovo: For Those Who Do.
Russian Doll Head illusion makeup
Follow me on Instagram: SammyLovesFossas
From playlist Interesting Videos
MATLAB and Simulink Student Design Challenge
This is our project based on the image processing.
From playlist MATLAB and Simulink Student Challenge 2013 Entries
From playlist Simulink Design Award: 2013 BEST Robotics
Marble Machine Spoon Elevator 3D Model
Just for fun. It was based on several videos from Youtube about marble machines, and some of my own ideas. Modeled with Solidworks 2015. Rendered with Simlab Composer 6 Mechanical Edition. Gifs made with Photoscape. Renders edited with IrfanView.
From playlist Marble Machines
Nikolaus Kriegskorte - Controversial stimuli: experiments to adjudicate computational hypotheses
Recorded 13 January 2023. Nikolaus Kriegeskorte of Columbia University presents "Controversial stimuli: Optimizing experiments to adjudicate among computational hypotheses" at IPAM's Explainable AI for the Sciences: Towards Novel Insights Workshop. Learn more online at: http://www.ipam.ucl
From playlist 2023 Explainable AI for the Sciences: Towards Novel Insights
Using & Expanding the NLP Models Hub 1 | Webinar
Spark NLP and Spark OCR Free Trials are available here: https://www.johnsnowlabs.com/spark-nlp-try-free/ The NLP Models Hub which powers the Spark NLP and NLU libraries takes a different approach than the hubs of other libraries like TensorFlow, PyTorch, and Hugging Face. While it also pr
From playlist AI & NLP Webinars
Learn about HEV modeling and simulation. In this video, you will: - Learn about different methods for creating HEV component models. - See how Powertrain Blockset™ and Simscape™ tools can be used for HEV modeling. - Learn best practices for getting started and creating new plant models.
From playlist Hybrid Electric Vehicles
DDPS | Learning hierarchies of reduced-dimension and context-aware models for Monte Carlo sampling
In this DDPS Seminar Series talk from Sept. 2, 2021, University of Texas at Austin postdoctoral fellow Ionut-Gabriel Farcas discusses hierarchies of reduced-dimension and context-aware low-fidelity models for multi-fidelity Monte Carlo sampling. Description: In traditional model reduction
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
This is Lecture 14 of the CSE519 (Data Science) course taught by Professor Steven Skiena [http://www.cs.stonybrook.edu/~skiena/] at Stony Brook University in 2016. The lecture slides are available at: http://www.cs.stonybrook.edu/~skiena/519 More information may be found here: http://www
From playlist CSE519 - Data Science Fall 2016
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 15
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai To follow along with the course, visit: http://cs330.stanford.edu/fall2021/index.html To view all online courses and programs offered by Stanford, visit: http:/
From playlist Stanford CS330: Deep Multi-Task & Meta Learning I Autumn 2021I Professor Chelsea Finn
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. After introducing this framework along with the concept of inference, which is central to it, Andriy focuses on two types of modern latent
From playlist Learning resources