Google's Deep Dream Applied to a white 512X512 for 240 frames
From playlist Odds and Ends
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
Can You Guess What This Is? | Deep Look
We hope you enjoyed this behind the scenes look from our new episode about mussel beards! Watch it here 👉 https://youtu.be/4vWtkzwFnS0 #deeplook #shorts #behindthescenes #mussels
From playlist Deep Look #Shorts
We’ve hit 2M Subs! 🎉 | Deep Look
A HUGE thanks to all of our fans for subscribing to our channel and watching our videos! 🥳 🎉 #shorts #deeplook
From playlist Deep Look #Shorts
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
29th Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series Talk
Date: Wednesday, June 23, 2021, 10:00am Eastern Time Zone (US & Canada) Speaker: Paul Hand Title: Signal Recovery with Generative Priors Abstract: Recovering images from very few measurements is an important task in imaging problems. Doing so requires assuming a model of what makes some
From playlist Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series
Inverse Problems under a Learned Generative Prior (Lecture 1) by Paul Hand
DISCUSSION MEETING THE THEORETICAL BASIS OF MACHINE LEARNING (ML) ORGANIZERS: Chiranjib Bhattacharya, Sunita Sarawagi, Ravi Sundaram and SVN Vishwanathan DATE : 27 December 2018 to 29 December 2018 VENUE : Ramanujan Lecture Hall, ICTS, Bangalore ML (Machine Learning) has enjoyed tr
From playlist The Theoretical Basis of Machine Learning 2018 (ML)
Paul Hand - Signal Recovery with Generative Priors - IPAM at UCLA
Recorded 29 November 2022. Paul Hand of Northeastern University presents "Signal Recovery with Generative Priors" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: Recovering images from very few measurements is an important task in imaging problems. Doing s
From playlist 2022 Multi-Modal Imaging with Deep Learning and Modeling
Chris Metzler - Adversarial Sensing: Learning-Based Approach to Imaging & Sensing w/ Unknown Models
Recorded 11 October 2022. Chris Metzler of the University of Maryland presents "Adversarial Sensing: A Learning-Based Approach to Imaging and Sensing with Unknown Forward Models" at IPAM's Diffractive Imaging with Phase Retrieval Workshop. Abstract: Adversarial sensing is a self-supervised
From playlist 2022 Diffractive Imaging with Phase Retrieval - - Computational Microscopy
Deep Decoder: Concise Image Representations from Untrained Networks (Lecture 2) by Paul Hand
DISCUSSION MEETING THE THEORETICAL BASIS OF MACHINE LEARNING (ML) ORGANIZERS: Chiranjib Bhattacharya, Sunita Sarawagi, Ravi Sundaram and SVN Vishwanathan DATE : 27 December 2018 to 29 December 2018 VENUE : Ramanujan Lecture Hall, ICTS, Bangalore ML (Machine Learning) has enjoyed tr
From playlist The Theoretical Basis of Machine Learning 2018 (ML)
Ruslan Salakhutdinov: "Advanced Hierarchical Models"
Graduate Summer School 2012: Deep Learning, Feature Learning "Advanced Hierarchical Models" Ruslan Salakhutdinov Institute for Pure and Applied Mathematics, UCLA July 24, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-fe
From playlist GSS2012: Deep Learning, Feature Learning
Ulugbek Kamilov: "Computational Imaging: Reconciling Models and Learning"
Deep Learning and Medical Applications 2020 "Computational Imaging: Reconciling Models and Learning" Ulugbek Kamilov, Washington University in St. Louis Abstract: There is a growing need in biological, medical, and materials imaging research to recover information lost during data acquis
From playlist Deep Learning and Medical Applications 2020
Daniel Cremers - Deep Learning: Challenges and Perspectives - IPAM at UCLA
Recorded 29 November 2022. Daniel Cremers of Technische Universtitat München presents "Deep Learning: Challenges and Perspectives" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: Since 2012 deep networks have revolutionized computer vision and many other a
From playlist 2022 Multi-Modal Imaging with Deep Learning and Modeling
Michael Unser - High-Speed Fourier Ptychography with Deep Spatio-Temporal Priors - IPAM at UCLA
Recorded 11 October 2022. Michael Unser of the École Polytechnique Fédérale de Lausanne (EPFL) Biomedical Imaging Group presents "High-Speed Fourier Ptychography with Deep Spatio-Temporal Priors" at IPAM's Diffractive Imaging with Phase Retrieval Workshop. Abstract: Fourier ptychography (F
From playlist 2022 Diffractive Imaging with Phase Retrieval - - Computational Microscopy
In any photo you take, you'll want your subject to be in focus. However, sometimes you may want the background to be out of focus to give a soft, artistic appearance to the photo. This is known as shallow depth of field. We hope you enjoy! To learn more, check out our written lesson here:
From playlist Digital Photography
Ulugbek Kamilov: Signal processing for nonlinear diffractive imaging
Abstract: Can modern signal processing be used to overcome the diffraction limit? The classical diffraction limit states that the resolution of a linear imaging system is fundamentally limited by one half of the wavelength of light. This implies that conventional light microscopes cannot d
From playlist Probability and Statistics