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Celebrating 100 episodes of Stanford Engineering’s “The Future of Everything”
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Catalyst for Collaborative Solutions
The initiative is dedicated to fostering university-wide interdisciplinary research that takes on significant global challenges.-
Catalyst: Can we move beyond fitness trackers to motivate the unmotivated to exercise? - Duration: 13 minutes, 9 seconds.
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Catalyst: Are the buildings you live and work in making you feel stressed? - Duration: 12 minutes, 38 seconds.
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Catalyst: The fight against bacterial infections enters the 21st century - Duration: 15 minutes.
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Catalyst: Science finds a better way to measure stress, anxiety and depression - Duration: 14 minutes, 30 seconds.
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Materials Science and Engineering Centennial
2019 marks the 100th anniversary of the Materials Science and Engineering department. This collection of talks was captured at the department's centennial celebration.-
Professor Alberto Salleo: Materials Science at Stanford: The beginning of the next century - Duration: 44 minutes.
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Professor Jennifer Widom: Dean's Welcome, Materials Science and Engineering Centennial Celebration - Duration: 8 minutes, 14 seconds.
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Dr. Craig Barrett, Featured Speaker - Duration: 47 minutes.
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Professor William Nix: History of the department - Duration: 40 minutes.
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Professor Jennifer Dionne: Inside out - Duration: 34 minutes.
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Professor Yi Cui: Nanotechnology for Energy and Environment - Duration: 33 minutes.
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The Future of Everything
A podcast and SiriusXM satellite radio series brought to you by Stanford School of Engineering-
Russ Altman: Artificial intelligence takes on COVID-19 - Duration: 29 minutes.
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Victor Carrion: How to beat stress in a pandemic - Duration: 28 minutes.
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Seema Yasmin: How to conquer a pandemic with communication - Duration: 27 minutes.
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Nigam Shah: A researcher turns to data to fight the COVID-19 virus - Duration: 29 minutes.
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Catherine Blish: Immunology is on the trail of a killer - Duration: 30 minutes.
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William Chueh: How to build a better battery - Duration: 28 minutes.
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John Etchemendy: How can we get the most from artificial intelligence? - Duration: 30 minutes.
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A Q&A with the host of Stanford Engineering's The Future of Everything podcast - Duration: 2 minutes, 59 seconds.
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Alex Dunn: When cells communicate by nudging one another - Duration: 27 minutes.
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Fiorenza Micheli: The race to save the ocean - Duration: 28 minutes.
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Nick Ouellette: What flocks of birds can tell us about engineering - Duration: 28 minutes.
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Shaili Jain: Treatments for PTSD are more effective than ever - Duration: 28 minutes.
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Intersections
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What is the most effective way to bring AI into the classroom? - Duration: 1 hour, 10 minutes.
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How is Silicon Valley changing Wall Street? - Duration: 1 hour, 11 minutes.
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Can we tell truth from fiction in the age of social media? - Duration: 1 hour, 21 minutes.
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The Future of Computational Journalism - Duration: 1 hour, 27 minutes.
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Looking to the past for clues on the future of energy - Duration: 1 hour, 15 minutes.
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Stanford Engineering News
Research and Ideas from the Stanford School of Engineering-
Robots help archaeologists to explore pre-Incan ruins in Peru - Duration: 3 minutes, 17 seconds.
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How can we extract the most energy from the wind? - Duration: 2 minutes, 54 seconds.
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Can we simulate blood flows to improve heart surgery? - Duration: 3 minutes, 27 seconds.
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Meet Jackrabbot: The social robot - Duration: 2 minutes, 46 seconds.
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Zhenan Bao: On A Quest to Develop Artificial Skin - Duration: 97 seconds.
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Stanford students set high-altitude balloon record - Duration: 90 seconds.
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Karl Deisseroth: Bringing CLARITY to brain research - Duration: 3 minutes, 8 seconds.
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Controlling light at the nanoscale - Duration: 2 minutes, 42 seconds.
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Auto-analysis of 3-D data map aids remodeling - Duration: 63 seconds.
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CNT chips could turn smartphones into supercomputers - Duration: 2 minutes, 54 seconds.
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Super-stretchy polymer could lead to artificial muscle - Duration: 29 seconds.
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Sean Follmer: Designing computers that let us think with our hands - Duration: 2 minutes, 15 seconds.
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Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/-
Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition - Duration: 57 minutes.
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Lecture 2 | Image Classification - Duration: 59 minutes.
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Lecture 3 | Loss Functions and Optimization - Duration: 1 hour, 14 minutes.
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Lecture 4 | Introduction to Neural Networks - Duration: 1 hour, 13 minutes.
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Lecture 5 | Convolutional Neural Networks - Duration: 1 hour, 8 minutes.
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Lecture 6 | Training Neural Networks I - Duration: 1 hour, 20 minutes.
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Lecture 7 | Training Neural Networks II - Duration: 1 hour, 15 minutes.
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Lecture 8 | Deep Learning Software - Duration: 1 hour, 18 minutes.
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Lecture 9 | CNN Architectures - Duration: 1 hour, 17 minutes.
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Lecture 10 | Recurrent Neural Networks - Duration: 1 hour, 13 minutes.
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Lecture 11 | Detection and Segmentation - Duration: 1 hour, 14 minutes.
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Lecture 12 | Visualizing and Understanding - Duration: 1 hour, 15 minutes.
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Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017)
Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation.-
Lecture 1 | Natural Language Processing with Deep Learning - Duration: 1 hour, 11 minutes.
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Lecture 2 | Word Vector Representations: word2vec - Duration: 1 hour, 18 minutes.
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Lecture 3 | GloVe: Global Vectors for Word Representation - Duration: 1 hour, 18 minutes.
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Lecture 4: Word Window Classification and Neural Networks - Duration: 1 hour, 16 minutes.
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Lecture 5: Backpropagation and Project Advice - Duration: 1 hour, 18 minutes.
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Lecture 6: Dependency Parsing - Duration: 1 hour, 23 minutes.
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Lecture 7: Introduction to TensorFlow - Duration: 1 hour, 12 minutes.
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Lecture 8: Recurrent Neural Networks and Language Models - Duration: 1 hour, 18 minutes.
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Lecture 9: Machine Translation and Advanced Recurrent LSTMs and GRUs - Duration: 1 hour, 20 minutes.
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Review Session: Midterm Review - Duration: 1 hour, 25 minutes.
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Lecture 10: Neural Machine Translation and Models with Attention - Duration: 1 hour, 21 minutes.
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Lecture 11: Gated Recurrent Units and Further Topics in NMT - Duration: 1 hour, 20 minutes.
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Engineering Heroes
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Breaking Barriers: Ellen Ochoa's Journey from Stanford to Space - Duration: 1 hour, 16 minutes.
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Perry McCarty, one of the original environmental engineers - Duration: 1 hour, 4 minutes.
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Stanford Engineering Hero Event - Perry McCarty - 10.18.16 - Duration: 1 hour, 13 minutes.
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Brad Parkinson: Stanford Engineering Hero - Duration: 2 minutes, 34 seconds.
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Andy Bechtolsheim - Stanford Engineering Hero - Duration: 2 minutes, 43 seconds.
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Donald Knuth: All Questions Answered - Heroes Lecture Stanford Engineering - Duration: 1 hour, 8 minutes.
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Vint Cerf: Re-Thinking the Internet - Duration: 1 hour, 21 minutes.
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Craig Barrett - Academic turned CEO - Duration: 109 seconds.
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Martin Hellman: The Wisdom of Foolishness - Duration: 1 hour, 11 minutes.
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Stanford Engineering Hero Lecture - Craig Barrett - Duration: 1 hour, 20 minutes.
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Stanford Engineering Hero Martin Hellman on inventing cryptography - Duration: 2 minutes, 21 seconds.
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Stanford Engineering Hero: Craig Barrett - Duration: 1 hour, 20 minutes.
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"What if?"
Stanford Engineering Alumni share the "What if?" question that drives their work.-
What if we could predict earthquakes? - Duration: 2 minutes, 4 seconds.
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What if we can get more girls interested in engineering? - Duration: 3 minutes, 9 seconds.
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What if everyone had access to a home that was built without damaging the planet? - Duration: 4 minutes, 39 seconds.
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What if we could use machine intelligence to save more lives during a natural disaster? - Duration: 5 minutes, 21 seconds.
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Engineering in Action
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Training AI not to misbehave - Duration: 3 minutes, 58 seconds.
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Can tiny medical implants treat disease? - Duration: 2 minutes, 23 seconds.
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Stretchable electrodes pave way for flexible electronics - Duration: 16 seconds.
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A time-lapse look at how Alzheimer’s spreads - Duration: 17 seconds.
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Robot hand moves a ping pong ball - Duration: 15 seconds.
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Robot gently touches a raspberry - Duration: 4 seconds.
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Invasive breast cancer cells push through ‘tissue’ - Duration: 15 seconds.
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E. coli hides “armor” in plain sight - Duration: 10 seconds.
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Future space ‘tow truck’ spots a derelict satellite - Duration: 25 seconds.
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A discovery that could further progress towards flexible electronics - Duration: 25 seconds.
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Long-lasting tags track molecules - Duration: 26 seconds.
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Color-coded bubbles point toward better lubricants - Duration: 37 seconds.
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stanfordonline - Channel
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