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Applications of Deep Neural Networks Course Overview (1.1, Spring 2022)

Spring 2022 Version. Applications of deep neural networks is a course offered in a hybrid format by Washington University in St. Louis. This course introduces Keras deep neural networks and highlights applications that neural networks are particularly adept at handling compared to previous machine learning models. Deep learning is a group of exciting new technologies […]
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Two Minute Papers: Visually Indicated Sounds | Two Minute Papers #79

The Scholarly Store is available here: https://shop.spreadshirt.net/TwoMinutePapers Using the power of deep learning, it is now possible to create a technique that looks at a silent video and synthesize appropriate sound effects for it. The usage is at the moment, limited to hitting these objects with a drumstick. Note: The authors seem to lean on […]
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Two Minute Papers: Estimating Matrix Rank With Neural Networks | Two Minute Papers #94

This tongue in cheek work is about identifying matrix ranks from images, plugging in a convolutional neural network where it is absolutely inaproppriate to use. The paper “Visually Identifying Rank” is available here: http://www.oneweirdkerneltrick.com/rank.pdf David Fouhey’s website is available here: http://www.cs.cmu.edu/~dfouhey/ The machine learning calculator is available here: http://armlessjohn404.github.io/calcuMLator/ The paper “Separable Subsurface Scattering” is […]
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Two Minute Papers: Photorealistic Images from Drawings | Two Minute Papers #80

The Two Minute Papers subreddit is available here: https://www.reddit.com/r/twominutepapers/ By using a convolutional neural networks (a powerful deep learning technique), it is now possible to build an application that takes a rough sketch as an input, and fetches photorealistic images from a database. ___________________________________ The paper “The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies” […]
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Two Minute Papers: AI Learns To Improve Smoke Simulations | Two Minute Papers #188

The paper “Data-Driven Synthesis of Smoke Flows with CNN-based Feature Descriptors” is available here: Data-Driven Synthesis of Smoke Flows with CNN-based Feature Descriptors Recommended for you: Wavelet Turbulence – https://www.youtube.com/watch?v=5xLSbj5SsSE Neural Network Learns The Physics of Fluids and Smoke – https://www.youtube.com/watch?v=iOWamCtnwTc We would like to thank our generous Patreon supporters who make Two Minute Papers […]
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Keras Convolutional Neural Neural Networks for Regression and Classification (6.2)

Convolutional neural networks bring very advanced image and time series processing capabilities to deep learning. CNNs are a foundational technology that are used in many different image related tasks in deep learning. This video introduces CNNs for MNIST and Fashion MNIST Code for This Video: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_06_2_cnn.ipynb Course Homepage: https://sites.wustl.edu/jeffheaton/t81-558/ Follow Me/Subscribe: https://www.youtube.com/user/HeatonResearch https://github.com/jeffheaton Tweets by […]
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Two Minute Papers: Deep Learning Program Learns to Paint | Two Minute Papers #49

Artificial neural networks were inspired by the human brain and simulate how neurons behave when they are shown a sensory input (e.g., images, sounds, etc). They are known to be excellent tools for image recognition, any many other problems beyond that – they also excel at weather predictions, breast cancer cell mitosis detection, brain image […]
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Two Minute Papers: Pruning Makes Faster and Smaller Neural Networks | Two Minute Papers #229

The paper “Learning to Prune Filters in Convolutional Neural Networks” is available here: https://arxiv.org/pdf/1801.07365.pdf We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Andrew Melnychuk, Brian Gilman, Christian Ahlin, Christoph Jadanowski, Dennis Abts, Emmanuel, Eric Haddad, Esa Turkulainen, Evan Breznyik, Frank Goertzen, Malek Cellier, Marten Rauschenberg, Michael Albrecht, Michael […]
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Two Minute Papers: AI Learns 3D Face Reconstruction | Two Minute Papers #198

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SingularityNET: SingularityDAO CEO Marcello Mari explains AI-powered DeFi on CNN!

Join SingularityDAO at https://app.singularitydao.ai/ Don’t forget to feed the algorithms by liking, subscribing and commenting! 😀 SingularityDAO YouTube: https://www.youtube.com/channel/UCuMyFDPebbrVKdRD2Nddnzw SingularityDAO Telegram: https://t.me/SingularityDAO SingularityDAO Telegram Announcements: https://t.me/sdaoann SingularityDAO Twitter: https://twitter.com/SingularityDao SingularityDAO Facebook: https://www.facebook.com/SingularityDAO SingularityDAO Instagram: https://www.instagram.com/singularitydao SingularityDAO Medium: https://medium.com/singularitydao SingularityDAO Newsletter: https://singularitynet.us16.list-manage.com/subscribe/post?u=d74195510c25bf501caf3011d&id=de16fc7da6 —- SingularityNET is a decentralized marketplace for artificial intelligence. We aim to create the […]
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Two Minute Papers: Can We Detect Neural Image Generators?

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CoRL 2020, Spotlight Talk 478: S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point…

“**S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds** Ran Cheng (Huawei)*; Christopher Agia (University of Toronto); Yuan Ren (Huawei); Xinhai Li (Huawei); Liu Bingbing (Huawei Noah’s Ark Lab, Canada) Publication: http://corlconf.github.io/paper_478/ **Abstract** With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with […]
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What is an Autoencoder? | Two Minute Papers #86

Autoencoders are neural networks that are capable of creating sparse representations of the input data and can therefore be used for image compression. There are denoising autoencoders that after learning these sparse representations, can be presented with noisy images. What is even better is a variant that is called the variational autoencoder that not only […]
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10 More Cool Deep Learning Applications | Two Minute Papers #52

In this episode, we present another round of incredible deep learning applications! _________________________ 1. Colorization – http://tinyclouds.org/colorize/ 2. RNN Music on Bob Sturm’s YouTube channel – https://www.youtube.com/watch?v=RaO4HpM07hE 3. Flow Machines by Sony – https://www.youtube.com/watch?v=buXqNqBFd6E 4. RNN Passwords – https://github.com/gehaxelt/RNN-Passwords 5. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding – http://arxiv.org/abs/1510.00149 […]
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How Do Neural Networks Learn? 🤖

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers Their instrumentation of a previous work we covered is available here: https://app.wandb.ai/stacey/aprl/reports/Adversarial-Policies-in-Multi-Agent-Settings–VmlldzoxMDEyNzE 📝 The paper “CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization” is available here: https://github.com/poloclub/cnn-explainer Live web demo: https://poloclub.github.io/cnn-explainer/ 🙏 We would like to thank our generous […]
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9 Cool Deep Learning Applications | Two Minute Papers #35

Machine learning provides us an incredible set of tools. If you have a difficult problem at hand, you don’t need to hand craft an algorithm for it. It finds out by itself what is important about the problem and tries to solve it on its own. In this video, you’ll see a number of incredible […]
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Powered by AI: How we’ve advanced Smart Camera for new Portal video-calling devices

Smart Camera was built on Facebook AI’s groundbreaking Mask R-CNN framework for object instance segmentation and keypoint detection. We’ve made additional speed and precision improvements in the computer vision models that power Smart Camera. We’re now using Detectron2, the new, second-generation object detection platform created by Facebook AI, to train Smart Camera’s pose estimation models. […]
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Building 3D deep learning models with PyTorch3D

In the same way that Torchvision and Detectron2 offer highly optimized libraries for 2D computer vision, PyTorch3D offers capabilities that support 3D data. Our open source library for 3D deep learning includes support for easy batching of heterogeneous meshes and point clouds, optimized implementations of common 3D operators such as Chamfer Loss and Graph Conv, […]
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Applications of Deep Neural Networks Class Session 8

The eight class gives an overview of computer vision and convolutional neural networks. Jupyter notebooks, data files, and other information can be found at: https://sites.wustl.edu/jeffheaton/t81-558/ Source of this machine learning/AI Video
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Vinay Anantharaman & Michal Wolski Interview – Nexus Lab Cohort 2 – Bite.ai

The podcast you’re about to hear is the third of a series of shows recorded at the NYU Future Labs AI Summit last week in New York City. In this episode, you’ll hear from Bite.ai, a startup founded by Vinay Anantharaman and Michal Wolski, founders who met working at Clarifai, another NYU Future Labs alumni, […]
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In Codice Ratio: Machine Transcription in the Vatican Secret Archive (TF Dev Summit '19)

The “In Codice Ratio” research project is developing a machine transcription system to speed up large scale search and knowledge discovery from historical archives. A custom CNN trained on crowdsourced annotations enables the system to deal with challenging medieval handwriting. See the revamped dev site → https://www.tensorflow.org/ Watch all TensorFlow Dev Summit ’19 sessions → […]
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CNN Office Hours

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2020 For more information, please visit: https://deeplearning.cs.cmu.edu/ YouTube Source for this AI Video