In recent years, deep-learning based side-channel attacks have been proven to be very effective and opened the door to automated implementation techniques. Building on this line of work, this talk explores how to take the approach a step further and showcases how to leverage the recent advance in AI explainability to quickly assess which parts of the implementation is responsible for the information. Through a concrete set by step example, we will showcase the promise of this approach, its limitations, and how it can be used today.

By Elie Bursztein

Full Abstract & Presentation Materials: https://www.blackhat.com/us-20..../briefings/schedule/

It seems like more and more applications and machines are getting on the image recognition train. It's a cool feature to have because it can assist society in many ways, such as boosting productivity and safety if we're dealing with automated vehicles.

Today on Feed My Curiosity, we look at how image recognition works. How does a machine learn what something looks like? Watch to find out!

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Watch live to learn about how the deep learning frameworks in MATLAB and Simulink can be used with TensorFlow and PyTorch to provide enhanced capabilities for building and training your Machine Learning model.

Heather Gorr, PhD and Yann Debray will show you can take full advantage of the MATLAB ecosystem and integrate it with resources developed by the open-source community. You can combine workflows that include data-centric preprocessing, model tuning, model compression, model integration, and automatic code generation with models developed outside of MATLAB.

Explore the options and benefits, along with examples, of the various interoperability pathways available, including:
- Importing and exporting models from TensorFlow, PyTorch, and ONNX into and from MATLAB
- Coexecuting MATLAB alongside installations of TensorFlow and PyTorch

Welcome to a tutorial where we'll be discussing how to load in our own outside datasets, which comes with all sorts of challenges!

First, we need a dataset. Let's grab the Dogs vs Cats dataset from Microsoft: https://www.microsoft.com/en-u....s/download/confirmat

Text tutorials and sample code: https://pythonprogramming.net/....loading-custom-data-

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With this video, I am beginning a new deep learning tutorial series for total beginners. In this deep learning tutorial python, I will cover following things in this series,
1. Explain neural network concepts in most easiest way
2. Go over math if needed, otherwise keep the tutorials simple and easy
3. Provide exercises that you can practice on
4. Use python, keras and tensorflow mainly. I might cover pytorch as well
5. Cover convolutional neural network (CNN) for image and video processing
6. Cover recurrent neural network (RNN) for sequential analysis and natural language processing (NLP)

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Next video: https://www.youtube.com/watch?v=yfsTZbwgMSE&list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO&index=2

Playlist for this deep learning series: https://www.youtube.com/playli....st?list=PLeo1K3hjS3u

Prerequisites for this series:
1: Python tutorials (first 16 videos): https://www.youtube.com/playli....st?list=PLeo1K3hjS3u
2: Pandas tutorials(first 8 videos): https://www.youtube.com/playli....st?list=PLeo1K3hjS3u
3: Machine learning playlist (first 16 videos): https://www.youtube.com/playli....st?list=PLeo1K3hjS3u

Special thanks to NVIDIA for gifting me Titan RTX GPU. This is Nvidia's top of the line, very powerful GPU that can make running deep learning jobs super fast. I am planning to make tutorials in future where I can utilize this GPU to benchmark deep learning training jobs. Here is more information on this GPU: https://www.nvidia.com/en-us/d....eep-learning-ai/prod

Keywords: Tensorflow tutorial, Deep Learning tutorials, TensorFlow tutorial for beginners, deep learning tutorials for beginners

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Sliding window object detection is a technique that allows you to detect objects in a picture. This technique is not very efficient as it is very compute intensive. Recently new techniques has been discovered that tried to improve performance such as R CNN, Fast R CNN, Faster R CNN etc. YOLO (You only look once) is a state of the art most modern technique that outperforms all other previous techniques such as sliding window object detection, R CNN, Fast and Faster R CNN etc. We will cover YOLO in future videos.

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#objectdetection #deeplearningobjectdetection #slidingwindowobjectdetection #deeplearningtutorial

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❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

In this video we will be test Audio/ Sound classification model for new test data using Deep Learning
Part1
https://www.youtube.com/watch?v=mHPpCXqQd7Y&list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi&index=70
In this video we will be create Audio/ Sound classification model using Deep Learning
Dataset: https://urbansounddataset.weeb....ly.com/download-urba
github: https://github.com/krishnaik06..../Audio-Classificatio
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https://mikesmales.medium.com/....sound-classification
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NVIDIA announced its new CPU today, a collaboration with ARM yielding the "Grace" CPU. The company is also releasing new RTX GPUs (the A4000, A5000, A6000).
Sponsor: Buy Corsair's 5000D Airflow Case on Amazon (https://geni.us/cnVP60)

We're already half-way through our initial new GN 'Volt' Large Modmat inventory. Grab one on the store if you want it this round: https://store.gamersnexus.net/....products/modmat-volt
You can also pick up an Original Modmat (also in stock & shipping) on the store: https://store.gamersnexus.net/products/modmat

NVIDIA's GTC 2021 conference saw the unveil of a series of new GPUs for professionals and data center applications, but all of these were overshadowed by NVIDIA's renewed interest in the CPU space. NVIDIA has been trying to purchase ARM since 2020 (currently pending regulatory approval), and in the meantime, the two organizations are working together to produce the new NVIDIA Grace CPU for data center and server applications. The NVIDIA Grace CPU aims to improve bandwidth of CPU-to-GPU communications and CPU-to-CPU communications, outperforming traditional PCIe signaling with NVIDIA's NVLink.

The GPUs announced are part of the former Quadro line, branding that NVIDIA is slowly moving away from. The A5000 and A4000 GPUs are in the high-end of Ampere RTX cards, with mobile versions also available in Max-Q laptops.

Separately from all of this, NVIDIA talked more about its Omniverse software and a few RTX games, including Black Myth: Wukong, Boundary, Narak: Bladepoint, and Bright Memory. Cyberpunk also got a comically short pseudo-reference.

Like our content? Please consider becoming our Patron to support us: http://www.patreon.com/gamersnexus

TIMESTAMPS

00:00 - NVIDIA GTC 2021 Announcements
01:03 - NVIDIA's New "Grace" ARM CPU
05:11 - New GPUs from NVIDIA: A4000, A5000
10:51 - A10, A16, & Omniverse

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Editorial, Host: Steve Burke
Editorial: Patrick Lathan
Video: Andrew Coleman

Casting graph neural networks (GNNs) within the Geometric Deep Learning blueprint, then demonstrating how we can use the blueprint to extend GNNs beyond the notion of permutation equivariance.
Guest Lecture at the Machine Learning with Graphs (CS224W) course, Stanford University, 30 November 2021

Slide deck: https://petar-v.com/talks/5G-CS224W.pdf

In this video, we will talk about the best laptops for deep learning.

In case you have a specific budget in mind, let me know in the comments below so that I can create another video based on your budget :)

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Deep Learning in Life Sciences - Lecture 05 - Interpretable Deep Learning (Spring 2021)
6.874/6.802/20.390/20.490/HST.506 Spring 2021 Prof. Manolis Kellis
Deep Learning in the Life Sciences / Computational Systems Biology
Playlist: https://youtube.com/playlist?l....ist=PLypiXJdtIca5sxV
Latest slides and course today: http://compbio.mit.edu/6874
Spring 2021 slides and materials: http://mit6874.github.io/

0:00 Lecture outline
3:08 Interpretability: definition, importance
10:30 Interpretability: ante-hoc vs. post-hoc
18:26 Interpreting models: Weight visualization
22:20 Interpreting models: Surrogate model
24:14 Interpreting models: Activation Maximization / Data generation
34:26 Interpreting models: Example-based
39:36 Interpreting decisions
42:24 Interpreting decisions: Example based
45:39 Interpreting decisions: Attribution methods
1:01:17 Interpreting decisions: Gradient based
1:08:55 Interpreting decisions: Backprop-based
1:13:23 Evaluating attributions
1:14:15 Evaluating attributions: Coherence
1:15:30 Evaluating attributions: Class sensitivity
1:16:20 Evaluating attributions: Selectivity
1:19:45 Evaluating attributions: Remove and retrain/keep and retrain
1:21:15 Lecture summary

この動画では、ニューラルネットワークの歴史を追いながらDeep Learningの基礎についてやさしく解説します。

前回の動画（今Deep Learningに取り組むべき理由）はこちらです。
https://www.youtube.com/watch?v=-Dl8s4iufxI

再生リスト「Deep Learning入門」
https://www.youtube.com/playli....st?list=PLg1wtJlhfh2

Neural Network Console
https://dl.sony.com/ja/

Neural Network Libraries
https://nnabla.org/ja/

This lecture, by DeepMind Research Scientist Felix Hill, first discusses the motivation for modelling language with ANNs: language is highly contextual, typically non-compositional and relies on reconciling many competing sources of information. This section also covers Elman's Finding Structure in Time and simple recurrent networks, the importance of context and transformers. In the second part, he explores unsupervised and representation learning for language from Word2Vec to BERT. Finally, Felix discusses situated language understanding, grounding and embodied language learning.

Download the slides here:
https://storage.googleapis.com..../deepmind-media/UCLx

Find out more about how DeepMind increases access to science here:
https://deepmind.com/about#access_to_science

Speaker Bio:

Felix Hill is a Research Scientist working on grounded language understanding, and has been at DeepMind for almost 4 years. He studied pure maths as an undergrad, then got very interested in linguistics and psychology after reading the PDP books by McClelland and Rumelhart, so started graduate school at the University of Cambridge, and ended up in the NLP group. To satisfy his interest in artificial neural networks, he visited Yoshua Bengio's lab in 2013 and started a series of collaborations with Kyunghyun Cho and Yoshua applying neural nets to text processing. This led to some of the first work on transfer learning with sentence representations (and a neural crossword solver). He also interned at FAIR in NYC with Jason Weston. At DeepMind, he's worked on developing agents that can understand language in the context of interactive 3D worlds, together with problems relating to mathematical and analogical reasoning.

About the lecture series:

The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. Over the past decade, Deep Learning has evolved as the leading artificial intelligence paradigm providing us with the ability to learn complex functions from raw data at unprecedented accuracy and scale. Deep Learning has been applied to problems in object recognition, speech recognition, speech synthesis, forecasting, scientific computing, control and many more. The resulting applications are touching all of our lives in areas such as healthcare and medical research, human-computer interaction, communication, transport, conservation, manufacturing and many other fields of human endeavour. In recognition of this huge impact, the 2019 Turing Award, the highest honour in computing, was awarded to pioneers of Deep Learning.

In this lecture series, research scientists from leading AI research lab, DeepMind, deliver 12 lectures on an exciting selection of topics in Deep Learning, ranging from the fundamentals of training neural networks via advanced ideas around memory, attention, and generative modelling to the important topic of responsible innovation.

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Machine learning using neural networks is a very powerful methodology which has demonstrated utility in many different situations. In this talk I will show how work in the mathematical discipline called topological data analysis can be used to (1) lessen the amount of data needed in order to be able to learn and (2) make the computations more transparent. We will work primarily with image and video data.

This talk was part of the workshop on "Topological Data Analysis - Theory and Applications" supported by the Tutte Institute and Western University: https://math.sci.uwo.ca/~jardine/TDA-2021.html