This document summarizes a presentation on deep neural networks for video applications at the edge. It discusses using neural networks for tasks like video generation, frame and clip classification, object detection, caption generation, and video question answering. It also covers techniques like convolutional neural networks, recurrent neural networks, and edge computing with federated machine learning. The presentation provides examples of applying these techniques and links to resources for further learning.
Machine Learning Tokyo - Deep Neural Networks for Video - NumberBoostAlex Conway
Slides from a talk I gave at the Machine Learning Tokyo meetup group on 20190318.
More info here: https://www.meetup.com/Machine-Learning-Tokyo/events/259467268/
Feel free to reach out if you ever need to build a computer vision system or need data labelled to train machine learning models :)
www.numberboost.com
PyConZA 2019 Keynote - Deep Neural Networks for Video ApplicationsAlex Conway
Slides from my PyConZA 2019 Keynote on "Deep Neural Networks for Video Applications"
Don't be afraid of A.I. ... git clone a relevant function (deep learning model), fine-tune it for your use case if required and use it to build cool things! I also do consulting if you get stuck or need help @@@ numberboost.com :P
"Most CCTV video cameras exist as a sort of time machine for insurance purposes. Deep neural networks make it easy to convert video into actionable data which can be used to trigger real-time anomaly alerts and optimize complex business processes. In addition to commercial applications, deep learning can be used to analyze large amounts of video recorded from the point of view of animals to study complex behavior patterns impossible to otherwise analyze. This talk will present some theory of deep neural networks for video applications as well as academic research and several applied real-world industrial examples, with code examples in python."
Note: links are hard to click in SlideShare but are clickable if you download PDF :)
#deeplearning #machinelearning #deeplearningforvideo #convolutionalneuralnetworks #recurrentneuralnetworks #centroidtracking #objectdetection #deepfakes #poseestimation #videomachinelearning #numberboost
PyConZA'17 Deep Learning for Computer VisionAlex Conway
Slides from my talk on deep learning for computer vision at PyConZA on 2017/10/06.
Description:
The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 to just 2.25% in 2017 (human level error is around 5%).
In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’.
The talk will give an overview of the cutting edge and some of the core mathematical concepts and will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python…
PyDresden 20170824 - Deep Learning for Computer VisionAlex Conway
Slides from my talk at PyDresden
The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the international ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 before deep learning to just 2.25% in 2017 (human level error is around 5%).
In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’.
The talk will give an overview of the cutting edge in the field and some of the core mathematical concepts behind the models. It will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python…
NVIDIA compute GPUs and software toolkits are key drivers behind major advancements in machine learning. Of particular interest is a technique called "deep learning", which utilizes what are known as Convolution Neural Networks (CNNs) having landslide success in computer vision and widespread adoption in a variety of fields such as autonomous vehicles, cyber security, and healthcare. In this talk is presented a high level introduction to deep learning where we discuss core concepts, success stories, and relevant use cases. Additionally, we will provide an overview of essential frameworks and workflows for deep learning. Finally, we explore emerging domains for GPU computing such as large-scale graph analytics, in-memory databases.
https://tech.rakuten.co.jp/
Machine Learning Tokyo - Deep Neural Networks for Video - NumberBoostAlex Conway
Slides from a talk I gave at the Machine Learning Tokyo meetup group on 20190318.
More info here: https://www.meetup.com/Machine-Learning-Tokyo/events/259467268/
Feel free to reach out if you ever need to build a computer vision system or need data labelled to train machine learning models :)
www.numberboost.com
PyConZA 2019 Keynote - Deep Neural Networks for Video ApplicationsAlex Conway
Slides from my PyConZA 2019 Keynote on "Deep Neural Networks for Video Applications"
Don't be afraid of A.I. ... git clone a relevant function (deep learning model), fine-tune it for your use case if required and use it to build cool things! I also do consulting if you get stuck or need help @@@ numberboost.com :P
"Most CCTV video cameras exist as a sort of time machine for insurance purposes. Deep neural networks make it easy to convert video into actionable data which can be used to trigger real-time anomaly alerts and optimize complex business processes. In addition to commercial applications, deep learning can be used to analyze large amounts of video recorded from the point of view of animals to study complex behavior patterns impossible to otherwise analyze. This talk will present some theory of deep neural networks for video applications as well as academic research and several applied real-world industrial examples, with code examples in python."
Note: links are hard to click in SlideShare but are clickable if you download PDF :)
#deeplearning #machinelearning #deeplearningforvideo #convolutionalneuralnetworks #recurrentneuralnetworks #centroidtracking #objectdetection #deepfakes #poseestimation #videomachinelearning #numberboost
PyConZA'17 Deep Learning for Computer VisionAlex Conway
Slides from my talk on deep learning for computer vision at PyConZA on 2017/10/06.
Description:
The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 to just 2.25% in 2017 (human level error is around 5%).
In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’.
The talk will give an overview of the cutting edge and some of the core mathematical concepts and will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python…
PyDresden 20170824 - Deep Learning for Computer VisionAlex Conway
Slides from my talk at PyDresden
The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the international ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 before deep learning to just 2.25% in 2017 (human level error is around 5%).
In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’.
The talk will give an overview of the cutting edge in the field and some of the core mathematical concepts behind the models. It will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python…
NVIDIA compute GPUs and software toolkits are key drivers behind major advancements in machine learning. Of particular interest is a technique called "deep learning", which utilizes what are known as Convolution Neural Networks (CNNs) having landslide success in computer vision and widespread adoption in a variety of fields such as autonomous vehicles, cyber security, and healthcare. In this talk is presented a high level introduction to deep learning where we discuss core concepts, success stories, and relevant use cases. Additionally, we will provide an overview of essential frameworks and workflows for deep learning. Finally, we explore emerging domains for GPU computing such as large-scale graph analytics, in-memory databases.
https://tech.rakuten.co.jp/
AI - A recap of the market
Machine Learning history and overview
Deep Learning explained in 2 slides
Deep Learning in Microsoft
Galaxy Classification in-depth
A demo!
Siddha Ganju, NVIDIA. Deep Learning for MobileIT Arena
Currently, Siddha is an Architect at Nvidia focusing on the Self-Driving initiative. She works towards stable and scalable training of neural networks on very large data centers, and utilizes simulation to validate the neural networks.
In 2017 Siddha led NASA’s Long-Period Comets team within their AI accelerator, called Frontier Development Lab, where she used machine learning to develop meteor detectors. Recently this project was able to provide the first-ever instrumental evidence of an outburst of 5 meteors coming from a previously known comet, called C/1907 G1 (Grigg-Mellish). As a member of the NASA FDL AI Technical Committee, Siddha is working towards incorporating AI in many space science projects!
Previously Siddha was a Deep Learning Data Scientist at Deep Vision where she worked on developing and deploying deep learning models on resource constraint edge devices. Siddha graduated from Carnegie Mellon University with a Master’s in Computational Data Science and a Bachelor’s in Computer Science and Technology from the National Institute of Technology (NIT), Hamirpur, India. She has also authored a book on Practical Deep Learning for Cloud, Mobile & Edge – O’Reilly Publishers
Speech Overview:
Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would potentially benefit from the new opportunities enabled by deep learning techniques. However, CNN’s are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. We explain how to practically bring the power of convolutional neural networks and deep learning to memory and power-constrained devices like smartphones and web browsers.
Building a cutting-edge data processing environment on a budgetGael Varoquaux
As a penniless academic I wanted to do "big data" for science. Open source, Python, and simple patterns were the way forward. Staying on top of todays growing datasets is an arm race. Data analytics machinery —clusters, NOSQL, visualization, Hadoop, machine learning, ...— can spread a team's resources thin. Focusing on simple patterns, lightweight technologies, and a good understanding of the applications gets us most of the way for a fraction of the cost.
I will present a personal perspective on ten years of scientific data processing with Python. What are the emerging patterns in data processing? How can modern data-mining ideas be used without a big engineering team? What constraints and design trade-offs govern software projects like scikit-learn, Mayavi, or joblib? How can we make the most out of distributed hardware with simple framework-less code?
Transfer learning (TL) is a research problem in machine learning (ML) that focuses on applying knowledge gained while solving one task to a related task
Convolutional Neural Networks for Image Classification (Cape Town Deep Learni...Alex Conway
Slides for my talk on:
"Convolutional Neural Networks for Image Classification"
...at the Cape Town Deep Learning Meet-up 20170620
https://www.meetup.com/Cape-Town-deep-learning/events/240485642/
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different
classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make training
faster, we used non-saturating neurons and a very efficient GPU implementation
of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
AI - A recap of the market
Machine Learning history and overview
Deep Learning explained in 2 slides
Deep Learning in Microsoft
Galaxy Classification in-depth
A demo!
Siddha Ganju, NVIDIA. Deep Learning for MobileIT Arena
Currently, Siddha is an Architect at Nvidia focusing on the Self-Driving initiative. She works towards stable and scalable training of neural networks on very large data centers, and utilizes simulation to validate the neural networks.
In 2017 Siddha led NASA’s Long-Period Comets team within their AI accelerator, called Frontier Development Lab, where she used machine learning to develop meteor detectors. Recently this project was able to provide the first-ever instrumental evidence of an outburst of 5 meteors coming from a previously known comet, called C/1907 G1 (Grigg-Mellish). As a member of the NASA FDL AI Technical Committee, Siddha is working towards incorporating AI in many space science projects!
Previously Siddha was a Deep Learning Data Scientist at Deep Vision where she worked on developing and deploying deep learning models on resource constraint edge devices. Siddha graduated from Carnegie Mellon University with a Master’s in Computational Data Science and a Bachelor’s in Computer Science and Technology from the National Institute of Technology (NIT), Hamirpur, India. She has also authored a book on Practical Deep Learning for Cloud, Mobile & Edge – O’Reilly Publishers
Speech Overview:
Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would potentially benefit from the new opportunities enabled by deep learning techniques. However, CNN’s are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. We explain how to practically bring the power of convolutional neural networks and deep learning to memory and power-constrained devices like smartphones and web browsers.
Building a cutting-edge data processing environment on a budgetGael Varoquaux
As a penniless academic I wanted to do "big data" for science. Open source, Python, and simple patterns were the way forward. Staying on top of todays growing datasets is an arm race. Data analytics machinery —clusters, NOSQL, visualization, Hadoop, machine learning, ...— can spread a team's resources thin. Focusing on simple patterns, lightweight technologies, and a good understanding of the applications gets us most of the way for a fraction of the cost.
I will present a personal perspective on ten years of scientific data processing with Python. What are the emerging patterns in data processing? How can modern data-mining ideas be used without a big engineering team? What constraints and design trade-offs govern software projects like scikit-learn, Mayavi, or joblib? How can we make the most out of distributed hardware with simple framework-less code?
Transfer learning (TL) is a research problem in machine learning (ML) that focuses on applying knowledge gained while solving one task to a related task
Convolutional Neural Networks for Image Classification (Cape Town Deep Learni...Alex Conway
Slides for my talk on:
"Convolutional Neural Networks for Image Classification"
...at the Cape Town Deep Learning Meet-up 20170620
https://www.meetup.com/Cape-Town-deep-learning/events/240485642/
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different
classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make training
faster, we used non-saturating neurons and a very efficient GPU implementation
of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Come puoi gestire i difetti? Se sei in una fabbrica, la produzione può produrre oggetti con difetti. Oppure i valori dei sensori possono dirti nel tempo che alcuni valori non sono "normali". Cosa puoi fare come sviluppatore (non come Data Scientist) con .NET o Azure per rilevare queste anomalie? Vediamo come in questa sessione.
How can you handle defects? If you are in a factory, production can produce objects with defects. Or values from sensors can tell you over time that some values are not "normal". What can you do as a developer (not a Data Scientist) with .NET o Azure to detect these anomalies? Let's see how in this session.
Presentations from our AI Meetup #3 you can find below. We talked about soil segmentation, deep learning in Automotive, The Usage of the YOLO Algorithm for Traffic Participants Detection.
Presentations are really educational #machinelearning! #artificialintelligence #deeplearning #automotive #ai #novisadai
We present applications of Azure Services such as Azure IaaS/PaaS and Azure RemoteApp in computational fluid dynamics and sparse linear algebra. We also present Microsoft Machine Learning Studio in prediction of the heating load in the buildings.
Introducing the use of the machine learning in the Matlab Environment. This technique is related to the Artificial Intelligence. Machine Learning is a discussed topic in the field of Computer Science, Robotics, Artificial Vision.
Similar to Deep Neural Networks for Video Applications at the Edge (20)
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Deep Neural Networks for Video Applications at the Edge
1. Deep Neural
Networks
for VIDEO
APPLICATIONS
AT THE EDGE
Neither Proprietary nor Confidential – Please Distribute ;)
Alex Conway
alex @ numberboost.com
@ALXCNWY
Oslo machine learning
Meetup 2019.05.16
Neitherconfidential norproprietary- pleasedistribute;)
43. 43
1. Neural Network Crash Course
2. Convolutional Neural Networks
3. Recurrent Neural Networks
4. Object Detection
5. Show and tell
6. Edge Computing & Federated ML
7. SEBENZ.AI
44. Jeremy Howard & Rachel Thomas
http://course.fast.ai
Richard Socher’s Class on NLP (great RNN resource)
http://web.stanford.edu/class/cs224n/
Andrej Karpathy’s Class on Computer Vision (great CNN resource)
http://cs231n.github.io
Keras docs
https://keras.io/
Great Free Resources
46. What is a neuron?
46
• 3 inputs [x1,x2,x3]
• 3 weights [w1,w2,w3]
• 1 bias term b
• Element-wise multiply and sum: inputs & weights
• Add bias term
• Apply activation function f
• Output of neuron is just a number (like theinputs!)
51. What is a
51
Inputs outputs
hidden
layer 1
hidden
layer 2
hidden
layer 3
Note: Outputs of one layer are inputs into the next layer
This (non-convolutional) architecture is called a “multi-layered perceptron”
Neural Network?(Deep)
57. “How much
error increases
when we increase
this weight”
How does a neural network learn?
57
New
weight = Old
weight
Learning
rate-
Gradient of
Error with
respect to Weight
( )x
102. Fine-tuning A CNN To Solve A New Problem
• Load pre-trained VGG network
• Remove final dense & softmax layers that predict 1000 ImageNet classes
• Replace with new dense & softmax layer to predict 8 categories
102
96.3% accuracy in under 2 minutes for
classifying products into categories
(WITH ONLY 3467TRAINING IMAGES!!1!)
