Optimizing training on Apache MXNet (January 2018)Julien SIMON
Techniques and tips to optimize training on Apache MXNet
Infrastructure performance: storage and I/O, GPU throughput, distributed training, CPU-based training, cost
Model performance: data augmentation, initializers, optimizers, etc.
Level 666: you should be familiar with Deep Learning and MXNet
MCL 322 Optimizing Training on Apache MXNet Julien SIMON
Techniques and tips to optimize training on Apache MXNet
Infrastructure performance: storage and I/O, GPU throughput, distributed training, CPU-based training, cost
Model performance: data augmentation, initializers, optimizers, etc.
Level 400: you should be familiar with Deep Learning and MXNet
Deep Learning for Developers (December 2017)Julien SIMON
Talk @ Code Europe, Poland, December 5th, 2017
- An introduction to Deep Learning
- An introduction to Apache MXNet
- Demos using Jupyter notebooks on Amazon SageMaker
- Resources
Optimizing training on Apache MXNet (January 2018)Julien SIMON
Techniques and tips to optimize training on Apache MXNet
Infrastructure performance: storage and I/O, GPU throughput, distributed training, CPU-based training, cost
Model performance: data augmentation, initializers, optimizers, etc.
Level 666: you should be familiar with Deep Learning and MXNet
MCL 322 Optimizing Training on Apache MXNet Julien SIMON
Techniques and tips to optimize training on Apache MXNet
Infrastructure performance: storage and I/O, GPU throughput, distributed training, CPU-based training, cost
Model performance: data augmentation, initializers, optimizers, etc.
Level 400: you should be familiar with Deep Learning and MXNet
Deep Learning for Developers (December 2017)Julien SIMON
Talk @ Code Europe, Poland, December 5th, 2017
- An introduction to Deep Learning
- An introduction to Apache MXNet
- Demos using Jupyter notebooks on Amazon SageMaker
- Resources
Some resources how to navigate in the hardware space in order to build your own workstation for training deep learning models.
Alternative download link: https://www.dropbox.com/s/o7cwla30xtf9r74/deepLearning_buildComputer.pdf?dl=0
Machine Learning Models on Mobile DevicesLars Gregori
Nowadays, mobile devices have enough computing power to run pre-trained models on them. This results in an optimal use of the hardware and an increase in speed, because the data are not sent over the Internet, which also means more privacy. In my presentation I will show different ways to integrate machine learning models into an iOS and Android application.
The participants will learn how to integrate a pre-training model into an iOS and Android application with CoreML and Tensorflow Lite, and how to re-train a model for own pictures and use it instead of the pre-trained model. All examples are shown in a small demo.
https://www.mcubed.london/sessions/machine-learning-models-mobile-devices/
Next-Generation Cloud Platform with optimization of rider & bicycle drafting formation analysis as a case study. UberCloud + Microsoft Azure + Trek Analysis
https://www.theubercloud.com/star-ccm-cloud
Machine Learning Models with Apache MXNet and AWS FargateAmazon Web Services
by Ahmad Khan, Sr. Solutions Architect, AWS
Deep Learning has been delivering state of the art results across a growing number of domains and use cases. Correspondingly, Deep Learning models are being deployed across a growing number of applications across segments. In this session, we will dive deep into serving machine learning models in production, and demonstrate how to efficiently deploy and serve models over serverless infrastructure using the open source project Model Server for Apache MXNet, Containers and AWS Fargate.
Find out more about Deep Learning in terms of
•AI
•Infrastructure
•Common neural network architectures and use cases
•An introduction to Apache MXNet
•Demos
•Resources
Machine Learning is increasingly being used by organisations to move from analysis to prediction. How AWS and open source technology can help you to perform both Deep Learning and Machine Learning
Talk given at Minds Mastering Machines #M3London October 2018. Many AI projects are plagued with inefficiency since we have prioritised speed of development over pragmatic development. There are many areas that we can improve and here is a selection of basic changes that all AI practitioners should understand. Please see the speaker notes for more information and references.
Approximate "Now" is Better Than Accurate "Later"NUS-ISS
How does Twitter track the top trending topics?
How does Amazon keep track of the top-selling items for the day?
How many cabs have been booked this month using your App?
Is the password that a new user is choosing a common/compromised password?
Modern web-scale systems process billions of transactions and generate terabytes of data every single day. In order to find answers to questions against this data, one would initiate a multi-minute query against a NoSQL datastore or kick off a batch job written in a distributed processing framework such as Spark or Flink. However, these jobs are throughput-heavy and not suited for realtime low-latency queries. However, you and your customers would like to have all this information "right now".
At the end of this talk, you'll realize that you can power these low-latency queries and with incredibly low memory footprint "IF" you are willing to accept answers that are, say, 96-99% accurate. This talk introduces some of the go-to probabilistic data structures that are used by organisations with large amounts of data - specifically Bloom filter, Count Min Sketch and HyperLogLog.
At StampedeCon 2014, John Tran of NVIDIA presented "GPUs in Big Data." Modern graphics processing units (GPUs) are massively parallel general-purpose processors that are taking Big Data by storm. In terms of power efficiency, compute density, and scalability, it is clear now that commodity GPUs are the future of parallel computing. In this talk, we will cover diverse examples of how GPUs are revolutionizing Big Data in fields such as machine learning, databases, genomics, and other computational sciences.
Future of computing is boring (and that is exciting!) alekn
We see a trend where computing becomes a metered utility similar to how the electric grid evolved. Initially electricity was generated locally but economies of scale (and standardization) made it more efficient and economical to have utility companies managing the electric grid. Similar developments can be seen in computing where scientific grids paved the way for commercial cloud computing offerings. However, in our opinion, that evolution is far from finished and in this paper we bring forward the remaining challenges and propose a vision for the future of computing. In particular we focus on diverging trends in the costs of computing and developer time, which suggests that future computing architectures will need to optimize for developer time.
Keywords—cloud computing, future, economics, cost
Some resources how to navigate in the hardware space in order to build your own workstation for training deep learning models.
Alternative download link: https://www.dropbox.com/s/o7cwla30xtf9r74/deepLearning_buildComputer.pdf?dl=0
Machine Learning Models on Mobile DevicesLars Gregori
Nowadays, mobile devices have enough computing power to run pre-trained models on them. This results in an optimal use of the hardware and an increase in speed, because the data are not sent over the Internet, which also means more privacy. In my presentation I will show different ways to integrate machine learning models into an iOS and Android application.
The participants will learn how to integrate a pre-training model into an iOS and Android application with CoreML and Tensorflow Lite, and how to re-train a model for own pictures and use it instead of the pre-trained model. All examples are shown in a small demo.
https://www.mcubed.london/sessions/machine-learning-models-mobile-devices/
Next-Generation Cloud Platform with optimization of rider & bicycle drafting formation analysis as a case study. UberCloud + Microsoft Azure + Trek Analysis
https://www.theubercloud.com/star-ccm-cloud
Machine Learning Models with Apache MXNet and AWS FargateAmazon Web Services
by Ahmad Khan, Sr. Solutions Architect, AWS
Deep Learning has been delivering state of the art results across a growing number of domains and use cases. Correspondingly, Deep Learning models are being deployed across a growing number of applications across segments. In this session, we will dive deep into serving machine learning models in production, and demonstrate how to efficiently deploy and serve models over serverless infrastructure using the open source project Model Server for Apache MXNet, Containers and AWS Fargate.
Find out more about Deep Learning in terms of
•AI
•Infrastructure
•Common neural network architectures and use cases
•An introduction to Apache MXNet
•Demos
•Resources
Machine Learning is increasingly being used by organisations to move from analysis to prediction. How AWS and open source technology can help you to perform both Deep Learning and Machine Learning
Talk given at Minds Mastering Machines #M3London October 2018. Many AI projects are plagued with inefficiency since we have prioritised speed of development over pragmatic development. There are many areas that we can improve and here is a selection of basic changes that all AI practitioners should understand. Please see the speaker notes for more information and references.
Approximate "Now" is Better Than Accurate "Later"NUS-ISS
How does Twitter track the top trending topics?
How does Amazon keep track of the top-selling items for the day?
How many cabs have been booked this month using your App?
Is the password that a new user is choosing a common/compromised password?
Modern web-scale systems process billions of transactions and generate terabytes of data every single day. In order to find answers to questions against this data, one would initiate a multi-minute query against a NoSQL datastore or kick off a batch job written in a distributed processing framework such as Spark or Flink. However, these jobs are throughput-heavy and not suited for realtime low-latency queries. However, you and your customers would like to have all this information "right now".
At the end of this talk, you'll realize that you can power these low-latency queries and with incredibly low memory footprint "IF" you are willing to accept answers that are, say, 96-99% accurate. This talk introduces some of the go-to probabilistic data structures that are used by organisations with large amounts of data - specifically Bloom filter, Count Min Sketch and HyperLogLog.
At StampedeCon 2014, John Tran of NVIDIA presented "GPUs in Big Data." Modern graphics processing units (GPUs) are massively parallel general-purpose processors that are taking Big Data by storm. In terms of power efficiency, compute density, and scalability, it is clear now that commodity GPUs are the future of parallel computing. In this talk, we will cover diverse examples of how GPUs are revolutionizing Big Data in fields such as machine learning, databases, genomics, and other computational sciences.
Future of computing is boring (and that is exciting!) alekn
We see a trend where computing becomes a metered utility similar to how the electric grid evolved. Initially electricity was generated locally but economies of scale (and standardization) made it more efficient and economical to have utility companies managing the electric grid. Similar developments can be seen in computing where scientific grids paved the way for commercial cloud computing offerings. However, in our opinion, that evolution is far from finished and in this paper we bring forward the remaining challenges and propose a vision for the future of computing. In particular we focus on diverging trends in the costs of computing and developer time, which suggests that future computing architectures will need to optimize for developer time.
Keywords—cloud computing, future, economics, cost
AWS re:Invent 2016: Deep Learning at Cloud Scale: Improving Video Discoverabi...Amazon Web Services
Deep learning continues to push the state of the art in domains such as video analytics, computer vision, and speech recognition. Deep networks are powered by amazing levels of representational power, feature learning, and abstraction. This approach comes at the cost of a significant increase in required compute power, which makes the AWS cloud an excellent environment for training. Innovators in this space are applying deep learning to a variety of applications. One such innovator, Vilynx, a startup based in Palo Alto, realized that the current pre-roll advertising-based models for mobile video weren’t returning publishers' desired levels of engagement. In this session, we explain the algorithmic challenges of scaling across multiple nodes, and what Intel is doing on AWS to overcome them. We describe the benefits of using AWS CloudFormation to set up a distributed training environment for deep networks. We also showcase Vilynx’s contributions to video discoverability, and explain how Vilynx uses AWS tools to understand video content. This session is sponsored by Intel.
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
infoShare AI Roadshow 2018 - Tomasz Kopacz (Microsoft) - jakie możliwości daj...Infoshare
Podczas tej sesji przyjrzymy się, w jaki sposób można skorzystać z platformy Microsoft do budowy tzw. „inteligentnych” rozwiązań. W przykładach zobaczymy zarówno Cognitive Services, jak i wykorzystaniu GPU (a dokładniej – Batch AI) do uczenia sieci neuronowych. Zajmiemy się także skomplikowanym zagadnieniami związanymi z projektowaniem – tak by algorytmy rozszerzały ludzkie możliwości (a nie nas zastępowały). Sesja zakłada że słuchacze umieją programować.
Keynote given at BOSC, 2010.
Does the hype surrounding cloud match the reality?
Can we use them to solve the problems in provisioning IT services to support next-generation sequencing?
Similar to Deep Learning for Developers (January 2018) (20)
An introduction to computer vision with Hugging FaceJulien SIMON
In this code-level talk, Julien will show you how to quickly build and deploy computer vision applications based on Transformer models. Along the way, you'll learn about the portfolio of open source and commercial Hugging Face solutions, and how they can help you deliver high-quality solutions faster than ever before.
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In this talk, we’ll see how you can put your AI/ML project on the right track from the get-go. Applying common sense and proven best practices, we’ll discuss skills, tools, methods, and more. We’ll also look at several real-life projects built by AWS customers in different industries and startups.
Building Machine Learning Inference Pipelines at Scale (July 2019)Julien SIMON
Talk at OSCON, Portland, 18/07/2019
Real-life Machine Learning applications require more than a single model. Data may need pre-processing: normalization, feature engineering, dimensionality reduction, etc. Predictions may need post-processing: filtering, sorting, combining, etc.
Our goal: build scalable ML pipelines with open source (Spark, Scikit-learn, XGBoost) and managed services (Amazon EMR, AWS Glue, Amazon SageMaker)
Optimize your Machine Learning Workloads on AWS (July 2019)Julien SIMON
Talk at Floor 28, Tel Aviv.
Infrastructure, tips to speed up training, hyperparameter optimization, model compilation, Amazon SageMaker Neo, cost optimization, Amazon Elastic Inference
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
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This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
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All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
6. Amazon EC2 P3 Instances
• Up to eight NVIDIA Tesla V100 GPUs
• 1 PetaFLOPs of computational performance – 14x better than P2
• 300 GB/s GPU-to-GPU communication (NVLink) – 9X better than P2
• 16GB GPU memory with 900 GB/sec peak GPU memory bandwidth
T h e f a s t e s t , m o s t p o w e r f u l G P U i n s t a n c e s i n t h e c l o u d
7. Amazon EC2 C5 with Intel® Xeon® Scalable
Processor
AVX 512
72 vCPUs
“Skylake”
144 GiB memory
C5
12 Gbps to EBS
2X vCPUs
2X performance
3X throughput
2.4X memory
C4
36 vCPUs
“Haswell”
4 Gbps to EBS
60 GiB memory
C5: Nex t Ge ne rat ion
Compute - Opt imize d
Insta nc e s wit h
Inte l® Xe on® Sca la ble Proc e ssor
AWS Compute opt imize d insta nc e s
support t he new Inte l® AV X - 512
a dva nc e d inst ruc t ion set , e na bling
you to more eff ic ie nt ly run ve c tor
proc e ssing work loa ds wit h single
a nd double floating point
pre c ision, suc h a s AI/ma c hine
le a rning or v ide o proc e ssing.
8. EU (Ireland) Region Linux On Demand
PricingvCPU ECU Memory (GiB) Instance Storage (GB) Linux/UNIX Usage
CPU c5.large 2 8 4 EBS Only $0.096 per Hour
c5.xlarge 4 16 8 EBS Only $0.192 per Hour
c5.2xlarge 8 31 16 EBS Only $0.384 per Hour
c5.4xlarge 16 62 32 EBS Only $0.768 per Hour
c5.9xlarge 36 139 72 EBS Only $1.728 per Hour
c5.18xlarge 72 278 144 EBS Only $3.456 per Hour
GPU p2.xlarge 4 12 61 EBS Only $0.972 per Hour
p2.8xlarge 32 94 488 EBS Only $7.776 per Hour
p2.16xlarge 64 188 732 EBS Only $15.552 per Hour
p3.2xlarge 8 23.5 61 EBS Only $3.305 per Hour
p3.8xlarge 32 94 244 EBS Only $13.22 per Hour
p3.16xlarge 64 188 488 EBS Only $26.44 per Hour
Source - https://aws.amazon.com/ec2/pricing/on-demand/?refid=em_67469
As of 19th January 2018
9. 9
EC2 Spot instances for training & inference
GPU - p3.16xlarge CPU - c5.18xlarge
C5 CPU Resources Available for Up to 19.8X cheaper over a 3 Month average
As of 19th January 2018
Source – Spot Pricing History Tool in EC2 Console https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-spot-instances-history.html
11. Convolutional Neural Networks (CNN)
Le Cun, 1998: handwritten digit recognition, 32x32 pixels
Convolution and pooling reduce dimensionality
https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/
12. https://news.developer.nvidia.com/expedia-ranking-hotel-images-with-deep-learning/
• Expedia have over 10 million images from
300,000 hotels
• Using great images boosts conversion
• Using Keras and EC2 GPU instances,
they fine-tuned a pre-trained Convolutional Neural
Network using 100,000 images
• Hotel descriptions now automatically feature the best
available images
CNN: Object Classification
17. Solution
Thorn and AWS-partner, MemSQL, built an age progressed facial recognition
service using data analytics and deep learning on AWS compute-optimized C5 to
identify missing children by matching images against child abuse material. Using
the compute power of Intel® Xeon® Scalable processors in C5, Thorn is able to
match thousands of pictures per second, in real time, against a database of
pictures that is being constantly updated. The goal is to eventually integrate this
capability into Spotlight, Thorn’s trafficking investigations tool that is used by
more than 5,300 officers in over 18 countries
Outcome
Thorn can apply 5,000 data points to a single face and classify, correlate, and
match the image to an image in a database. As a result, the organization’s
solution can make a positive image match in 200 milliseconds, compared to 20
minutes previously.
Spotlight Identifies an average of 5 kids per day.
Source: https://itpeernetwork.intel.com/digital-defenders-fight-child-exploitation/
www.wearethorn.org
350 volunteers/members
United States
Non Profit
Organization
Thorn, a global nonprofit organization
headquartered in Los Angeles, CA
joins forces with the sharpest minds
from tech, non-profit, government
and law enforcement to stop the
spread of child sexual exploitation
and abuse material and stand up to
child traffickers.
www.memsql.com
Partner
MemSQL is a real-time data
warehouse for cloud and on-premises
that delivers immediate insights
across live and historical data.
AI helps find missing kids
19. Long Short Term Memory Networks (LSTM)
• A LSTM neuron computes the
output based on the input and a
previous state
• LSTM networks have memory
• They’re great at predicting
sequences, e.g. machine
translation
21. GAN: Welcome to the (un)real world, Neo
Generating new ”celebrity” faces
https://github.com/tkarras/progressive_growing_of_gans
From semantic map to 2048x1024 picture
https://tcwang0509.github.io/pix2pixHD/
23. Apache MXNet: Open Source library for Deep Learning
Programmable Portable High Performance
Near linear scaling
across hundreds of
GPUs
Highly efficient
models for
mobile
and IoT
Simple syntax,
multiple
languages
Most Open Best On AWS
Optimized for
Deep Learning on AWS
Accepted into the
Apache Incubator
MXNet 1.0 released on December 4th
24. Input Output
1 1 1
1 0 1
0 0 0
3
mx. sym. Convol ut i on( dat a, ker nel =( 5, 5) , num_f i l t er =20)
mx. sym. Pool i ng( dat a, pool _t ype=" max" , ker nel =( 2, 2) ,
st r i de=( 2, 2)
l st m. l st m_unr ol l ( num_l st m_l ayer , seq_l en, l en, num_hi dden, num_embed)
4 2
2 0
4=Max
1
3
...
4
0.2
-0.1
...
0.7
mx. sym. Ful l yConnect ed( dat a, num_hi dden=128)
2
mx. symbol . Embeddi ng( dat a, i nput _di m, out put _di m = k)
0.2
-0.1
...
0.7
Queen
4 2
2 0
2=Avg
Input Weights
cos(w, queen ) = cos(w, k i n g) - cos(w, m an ) + cos(w, w om an )
mx. sym. Act i vat i on( dat a, act _t ype=" xxxx" )
" r el u"
" t anh"
" si gmoi d"
" sof t r el u"
Neural Art
Face Search
Image Segmentation
Image Caption
“ People Riding
Bikes”
Bicycle, People,
Road, Sport
Image Labels
Image
Video
Speech
Text
“ People Riding
Bikes”
Machine Translation
“ Οι άνθρωποι
ιππασίας ποδήλατα”
Events
mx. model . FeedFor war d model . f i t
mx. sym. Sof t maxOut put
28. Demos
- Hello World: learn a synthetic data set
- Classify images with pre-trained models
- Classify MNIST with a MLP and a CNN
https://github.com/juliensimon/dlnotebooks