Gömülü sistemler özellikle düşük güç harcayarak yüksek işlem gücü sağladığından drone, elektro-optik, robotik ve otonom sistemlerde yaygın bir şekilde kullanılmaktadır.
Bu eğitimimizde derin öğrenme uygulamalarının çalıştırılabildiği gömülü sistemler (FPGA ve GPU), örnek uygulamalar ve uygulama geliştirme süreci anlatılmıştır.
Personalized Job Recommendation System at LinkedIn: Practical Challenges and ...Benjamin Le
A Industry talk given at RecSys 2017 talking about Job Recommendations at LinkedIn and some of the challenges we faced and solved. https://recsys.acm.org/recsys17/industry-session-2/#content-tab-1-4-tab
Build a Deep Learning Video Analytics Framework | SIGGRAPH 2019 Technical Ses...Intel® Software
Explore how to build a unified framework based on FFmpeg and GStreamer to enable video analytics on all Intel® hardware, including CPUs, GPUs, VPUs, FPGAs, and in-circuit emulators.
Installation and configuration of Wireless NICAjay Jassi
This is a presentation showing how to install wireless NIC hardware and software. Images and annotations are used, to make it clear.
This is part of my IT coursework*
Ray Tracing with Intel® Embree and Intel® OSPRay: Use Cases and Updates | SIG...Intel® Software
Explore practical examples of Intel® Embree and Intel® OSPRay in production rendering and the best practices of using the kernels in typical rendering pipelines.
my presentation on AlphaZero (https://arxiv.org/abs/1712.01815) for AI seminar (http://ktiml.mff.cuni.cz/~bartak/ui_seminar/)
LaTeX source code is avalailable at https://github.com/mathemage/AlphaZero-presentation
Personalized Job Recommendation System at LinkedIn: Practical Challenges and ...Benjamin Le
A Industry talk given at RecSys 2017 talking about Job Recommendations at LinkedIn and some of the challenges we faced and solved. https://recsys.acm.org/recsys17/industry-session-2/#content-tab-1-4-tab
Build a Deep Learning Video Analytics Framework | SIGGRAPH 2019 Technical Ses...Intel® Software
Explore how to build a unified framework based on FFmpeg and GStreamer to enable video analytics on all Intel® hardware, including CPUs, GPUs, VPUs, FPGAs, and in-circuit emulators.
Installation and configuration of Wireless NICAjay Jassi
This is a presentation showing how to install wireless NIC hardware and software. Images and annotations are used, to make it clear.
This is part of my IT coursework*
Ray Tracing with Intel® Embree and Intel® OSPRay: Use Cases and Updates | SIG...Intel® Software
Explore practical examples of Intel® Embree and Intel® OSPRay in production rendering and the best practices of using the kernels in typical rendering pipelines.
my presentation on AlphaZero (https://arxiv.org/abs/1712.01815) for AI seminar (http://ktiml.mff.cuni.cz/~bartak/ui_seminar/)
LaTeX source code is avalailable at https://github.com/mathemage/AlphaZero-presentation
Introduction to Deep Learning, Keras, and TensorFlowSri Ambati
This meetup was recorded in San Francisco on Jan 9, 2019.
Video recording of the session can be viewed here: https://youtu.be/yG1UJEzpJ64
Description:
This fast-paced session starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next, we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. If time permits, we'll look at the UAT, CLT, and the Fixed Point Theorem. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Oswald's Bio:
Oswald Campesato is an education junkie: a former Ph.D. Candidate in Mathematics (ABD), with multiple Master's and 2 Bachelor's degrees. In a previous career, he worked in South America, Italy, and the French Riviera, which enabled him to travel to 70 countries throughout the world.
He has worked in American and Japanese corporations and start-ups, as C/C++ and Java developer to CTO. He works in the web and mobile space, conducts training sessions in Android, Java, Angular 2, and ReactJS, and he writes graphics code for fun. He's comfortable in four languages and aspires to become proficient in Japanese, ideally sometime in the next two decades. He enjoys collaborating with people who share his passion for learning the latest cool stuff, and he's currently working on his 15th book, which is about Angular 2.
Knowledge Graphs have proven to be extremely valuable to rec-
ommender systems, as they enable hybrid graph-based recommen-
dation models encompassing both collaborative and content infor-
mation. Leveraging this wealth of heterogeneous information for
top-N item recommendation is a challenging task, as it requires the
ability of effectively encoding a diversity of semantic relations and
connectivity patterns. In this work, we propose entity2rec, a novel
approach to learning user-item relatedness from knowledge graphs
for top-N item recommendation. We start from a knowledge graph
modeling user-item and item-item relations and we learn property-
specific vector representations of users and items applying neural
language models on the network. These representations are used
to create property-specific user-item relatedness features, which
are in turn fed into learning to rank algorithms to learn a global
relatedness model that optimizes top-N item recommendations. We
evaluate the proposed approach in terms of ranking quality on
the MovieLens 1M dataset, outperforming a number of state-of-
the-art recommender systems, and we assess the importance of
property-specific relatedness scores on the overall ranking quality.
System Device Tree and Lopper: Concrete Examples - ELC NA 2022Stefano Stabellini
System Device Tree is an extension to Device Tree to describe all the hardware on an SoC, including heterogeneous CPU clusters and secure resources not typically visible to an Operating System like Linux. This full view allows the System Device Tree to be the "One true source" of the entire hardware description and helps to prevent the common (and hard-to-debug) problem of conflicting resources and system consistency. Lopper is an Open Source framework to parse and manipulate System Device Tree. With Lopper, it is possible to generate multiple traditional Device Trees from a single larger System Device Tree. This presentation will provide an overview of System Device Tree and will discuss the latest updates of the specification and tooling. The talk will illustrate multiple use-cases for System Device Tree with concrete examples, such as Linux running on the more powerful CPU cluster and Zephyr running on a smaller Cortex-R cluster. It will also show how to use Lopper to generate multiple traditional Device Trees targeting different OSes, not just Linux but also Zephyr/other RTOSes. Finally, an end-to-end demo based on Yocto to build a complete heterogeneous system with multiple OSes and RTOSes running on different clusters on a single reference board will be shown.
Updated version of the RecSys TEL lecture I already gave as invited talk in the UK, NL and DE. The conclusion parts is totally new and aligned to the new book on RecSys for Learning at Springer that will appear soon in 2012.
Accelerating Virtual Machine Access with the Storage Performance Development ...Michelle Holley
Abstract: Although new non-volatile media inherently offers very low latency, remote access
using protocols such as NVMe-oF and presenting the data to VMs via virtualized interfaces such as virtio
adds considerable software overhead. One way to reduce the overhead is to use the Storage
Performance Development Kit (SPDK), an open-source software project that provides building blocks for
scalable and efficient storage applications with breakthrough performance. Comparing the software
paths for virtualizing block storage I/O illustrates the advantages of the SPDK-based approach. Empirical
data shows that using SPDK can improve CPU efficiency by up to 10 x and reduce latency up to 50% over
existing methods. Future enhancements for SPDK will make its advantages even greater.
Speaker Bio: Anu Rao is Product line manager for storage software in Data center Group. She helps
customer ease into and adopt open source Storage software like Storage Performance Development Kit
(SPDK) and Intelligent Software Acceleration-Library (ISA-L).
Principal Component Analysis (PCA) and LDA PPT SlidesAbhishekKumar4995
Machine learning (ML) technique use for Dimension reduction, feature extraction and analyzing huge amount of data are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are easily and interactively explained with scatter plot graph , 2D and 3D projection of Principal components(PCs) for better understanding.
byteLAKE and Lenovo presenting Federated Learning at MWC 2019byteLAKE
byteLAKE and Lenovo presenting Federated Learning for IoT live on stage at #MWC19
• real time machine learning
• data stays on edge, only models travel beyond
• leverage on all local AI models across IoT distributed infrastructure
More at: https://www.bytelake.com/en/federated-learning/ and www.byteLAKE.com/en/MWC19
Small introduction to FPGA acceleration and the impact of the new High Level Synthesis toolchains to their programmability
Video here: https://www.linkedin.com/posts/marcobarbone_can-my-application-benefit-from-fpga-acceleration-activity-6848674747375460352-0fua
Slides by Míriam Bellver at the UPC Reading group for the paper:
Liu, Wei, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, and Scott Reed. "SSD: Single Shot MultiBox Detector." ECCV 2016.
Full listing of papers at:
https://github.com/imatge-upc/readcv/blob/master/README.md
by Harald Steck (Netflix Inc., US), Roelof van Zwol (Netflix Inc., US) and Chris Johnson (Spotify Inc., US)
Slides of the tutorial on interactive recommender systems at the 2015 conference on Recommender Systems (RecSys).
Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In the larger ecosystem of recommender systems used on a website, it is positioned between a lean-back recommendation experience and an active search for a specific piece of content. Besides this aspect, we will discuss several parts that are especially important for interactive recommender systems, including the following: design of the user interface and its tight integration with the algorithm in the back-end; computational efficiency of the recommender algorithm; as well as choosing the right balance between exploiting the feedback from the user as to provide relevant recommendations, and enabling the user to explore the catalog and steer the recommendations in the desired direction.
In particular, we will explore the field of interactive video and music recommendations and their application at Netflix and Spotify. We outline some of the user-experiences built, and discuss the approaches followed to tackle the various aspects of interactive recommendations. We present our insights from user studies and A/B tests.
The tutorial targets researchers and practitioners in the field of recommender systems, and will give the participants a unique opportunity to learn about the various aspects of interactive recommender systems in the video and music domain. The tutorial assumes familiarity with the common methods of recommender systems.
DATE: Wednesday, Sept 16, 2015, 11:00-12:30
Optimizing Servers for High-Throughput and Low-Latency at DropboxScyllaDB
I'm going to discuss the efficiency/performance optimizations of different layers of the system. Starting from the lowest levels like hardware and drivers: these tunings can be applied to pretty much any high-load server. Then we’ll move to Linux kernel and its TCP/IP stack: these are the knobs you want to try on any of your TCP-heavy boxes. Finally, we’ll discuss library and application-level tunings, which are mostly applicable to HTTP servers in general and nginx/envoy specifically.
For each potential area of optimization I’ll try to give some background on latency/throughput tradeoffs (if any), monitoring guidelines, and, finally, suggest tunings for different workloads.
Also, I'll cover more theoretical approaches to performance analysis and the newly developed tooling like `bpftrace` and new `perf` features.
The new functionalities of KNOWAGE 8 allow to integrate advanced and increasingly personalized analyses in decision-making processes in a simple and rapid way.
Introduction to Deep Learning, Keras, and TensorFlowSri Ambati
This meetup was recorded in San Francisco on Jan 9, 2019.
Video recording of the session can be viewed here: https://youtu.be/yG1UJEzpJ64
Description:
This fast-paced session starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next, we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. If time permits, we'll look at the UAT, CLT, and the Fixed Point Theorem. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Oswald's Bio:
Oswald Campesato is an education junkie: a former Ph.D. Candidate in Mathematics (ABD), with multiple Master's and 2 Bachelor's degrees. In a previous career, he worked in South America, Italy, and the French Riviera, which enabled him to travel to 70 countries throughout the world.
He has worked in American and Japanese corporations and start-ups, as C/C++ and Java developer to CTO. He works in the web and mobile space, conducts training sessions in Android, Java, Angular 2, and ReactJS, and he writes graphics code for fun. He's comfortable in four languages and aspires to become proficient in Japanese, ideally sometime in the next two decades. He enjoys collaborating with people who share his passion for learning the latest cool stuff, and he's currently working on his 15th book, which is about Angular 2.
Knowledge Graphs have proven to be extremely valuable to rec-
ommender systems, as they enable hybrid graph-based recommen-
dation models encompassing both collaborative and content infor-
mation. Leveraging this wealth of heterogeneous information for
top-N item recommendation is a challenging task, as it requires the
ability of effectively encoding a diversity of semantic relations and
connectivity patterns. In this work, we propose entity2rec, a novel
approach to learning user-item relatedness from knowledge graphs
for top-N item recommendation. We start from a knowledge graph
modeling user-item and item-item relations and we learn property-
specific vector representations of users and items applying neural
language models on the network. These representations are used
to create property-specific user-item relatedness features, which
are in turn fed into learning to rank algorithms to learn a global
relatedness model that optimizes top-N item recommendations. We
evaluate the proposed approach in terms of ranking quality on
the MovieLens 1M dataset, outperforming a number of state-of-
the-art recommender systems, and we assess the importance of
property-specific relatedness scores on the overall ranking quality.
System Device Tree and Lopper: Concrete Examples - ELC NA 2022Stefano Stabellini
System Device Tree is an extension to Device Tree to describe all the hardware on an SoC, including heterogeneous CPU clusters and secure resources not typically visible to an Operating System like Linux. This full view allows the System Device Tree to be the "One true source" of the entire hardware description and helps to prevent the common (and hard-to-debug) problem of conflicting resources and system consistency. Lopper is an Open Source framework to parse and manipulate System Device Tree. With Lopper, it is possible to generate multiple traditional Device Trees from a single larger System Device Tree. This presentation will provide an overview of System Device Tree and will discuss the latest updates of the specification and tooling. The talk will illustrate multiple use-cases for System Device Tree with concrete examples, such as Linux running on the more powerful CPU cluster and Zephyr running on a smaller Cortex-R cluster. It will also show how to use Lopper to generate multiple traditional Device Trees targeting different OSes, not just Linux but also Zephyr/other RTOSes. Finally, an end-to-end demo based on Yocto to build a complete heterogeneous system with multiple OSes and RTOSes running on different clusters on a single reference board will be shown.
Updated version of the RecSys TEL lecture I already gave as invited talk in the UK, NL and DE. The conclusion parts is totally new and aligned to the new book on RecSys for Learning at Springer that will appear soon in 2012.
Accelerating Virtual Machine Access with the Storage Performance Development ...Michelle Holley
Abstract: Although new non-volatile media inherently offers very low latency, remote access
using protocols such as NVMe-oF and presenting the data to VMs via virtualized interfaces such as virtio
adds considerable software overhead. One way to reduce the overhead is to use the Storage
Performance Development Kit (SPDK), an open-source software project that provides building blocks for
scalable and efficient storage applications with breakthrough performance. Comparing the software
paths for virtualizing block storage I/O illustrates the advantages of the SPDK-based approach. Empirical
data shows that using SPDK can improve CPU efficiency by up to 10 x and reduce latency up to 50% over
existing methods. Future enhancements for SPDK will make its advantages even greater.
Speaker Bio: Anu Rao is Product line manager for storage software in Data center Group. She helps
customer ease into and adopt open source Storage software like Storage Performance Development Kit
(SPDK) and Intelligent Software Acceleration-Library (ISA-L).
Principal Component Analysis (PCA) and LDA PPT SlidesAbhishekKumar4995
Machine learning (ML) technique use for Dimension reduction, feature extraction and analyzing huge amount of data are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are easily and interactively explained with scatter plot graph , 2D and 3D projection of Principal components(PCs) for better understanding.
byteLAKE and Lenovo presenting Federated Learning at MWC 2019byteLAKE
byteLAKE and Lenovo presenting Federated Learning for IoT live on stage at #MWC19
• real time machine learning
• data stays on edge, only models travel beyond
• leverage on all local AI models across IoT distributed infrastructure
More at: https://www.bytelake.com/en/federated-learning/ and www.byteLAKE.com/en/MWC19
Small introduction to FPGA acceleration and the impact of the new High Level Synthesis toolchains to their programmability
Video here: https://www.linkedin.com/posts/marcobarbone_can-my-application-benefit-from-fpga-acceleration-activity-6848674747375460352-0fua
Slides by Míriam Bellver at the UPC Reading group for the paper:
Liu, Wei, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, and Scott Reed. "SSD: Single Shot MultiBox Detector." ECCV 2016.
Full listing of papers at:
https://github.com/imatge-upc/readcv/blob/master/README.md
by Harald Steck (Netflix Inc., US), Roelof van Zwol (Netflix Inc., US) and Chris Johnson (Spotify Inc., US)
Slides of the tutorial on interactive recommender systems at the 2015 conference on Recommender Systems (RecSys).
Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In the larger ecosystem of recommender systems used on a website, it is positioned between a lean-back recommendation experience and an active search for a specific piece of content. Besides this aspect, we will discuss several parts that are especially important for interactive recommender systems, including the following: design of the user interface and its tight integration with the algorithm in the back-end; computational efficiency of the recommender algorithm; as well as choosing the right balance between exploiting the feedback from the user as to provide relevant recommendations, and enabling the user to explore the catalog and steer the recommendations in the desired direction.
In particular, we will explore the field of interactive video and music recommendations and their application at Netflix and Spotify. We outline some of the user-experiences built, and discuss the approaches followed to tackle the various aspects of interactive recommendations. We present our insights from user studies and A/B tests.
The tutorial targets researchers and practitioners in the field of recommender systems, and will give the participants a unique opportunity to learn about the various aspects of interactive recommender systems in the video and music domain. The tutorial assumes familiarity with the common methods of recommender systems.
DATE: Wednesday, Sept 16, 2015, 11:00-12:30
Optimizing Servers for High-Throughput and Low-Latency at DropboxScyllaDB
I'm going to discuss the efficiency/performance optimizations of different layers of the system. Starting from the lowest levels like hardware and drivers: these tunings can be applied to pretty much any high-load server. Then we’ll move to Linux kernel and its TCP/IP stack: these are the knobs you want to try on any of your TCP-heavy boxes. Finally, we’ll discuss library and application-level tunings, which are mostly applicable to HTTP servers in general and nginx/envoy specifically.
For each potential area of optimization I’ll try to give some background on latency/throughput tradeoffs (if any), monitoring guidelines, and, finally, suggest tunings for different workloads.
Also, I'll cover more theoretical approaches to performance analysis and the newly developed tooling like `bpftrace` and new `perf` features.
The new functionalities of KNOWAGE 8 allow to integrate advanced and increasingly personalized analyses in decision-making processes in a simple and rapid way.
Ankara Deep Learning - Derin Öğrenme Etkinliği 1Ferhat Kurt
13 Ekim 2015 saat 19:00'da Hacettepe Teknokent Safir Bloklar Konferans Salonunda gerçekleştirdiğimiz Derin Öğrenme etkinliğinin sunum dosyası.
Sunum videosu: https://www.youtube.com/watch?v=K74rzKSsGs8
* Derin Öğrenme Nedir?
* Sektörel Kullanım Alanları Nelerdir?
* Bilişim Devleri ve Endüstri Öncüleri (Google, Facebook, Baidu, IBM, Tesla Motors) Derin Öğrenmeyi Nasıl Kullanıyor?
* Savunma, Güvenlik, Sağlık, Otomotiv, Otonom Sistem, E-ticaret ve Eğlence Sektöründe Değişim Nasıl Olacak?
* Derin öğrenme ile, insan gibi düşünen robotlar nasıl yapılıyor?
Linkedin Türkiye Derin Öğrenme Grubu: https://www.linkedin.com/grp/home?gid=8334641
Ankara Derin Öğrenme Meetup Sayfası: http://www.meetup.com/Ankara-Deep-Learning
http://www.derinogrenme.com
twitter: @ikivanc
Sorularınız için: http://aka.ms/msdniot
IoT, Veri Analizi ve Yapay Zeka Servislerinin Birlikte Kullanım Senaryoları
Günlük hayattaki kullanımımızda IoT cihazlarının yeri git gide artıyor, bu cihazlarda pek çok sensör kullanılıyor yalnız bu sensörlerden gelen verilerin analizi ve mantıklı sonuçların çıkarılmasını çoğu firma gerçekleştirmiyor.
Bu oturumda kurumsal ve günlük hayatta geliştireceğiniz IoT cihazları ve bu cihalardan çıkarılacak katma değerli akıllı sistemlerin nasıl geliştirileceğini ele alacağız.
Yapay Sinir Ağı Geliştirmesi ve Karakter TanımaBusra Pamuk
Lisans bitirme projesi olarak hazırlamış olduğum projenin rapor dosyasıdır. Projemde kısaca; ileri beslemeli yapay sinir ağı modelini ve öğrenme işlemini gerçekleştirmek için geri besleme algoritmasını Java programlama dilini kullanarak implement ettim. Oluşturduğum yapay sinir ağını bir arayüz ile birleştirerek harf tanıma yapan bir sistem oluşturdum.
2020 Yılında SEO: Yapay Zeka ve Makine Öğrenme Dünyasında SEO'nun GeleceğiYiğit Konur
SEOzone Meetups'ın Ocak ayı için yaptığım sunumda SEO'nun geleceği konusunda, benim ve endüstri liderlerinin görüşlerini bir araya getirdim. Bu sunumda, Hubspot'un "History of SEO", Rand Fishkin'in "Ranking Factors 2015" ve "Mad Science Experiments in SEO & Social Media" ile Marcus Tober'in SEOzone'da yaptığı "Ranking Factors for Mobile & Desktop Search" sunumlarından bazı alıntılar yaptım. Ayrıca Google sıralamalarının değişimi için Dr. Pete'in efsanevi "Beyond 10 Blue Links: The Future of Ranking" sunumundan bazı eklemeler yapıp, Web 3.0 kavramından bahsederken Hatem Mahmoud'un "Web 3.0 - Semantic Web" sunumundan faydalandım. Türkiye'deki pazara ait örnekleri verirken, R10.net üzerindeki bazı başlıklardan örnekler seçtim. Bu başlıklar tamamen rastgele seçilmiş olup, ilgili kişilerin isimleri blurlanarak, kişisel bir yönelim olmadığını ve sektörün içinde olduğu durumu göstermek için belirtilmiştir.
İlgili sunumlara ait linkler:
(Thanks for these contributors who deliver great materials to industry)
Marcus Tober: http://www.slideshare.net/seozeo/seozone-2015-marcus-tober-ranking-factors-for-mobile-and-desktop-search
Rand Fishkin: http://www.slideshare.net/randfish/mad-science-experiments-in-seo-social-media
Rand Fishkin: www.slideshare.net/randfish/search-ranking-factors-in-2015/
Rand Fishkin: http://www.slideshare.net/randfish/onsite-seo-in-2015-an-elegant-weapon-for-a-more-civilized-marketer
Dr. Pete Meyers: www.slideshare.net/crumplezone/beyond-10-blue-links-the-future-of-ranking
Hatem Mahmoud: http://www.slideshare.net/HatemMahmoud/web-30-the-semantic-web/136
Hubspot: www.slideshare.net/HubSpot/hub-spot-historyofseocoffeetips/
Yapay Sinir Ağı Geliştirmesi ve Karakter TanımaBusra Pamuk
Lisans bitirme projesi olarak hazırlamış olduğum projenin sunum dosyasıdır. Projemde kısaca; ileri beslemeli yapay sinir ağı modelini ve öğrenme işlemini gerçekleştirmek için geri besleme algoritmasını Java programlama dilini kullanarak implement ettim. Oluşturduğum yapay sinir ağını bir arayüz ile birleştirerek harf tanıma yapan bir sistem oluşturdum.
03 Ali Yavuz ŞAHİN
06 Haberler
14 Sektörden: Ersin Uyar
16 Söyleşi: Christian Hentschel
20 Yapay Zeka İşleri Kolaylaştıracak mı?
24 Blog'unuzun Başarısız Olmasının 10 Sebebi!
28 Siber Risklere Karşı Hazır mısınız?
30 Fujitsu Forum 2016’da Dijitalleşme Rüzgarı
32 Dijital Devrim 5 Yıl İçinde Geleneksel İş Modellerini Yok Edecek
38 2017 Yılının 10 Teknoloji Trendi
40 5G Kullanıcı Sayısı 2022’de Yarım Milyara Ulaşacak
42 Mobil Siteler, Masaüstü Sitelere Yetişti
44 Bankacılık Sektörü Risk Yönetiminde Çıkış Yolu Arıyor
46 Akıllı Telefonlar Ofisteki Verimliliği Azaltıyor
48 Siber Suç Ekonomisi Sağlık Sektöründeki İyi Korunmayan Verilerle Zenginleşiyor
50 Fidye Yazılım Mağdurları 2.6 Kat Arttı
52 Cihazlarımızın Kaçı Koruma Altında?
54 2017’de Dünyayı Bekleyen Siber Tehditler
56 Türkiye Ekonomisine Yönelik Siber Saldırılar Artışta
58 BT Günlüğü Test Merkezi
67 Ecevit BIKTIM
Yapay sinir ağları ile alakalı İnternet de bulunan belgelerden yararlanarak, yapay sinir ağlarına giriş için hazırladığım sunumum. Yapay sinir ağları ile alakalı herhangi bir bilginiz yok ise başlangıç için ideal bir kaynak. (Telif hakkı bulundurabileceği gerekçesi ile kendim hazırlamadığım resimleri kaldırdım. )
Semiconductors are the driving force behind the AI evolution and enable its adoption across various application areas ranging from connected and automated driving to smart healthcare and wearables. Given that, electronics research, design and manufacturing communities around the world are increasingly investing in specialized AI chips providing less latency, greater processing power, higher bandwidth and faster performance. AI also attracts new technology players to invest in making their own specialized AI chips, changing the electronics manufacturing landscape and moving the AI technology towards machine learning, deep learning and neural networks.
Backend.AI Technical Introduction (19.09 / 2019 Autumn)Lablup Inc.
This slide introduces technical specs and details about Backend.AI 19.09.
* On-premise clustering / container orchestration / scaling on cloud
* Container-level fractional GPU technology to use one GPU as many GPUs on many containers at the same time.
* NVidia GPU Cloud integrations
* Enterprise features
Introduction to Software Defined Visualization (SDVis)Intel® Software
Software defined visualization (SDVis) is an open-source initiative from Intel and industry collaborators. Improve the visual fidelity, performance, and efficiency of prominent visualization solutions, while supporting the rapidly growing big data use on workstations through high-performance computing (HPC) on supercomputing clusters without memory limitations and cost of GPU-based solutions.
Enabling Artificial Intelligence - Alison B. LowndesWithTheBest
An overview and update of our hardware and software offering and support provided to the Machine & Deep Learning Community around the world.
Alison B. Lowndes, AI DevRel, EMEA
Breaking New Frontiers in Robotics and Edge Computing with AIDustin Franklin
This NVIDIA webinar will cover the latest tools and techniques to deploy advanced AI at the edge, including Jetson TX2 and TensorRT. Get up to speed on recent developments in robotics and deep learning.
By participating you'll learn:
1. How to build high-performance, energy-efficient embedded systems
2. Workflows for training AI in the cloud and deploying at the edge
3. The latest upcoming JetPack release and its performance improvements.
4. Real-time deep learning primitives for autonomous navigation.
5. NVIDIA’s latest Isaac Initiative for robotics
Harnessing the virtual realm for successful real world artificial intelligenceAlison B. Lowndes
Artificial Intelligence is impacting all areas of society, from healthcare and transportation to smart cities and energy. How NVIDIA invests both in internal pure research and accelerated computation to enable its diverse customer base, across gaming & extended reality, graphics, AI, robotics, simulation, high performance scientific computing, healthcare & more. You will be introduced to the GPU computing platform & shown real world successfully deployed applications as well as a glimpse into the current state of the art across academia, enterprise and startups.
Webinar: NVIDIA JETSON – A Inteligência Artificial na palma de sua mãoEmbarcados
Objetivo do Webinar: Venha saber como a plataforma NVIDIA Jetson e suas ferramentas habilitam você a desenvolver e implantar robôs, drones, aplicativos de IVA e outras máquinas autônomas com tecnologia AI que pensam por conta própria.
Apoio: Arrow e NVIDIA.
Convidado: Marcel Saraiva
Gerente de Contas Enterprise da NVIDIA, executivo com 20 anos de expereincia no mercado de TI, teve na sua carreia passagens pela SGI (Silicon Graphics), Intel e Scansource. Engenheiro eletrico formado pela FEI, com pós-graduação em Marketing pela FAAP e MBA em Gestão Empresarial pela FGV.
Link para o Webinar: https://www.embarcados.com.br/webinars/nvidia-jetson-a-inteligencia-artificial-na-palma-de-sua-mao/
Axel Koehler from Nvidia presented this deck at the 2016 HPC Advisory Council Switzerland Conference.
“Accelerated computing is transforming the data center that delivers unprecedented through- put, enabling new discoveries and services for end users. This talk will give an overview about the NVIDIA Tesla accelerated computing platform including the latest developments in hardware and software. In addition it will be shown how deep learning on GPUs is changing how we use computers to understand data.”
In related news, the GPU Technology Conference takes place April 4-7 in Silicon Valley.
Watch the video presentation: http://insidehpc.com/2016/03/tesla-accelerated-computing/
See more talks in the Swiss Conference Video Gallery:
http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter:
http://insidehpc.com/newsletter
We develop custom Image Recognition systems for Aerospace and defence applications. Using algorithms like Deep Convolutional Neural Networks and Regional Convolutional Neural Networks.
Our algorithms for Target Recognition and Tracking are designed from the beginning to be run on embedded systems. We target both GPU and FPGA devices.
To Train and Validate our algorithms we developed a process to generate photorealistic 3D environments.
Those 3D Environments are used to produce realistic video streams of the targets in different environmental conditions (lighting, adverse meteorological conditions, camouflage, point-of-view).
The same technology can be used to Train and Test Automotive Vision Systems.
Reconfigurable ML Accelerators in VEDLIoT. Marco Tassemeier. Workshop on Deep Learning for IoT (DL4IoT), co-located with HiPEAC 2022, Budapest, Hungary, June 2022
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.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
3. Microsoft & Google
“Superhuman” Image
Recognition
Microsoft
“Super Deep Network”
Berkeley’s Brett
End-to-End
Reinforcement Learning
Deep Speech 2
One network, 2 languages
A New Computing Model
Hits Pop Culture
AlphaGo
Rivals a World Champion
TU Delft Deep-Learning
Amazon Picking Champion
YAPAY ZEKA KİLOMETRE TAŞLARI
6. GPU'lar üstün
performans ve
verimlilik sunar
Tümleşik algılama
ve derin öğrenme,
otonomluk sağlar
x1
x2
x3
x4
OTONOM MAKİNELERİN YÜKSELİŞİ
Otonomluk
gerektiren yeni
kullanım durumları
8. Jetson TX1
Bir Modül Üzerinde Süper Bilgisayar
10 W altında benzersiz performans
Otonom makineler için gelişmiş teknoloji
Kredi kartından daha küçük
9. JETSON TX1
GPU 1 TFLOP/s 256-core Maxwell
CPU 4x 64-bit ARM A57 CPUs | 1.6GHz
Memory 4 GB LPDDR4 | 25.6 GB/s
Video decode 4K 60Hz H.264
Video encode 4K 30Hz H.264
CSI Up to 6 cameras | 1400 Mpix/s
Display 2x DSI, 1x eDP 1.4, 1x DP 1.2/HDMI
Wi-Fi 802.11 2x2 ac
Networking 1 Gigabit Ethernet
PCI-E Gen 2 1x1 + 1x4
Storage 16 GB eMMC, SDIO, SATA
Other 3x UART, 3x SPI, 4x I2C, 4x I2S, GPIOs
Power 10-15W, 6.6V-19.5VDC
Size 50mm x 87mm
Modül Üstünde Sistem
16. • Infrared devices:
• SICK LIDAR (LMS 200); Hokuyo; rpLIDAR
• Asus Xtion Pro Live (PrimeSense)
• Intel RealSense (mult. generations)
• Stereo and color cameras:
• StereoLabs Zed (consumer-oriented)
• Point Grey Research USB3 and GigE
• e-con Systems CSI-MIPI Cameras
with external ISP
THE PERIPHERALS JETSON
CONNECTS WITH
including Community Contributions
36. • Baseline is cuDNN / cuBLAS
• Direct convolution kernels for small batch
• Custom Winograd & Implicit GEMM for Half2
• Custom Deconvolution for filter size == stride case
• Weight pre-transform for Winograd
• Optimal T/N choice for BLAS
• Run cudnnFindForwardConvolutionEx() with multiple iterations
Autotuning
Choose the fastest kernel for each layer
37. // create the network definition
INetworkDefinition* network = infer->createNetwork();
// create a map from caffe blob names to GIE tensors
std::unordered_map<std::string, infer1::Tensor> blobNameToTensor;
// populate the network definition and map
CaffeParser* parser = new CaffeParser;
parser->parse(deployFile, modelFile, *network, blobNameToTensor);
// tell GIE which tensors are required outputs
for (auto& s : outputs)
network->setOutput(blobNameToTensor[s]);
Build
Importing a Caffe Model
38. // Specify the maximum batch size and scratch size
CudaEngineBuildContext buildContext;
buildContext.maxBatchSize = maxBatchSize;
buildContext.maxWorkspaceSize = 1 << 20;
// create the engine
ICudaEngine* engine =
infer->createCudaEngine(buildContext, *network);
// serialize to a C++ stream
engine->serialize(gieModelStream);
Build
Engine Creation
39. // get array bindings for input and output
int inputIndex = engine->getBindingIndex(INPUT_BLOB_NAME),
outputIndex = engine->getBindingIndex(OUTPUT_BLOB_NAME);
// set array of input and output buffers
void* buffers[2];
buffers[inputIndex] = gpuInputBuffer;
buffers[outputIndex] = gpuOutputBuffer;
Runtime
Binding Buffers
40. // Specify the batch size
CudaEngineContext context;
context.batchSize = batchSize;
// add GIE kernels to the given stream
engine->enqueue(context, buffers, stream, NULL);
<…>
// wait on the stream
cudaStreamSynchronize(stream);
Runtime
Running the Engine
41. Training organizations and individuals to solve challenging problems using Deep Learning
On-site workshops and online courses presented by certified experts
Covering complete workflows for proven application use cases
Image classification, object detection, natural language processing, recommendation systems, and more
www.nvidia.com/dli
Hands-on Training for Data Scientists and Software Engineers
NVIDIA Deep Learning Institute
48. A reinforcement learning agent includes:
state (environment)
actions (controls)
reward (feedback)
A value function predicts the future reward
of performing actions in the current state
Given the recent state, action with the maximum
estimated future reward is chosen for execution
For agents with complex state spaces, deep
networks are used as Q-value approximator
Numerical solver (gradient descent) optimizes
the network on-the-fly based on reward inputs
Q-LEARNING
How’s it work?
49.
50. LSTM ACCELERATION
Launch a 2D grid of RNN cells
Multiple layers in a single call are faster
Doesn’t suffer from vanishing gradient
Able to adopt long-term strategy
Supports:
Partially-observable environments
Uni/Bidirectional RNNs
Non-uniform length minibatches
Dropout between layers
52. Derin Öğrenme Sunucuları
(Kütüphane, veri setleri, ağ yapısı ve modellerini içerir)
İstek ön işleme ve sonuç döndürme katmanı
Kullanıcı arayüzü (Web+Api desteği)
Görüntü
Analizi
Ses analizi Veri analizi
Müşteriye özel
analiz yapısı
Girdi Çıktı
Resim
Video
Ses (sinyal)
Veri
Gerçek zamanlı
Sınıflandırılmış
veya
anlamlandırılmış
çıktı
Open Zeka Mimarisi
53. Open Zeka API
GPU ve CPU Bulutu Üzerinde Gömülü Sistemler
Jetson TX1-TK1
Rasberry Pi 3
Test devam ediyor
56. Open Zeka Servisi
Son kullanıcıya Cloud üzerinde
insan algısına yakın bir seviyede
görüntü, ses ve veri analizi
sunma
Model barındırma servisi
(Geliştirici arayüz desteği)
Algoritma geliştirme ve barındırma
servisi (Esnek mimari)
57. Nerede Kullanılacak
• Kamera görüntülerinin (Resim-akış) gerçek
zamanlı anlamlandırılması,
• Eğlence sektörü,
• Sürücü destek sistemleri,
• Otonom ve robotik sistemler (Gömülü teknoloji)
• Savunma sanayiinde sensör kullanan mimarilere
yapay zekâ kazandırılması (Karar destek sistemi)
• Sağlık alanında görüntü ve veri analizi
• Büyük veri analizi (Finans)
Güvenlik
kameralarının
bulut içerisinde
gerçek zamanlı
analizi
68. Türkiye Derin Öğrenme Grubu Sayfası: https://www.linkedin.com/grp/home?gid=8334641
Ankara Derin Öğrenme Meetup Sayfası: http://www.meetup.com/Ankara-Deep-Learning
Derin Öğrenme Grup Sayfası: https://www.facebook.com/groups/derin.ogrenme
http://www.derinogrenme.com
69. “If we knew what it was we were doing, it
would not be called research, would it?”
Einstein
TEŞEKKÜRLER.
