Arno candel h2o_a_platform_for_big_math_hadoop_summit_june2016Sri Ambati
H2O: A Platform for Big Math
From just your laptop to 100's of nodes, H2O gives you a Single System Image - easy aggregation of all the memory and all the cores, and a simple coding style that scales wide at in-memory speeds. H2O is easily 1000x faster than disk based clustering solutions, and often 10x faster than best-of-breed alternative in-memory solutions - and will work directly on your existing Hadoop cluster. H2O ingests a wide variety of formats, parallel and distributed across the cluster, and stores the data highly compressed and then lets you do scale-out math at memory-bandwidth speeds (on compressed data!), making terabyte-scale munging an interactive experience. This is a technical talk on the insides of H2O, specifically focusing on the Single-System-Image aspect: how we write single-threaded code, and have H2O auto-parallelize and auto-scale-out to 100's of nodes and 1000's of cores.
Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform. He is also the main author of H2O’s Deep Learning. Before joining H2O.ai, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker. Arno was named "2014 Big Data All-Star" by Fortune Magazine. Follow him on Twitter: @ArnoCandel.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
Machine Learning in 2016: Live Q&A with Carlos GuestrinTuri, Inc.
Live webinar session with Carlos Guestrin, Dato CEO and Amazon Professor of Machine Learning at University of Washington. Carlos reviewed 2015 highlights, previewed the Dato roadmap, and answered real-time questions from participants about use cases, algorithms, and resources.
Arno candel h2o_a_platform_for_big_math_hadoop_summit_june2016Sri Ambati
H2O: A Platform for Big Math
From just your laptop to 100's of nodes, H2O gives you a Single System Image - easy aggregation of all the memory and all the cores, and a simple coding style that scales wide at in-memory speeds. H2O is easily 1000x faster than disk based clustering solutions, and often 10x faster than best-of-breed alternative in-memory solutions - and will work directly on your existing Hadoop cluster. H2O ingests a wide variety of formats, parallel and distributed across the cluster, and stores the data highly compressed and then lets you do scale-out math at memory-bandwidth speeds (on compressed data!), making terabyte-scale munging an interactive experience. This is a technical talk on the insides of H2O, specifically focusing on the Single-System-Image aspect: how we write single-threaded code, and have H2O auto-parallelize and auto-scale-out to 100's of nodes and 1000's of cores.
Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform. He is also the main author of H2O’s Deep Learning. Before joining H2O.ai, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker. Arno was named "2014 Big Data All-Star" by Fortune Magazine. Follow him on Twitter: @ArnoCandel.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
Machine Learning in 2016: Live Q&A with Carlos GuestrinTuri, Inc.
Live webinar session with Carlos Guestrin, Dato CEO and Amazon Professor of Machine Learning at University of Washington. Carlos reviewed 2015 highlights, previewed the Dato roadmap, and answered real-time questions from participants about use cases, algorithms, and resources.
Transformation, H2O Open Dallas 2016, Keynote by Sri Ambati, Sri Ambati
Transformation with Data and AI, H2O Open Dallas 2016, Keynote by Sri Ambati, founder @h2o.ai @srisatish
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
What is "deep learning" and why is it suddenly so popular? In this talk I explore how Deep Learning provides a convenient framework for expressing learning problems and using GPUs to solve them efficiently.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
The Astonishing Resurrection of AI (A Primer on Artificial Intelligence)Matt Turck
Supporting slides for a presentation at the Yale Entrepreneurship Breakfast on March 27, 2015. A primer on how artificial intelligence (AI) rose from of the ashes to become a fascinating category for startup founders and venture capitalists. Mentions our portfolio company x.ai as an example.
The ppt Sujoy and I made for the Psi Phi ( An Inter School Competition held by our School). Our Topic was Artificial Intelligence.
Credits:
Theme Images from ESET NOD32 (My Antivirus of Choice)
Backgrounds from SwimChick.net (Amazing designs here)
Credits Image from Full Metal Alchemist (One of my favorite Anime).
Tutorial for Machine Learning 101 (an all-day tutorial at Strata + Hadoop World, New York City, 2015)
The course is designed to introduce machine learning via real applications like building a recommender image analysis using deep learning.
In this talk we cover deployment of machine learning models.
Transform your Business with AI, Deep Learning and Machine LearningSri Ambati
Video: https://www.youtube.com/watch?v=R3IXd1iwqjc
Meetup: http://www.meetup.com/SF-Bay-ACM/events/231709894/
In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will introduce H2O, a scalable open-source machine learning platform, and show live demos on how to train sophisticated machine learning models on large distributed datasets. He will show how data scientists and application developers can use the Flow GUI, R, Python, Java, Scala, JavaScript and JSON to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech, and marketing.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Accelerating Machine Learning and Deep Learning At Scale...With Apache Spark:...Spark Summit
Deep learning is a fast growing subset of machine learning. There is an emerging trend to conduct deep learning in the same cluster along with existing data processing pipelines to support feature engineering and traditional machine learning. As the leading framework for Distributed ML, we believe that the addition of deep learning to the super-popular Spark framework is important, because it allows Spark developers to perform a range of data analysis tasks within a single framework that helps avoid the complexity inherent in using multiple frameworks and libraries. As one of the early and top contributors to Apache Spark, Intel is thrilled to share with the community a big deal contribution to open source Spark…”BigDL” -… A distributed deep Learning framework organically built on Big Data (Apache Spark) platform. It combines the benefits of “high performance computing” and “Big Data” architecture for rich deep learning support. With BigDL on Spark, customers can eliminate large volume of unnecessary dataset transfer between separate systems, eliminate separate HW clusters and move towards a CPU cluster, reduce system complexity and the latency for end-to-end learning. Ultimately, customers can achieve better scale, higher resource utilization, ease of use/development, and better TCO. Feature parity with Caffe and Torch, significant performance boost when combined with Intel’s Math Kernel Library (MKL), scale-out, fault tolerance, elasticity and dynamic resource sharing are some of the prominent features of BigDL.
BigDL open source project will be launched at the 2017 Spark Summit East and this keynote will help spotlight this new contribution and benefits to the Spark developer community and encourage their wide contribution and collaboration. We will also showcase some real world applications of Big DL from early customers’ adoption.
Let Non-Developers Develop your Site. Manu RaivioFuture Insights
FOWA London 2015
Testing different versions of your site can be hugely distracting; it’s both trivial and non-trivial, and terribly time-consuming. Layer-based iterative development can help by effectuating all changes in the user’s browser instead of the site’s backend. Manu will present this new approach to developing the best performing version of a site using the Frosmo javascript tag and SaaS platform. Together they can save time and let those most interested in the results be in charge of the tests, without putting your site at risk.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Transformation, H2O Open Dallas 2016, Keynote by Sri Ambati, Sri Ambati
Transformation with Data and AI, H2O Open Dallas 2016, Keynote by Sri Ambati, founder @h2o.ai @srisatish
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
What is "deep learning" and why is it suddenly so popular? In this talk I explore how Deep Learning provides a convenient framework for expressing learning problems and using GPUs to solve them efficiently.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
The Astonishing Resurrection of AI (A Primer on Artificial Intelligence)Matt Turck
Supporting slides for a presentation at the Yale Entrepreneurship Breakfast on March 27, 2015. A primer on how artificial intelligence (AI) rose from of the ashes to become a fascinating category for startup founders and venture capitalists. Mentions our portfolio company x.ai as an example.
The ppt Sujoy and I made for the Psi Phi ( An Inter School Competition held by our School). Our Topic was Artificial Intelligence.
Credits:
Theme Images from ESET NOD32 (My Antivirus of Choice)
Backgrounds from SwimChick.net (Amazing designs here)
Credits Image from Full Metal Alchemist (One of my favorite Anime).
Tutorial for Machine Learning 101 (an all-day tutorial at Strata + Hadoop World, New York City, 2015)
The course is designed to introduce machine learning via real applications like building a recommender image analysis using deep learning.
In this talk we cover deployment of machine learning models.
Transform your Business with AI, Deep Learning and Machine LearningSri Ambati
Video: https://www.youtube.com/watch?v=R3IXd1iwqjc
Meetup: http://www.meetup.com/SF-Bay-ACM/events/231709894/
In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will introduce H2O, a scalable open-source machine learning platform, and show live demos on how to train sophisticated machine learning models on large distributed datasets. He will show how data scientists and application developers can use the Flow GUI, R, Python, Java, Scala, JavaScript and JSON to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech, and marketing.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Accelerating Machine Learning and Deep Learning At Scale...With Apache Spark:...Spark Summit
Deep learning is a fast growing subset of machine learning. There is an emerging trend to conduct deep learning in the same cluster along with existing data processing pipelines to support feature engineering and traditional machine learning. As the leading framework for Distributed ML, we believe that the addition of deep learning to the super-popular Spark framework is important, because it allows Spark developers to perform a range of data analysis tasks within a single framework that helps avoid the complexity inherent in using multiple frameworks and libraries. As one of the early and top contributors to Apache Spark, Intel is thrilled to share with the community a big deal contribution to open source Spark…”BigDL” -… A distributed deep Learning framework organically built on Big Data (Apache Spark) platform. It combines the benefits of “high performance computing” and “Big Data” architecture for rich deep learning support. With BigDL on Spark, customers can eliminate large volume of unnecessary dataset transfer between separate systems, eliminate separate HW clusters and move towards a CPU cluster, reduce system complexity and the latency for end-to-end learning. Ultimately, customers can achieve better scale, higher resource utilization, ease of use/development, and better TCO. Feature parity with Caffe and Torch, significant performance boost when combined with Intel’s Math Kernel Library (MKL), scale-out, fault tolerance, elasticity and dynamic resource sharing are some of the prominent features of BigDL.
BigDL open source project will be launched at the 2017 Spark Summit East and this keynote will help spotlight this new contribution and benefits to the Spark developer community and encourage their wide contribution and collaboration. We will also showcase some real world applications of Big DL from early customers’ adoption.
Let Non-Developers Develop your Site. Manu RaivioFuture Insights
FOWA London 2015
Testing different versions of your site can be hugely distracting; it’s both trivial and non-trivial, and terribly time-consuming. Layer-based iterative development can help by effectuating all changes in the user’s browser instead of the site’s backend. Manu will present this new approach to developing the best performing version of a site using the Frosmo javascript tag and SaaS platform. Together they can save time and let those most interested in the results be in charge of the tests, without putting your site at risk.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Deep Learning is the area of machine learning and one of the most talked about trends in business and computer science today.
In this talk, I will give a review of Deep Learning explaining what it is, what kinds of tasks it can do today, and what it probably could do in the future.
How to use Artificial Intelligence with Python? EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Deep learning is an emerging topic in artificial intelligence (AI). A subcategory of machine learning, deep learning deals with the use of neural networks to improve things like speech recognition, computer vision, and natural language processing. It's quickly becoming one of the most sought-after fields in computer science. In the last few years, deep learning has helped forge advances in areas as diverse as object perception, machine translation, and voice recognition--all research topics that have long been difficult for AI researchers to crack.
Kaz Sato, Evangelist, Google at MLconf ATL 2016MLconf
Machine Intelligence at Google Scale: Tensor Flow and Cloud Machine Learning: The biggest challenge of Deep Learning technology is the scalability. As long as using single GPU server, you have to wait for hours or days to get the result of your work. This doesn’t scale for production service, so you need a Distributed Training on the cloud eventually. Google has been building infrastructure for training the large scale neural network on the cloud for years, and now started to share the technology with external developers. In this session, we will introduce new pre-trained ML services such as Cloud Vision API and Speech API that works without any training. Also, we will look how TensorFlow and Cloud Machine Learning will accelerate custom model training for 10x – 40x with Google’s distributed training infrastructure.
Nathan benaich The evolving AI marketplace: from startups to the giantsSudeep Sakalle
Nathan Benaich | Playfair Capital
The evolving AI marketplace: from startups to the giants
AI: what we’re talking about and how best to use it
Today’s marketplace for AI-driven software products
How is the ecosystem changing?
Discussion with the founders of Ravelin, Gluru and Seldon
Inside Deep Learning: theory and practice of modern deep learningManning Publications
Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped—you’ll dive into math, theory, and practical applications. Everything is clearly explained in plain English.
Learn more about the book here: http://mng.bz/MXyE
Read: issuu.com/shuweigoh/docs/skymind
At Skymind, we’re tackling some of the most advanced problems in data analysis and machine intelligence. We offer state-of-the-art, flexible, scalable deep learning for industry. Deep learning is becoming an important tool set for natural-language processing (NLP), computer vision, database predictions, pattern recognition, image/video processing and fraud detection.
Introducing TensorFlow: The game changer in building "intelligent" applicationsRokesh Jankie
This is the slidedeck used for the presentation of the Amsterdam Pipeline of Data Science, held in December 2016. TensorFlow in the open source library from Google to implement deep learning, neural networks. This is an introduction to Tensorflow.
Note: Videos are not included (which were shown during the presentation)
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
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.
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.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
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.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
How to Get CNIC Information System with Paksim Ga.pptx
Deep Learning Lightning Talk
1. Deep Learning Lightning Talk 1
Deep Learning
State-of-the-art, powerful way
to do machine learning
Keynote Template
2. Deep Learning Lightning Talk 2
Our goal: Build awesome data products
User activity, business data,
images, text, audio…
Big Data technologies:
Hadoop, Spark, Hive, Pig, Storm, Impala…
Extract meaning from data and
incorporate it into product:
predicion, analytics, recommendations
!
Technology
!
Data
!
Machine Learning
3. Deep Learning Lightning Talk 3
!
Machine Learning
Machine Learning
There are tons of different machine learning algorithms
for different problems
Unsupervised methods: Customer segmentation
(clustering), dataset visualisation, dimensionality
reduction
⊞
Supervised methods: Predictions, classifications.
Example product: spam filtering
⊞
Recommendations, anomaly detection…
!
⊞
4. Deep Learning Lightning Talk 4
Difficult problems
Image
Recognition
"
Speech
Recognition
♫
Natural Language
Processing
$
6. Deep Learning Lightning Talk 6
Examples
! Skype Translator ! Google+ Photo Tagging
http://www.youtube.com/watch?v=eu9kMIeS0wQ
+ Voice recognition in Android 4.0+, Apple’s Siri, Baidu’s Image Search, and more…
7. Deep Learning Lightning Talk 7
A bit of theory
Deep Learning is a „bigger and badder” approach to neural networks, which are known since 80’
y= g(x ⊗ W)
8. Deep Learning Lightning Talk 7
A bit of theory
Deep Learning is a „bigger and badder” approach to neural networks, which are known since 80’
y= g(x ⊗ W)
9. Deep Learning Lightning Talk 7
A bit of theory
Deep Learning is a „bigger and badder” approach to neural networks, which are known since 80’
y= g(x ⊗ W)
10. Deep Learning Lightning Talk 7
A bit of theory
Deep Learning is a „bigger and badder” approach to neural networks, which are known since 80’
y= g(x ⊗ W)
Now we have much more computing
power to train large (and deep) networks
⊞
Now we know better regularization and
optimization methods
⊞
Now we have much more labeled data⊞
Now we can also train models with
unlabeled data
⊞
11. Deep Learning Lightning Talk 8
Why it works?
Let’s consider the problem of face recognition
That’s how we see it
0.2 0.0 0.1 1.0 1.0 0.1 0.4 0.8 1.0 ... 0.1 That’s how „machine” sees it
12. Deep Learning Lightning Talk 9
Why it works?
It’s much easier to infer that something is a face based on that it has two eyes and nose, than it has some
black pixels in lower left corner, and white area somewhere in the middle
14. Deep Learning Lightning Talk 11
A bit of practice
GPU
Numerical operations are very efficient, up
to 100x faster than CPU
⊞
Single machine, no communication overhead⊞
Significant memory contraints, we can’t train
larger models
⊟
15. Deep Learning Lightning Talk 12
A bit of practice
TASK
one learning task, many workers
different parameters for each worker
PICK BEST MODEL Netflix style!
GPU
WORKER 1 WORKER 2 WORKER 3 WORKER 4
17. Deep Learning Lightning Talk 13
A bit of practice
Cluster
WORKER2
WORKER1
WORKER3
WORKER4
+ ASYNCHRONOUS PARAMETERS SERVER
Google style!
18. Deep Learning Lightning Talk 14
A bit of practice
Cluster
We can train much larger and more
powerful models
⊞
Scalable⊞
Poor resource utlization, even if we restrict
connectivity
⊟
Complicated⊟
19. Deep Learning Lightning Talk 15
Hype
NETFLIX MOVES INTO DEEP LEARNING
RESEARCH TO IMPROVE PERSONALIZATION
10 BREAKTHROUGH TECHNOLOGIES 2013
GIGAOM GUIDE TO DEEP LEARNING:
WHO’S DOING IT AND WHY IT MATTERS
NYU „DEEP LEARNING” PROFESSOR LECUN WILL HEAD
FACEBOOK’S NEW ARTIFICIAL INTELLIGENCE LAB
Geoffrey Hinton
Leading researcher in DL, his startup
was acquired by Google
Lookflow
Deep Learning image startup,
acquired by Yahoo
DeepMind
Deep Learning startup, acquired by
Google for 400 mln USD
Yan LeCun
Leading researcher in DL, hired by
Facebook to lead new AI lab.
20. Deep Learning Lightning Talk 16
Geoffrey Hinton
Leading researcher in DL, his startup
was acquired by Google
Lookflow
Deep Learning image startup,
acquired by Yahoo
DeepMind
Deep Learning startup, acquired by
Google for 400 mln USD
Yan LeCun
Leading researcher in DL, hired by
Facebook to lead new AI lab.
Hype
21. Deep Learning Lightning Talk 17
It’s not a silver bullet
It’s difficult.
Sometimes it’s better to use simpler method.
"
#
Nevertheless, it’s a very powerful technique, has attention of biggest IT
companies and brings us closer to real artificial intelligence
It requires substantial computing power and memory.
Sometimes it’s not feasible to use deep learning models, especially if we have to train them regularly
!
It’s kind of `black-box`
Sometimes we can’t draw conclusions from learned features
22. !
:)
THANKS
$ mateusz.buskiewicz@getbase.com
RESOURCES
MOOC: Neural Networks for Machine Learning
& https://www.coursera.org/course/neuralnets
DL Tutorials + sample code
& http://deeplearning.net/
Google+ Deep Learning Community
& https://plus.google.com/u/0/communities/112866381580457264725
Deep Learning Book by Yoshua Bengio (draft)
& http://www.iro.umontreal.ca/~bengioy/dlbook/
Deep Learning Libraries & Software
& http://deeplearning.net/software_links/