Slides from the TensorFlow meetup at eBay NYC 06/07/2016 based on my blog https://medium.com/@st553/using-transfer-learning-to-classify-images-with-tensorflow-b0f3142b9366
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/from-inference-to-action-ai-beyond-pattern-recognition-a-keynote-presentation-from-pieter-abbeel/
Professor Pieter Abbeel, Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab, presents the “From Inference to Action: AI Beyond Pattern Recognition” tutorial at the May 2021 Embedded Vision Summit.
Pattern recognition—such as that used in image recognition, speech recognition and machine translation—has been the primary focus of the last decade’s progress in artificial intelligence. But intelligence fundamentally requires more than mere pattern recognition: It also requires the ability to achieve goal-oriented behaviors. Two new methods, deep reinforcement learning and deep imitation learning, provide paradigms for learning goal-oriented behaviors and have shown great promise in recent research. These approaches have demonstrated remarkable success in learning to play video games, learning to control simulated and real robots, mastering the classical game of Go and automation of character animation.
In this talk, Abbeel describes the ideas underlying these advances, and their current capabilities and limitations, with a focus on practical applications. He explores the characteristics that have unlocked important new use cases (e.g. AI robotic automation in warehouses) while others (e.g., self-driving cars) remain AI-bottlenecked. He also highlights important areas where significant breakthroughs are still needed.
Slides from the TensorFlow meetup at eBay NYC 06/07/2016 based on my blog https://medium.com/@st553/using-transfer-learning-to-classify-images-with-tensorflow-b0f3142b9366
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/from-inference-to-action-ai-beyond-pattern-recognition-a-keynote-presentation-from-pieter-abbeel/
Professor Pieter Abbeel, Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab, presents the “From Inference to Action: AI Beyond Pattern Recognition” tutorial at the May 2021 Embedded Vision Summit.
Pattern recognition—such as that used in image recognition, speech recognition and machine translation—has been the primary focus of the last decade’s progress in artificial intelligence. But intelligence fundamentally requires more than mere pattern recognition: It also requires the ability to achieve goal-oriented behaviors. Two new methods, deep reinforcement learning and deep imitation learning, provide paradigms for learning goal-oriented behaviors and have shown great promise in recent research. These approaches have demonstrated remarkable success in learning to play video games, learning to control simulated and real robots, mastering the classical game of Go and automation of character animation.
In this talk, Abbeel describes the ideas underlying these advances, and their current capabilities and limitations, with a focus on practical applications. He explores the characteristics that have unlocked important new use cases (e.g. AI robotic automation in warehouses) while others (e.g., self-driving cars) remain AI-bottlenecked. He also highlights important areas where significant breakthroughs are still needed.
This presentation introduces Deep Learning (DL) concepts, such as neural neworks, backprop, activation functions, and Convolutional Neural Networks, followed by an Angular application that uses TypeScript in order to replicate the Tensorflow playground.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
This is a 2 hours overview on the deep learning status as for Q1 2017.
Starting with some basic concepts, continue to basic networks topologies , tools, HW/Accelerators and finally Intel's take on the the different fronts.
Introducing the use of the machine learning in the Matlab Environment. This technique is related to the Artificial Intelligence. Machine Learning is a discussed topic in the field of Computer Science, Robotics, Artificial Vision.
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. During this workshop, we will provide a short background on Deep Learning focusing on relevant application domains and an introduction to the powerful and scalable Deep Learning framework, Apache MXNet. At the end of this tutorial you’ll be able to train your own deep neural network, fine tune existing state of the art models for image and object recognition. We’ll also deep dive on setting up your deep learning infrastructure on AWS and model deployment on AWS Lambda.
This presentation introduces Deep Learning (DL) concepts, such as neural neworks, backprop, activation functions, and Convolutional Neural Networks, followed by an Angular application that uses TypeScript in order to replicate the Tensorflow playground.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
This is a 2 hours overview on the deep learning status as for Q1 2017.
Starting with some basic concepts, continue to basic networks topologies , tools, HW/Accelerators and finally Intel's take on the the different fronts.
Introducing the use of the machine learning in the Matlab Environment. This technique is related to the Artificial Intelligence. Machine Learning is a discussed topic in the field of Computer Science, Robotics, Artificial Vision.
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. During this workshop, we will provide a short background on Deep Learning focusing on relevant application domains and an introduction to the powerful and scalable Deep Learning framework, Apache MXNet. At the end of this tutorial you’ll be able to train your own deep neural network, fine tune existing state of the art models for image and object recognition. We’ll also deep dive on setting up your deep learning infrastructure on AWS and model deployment on AWS Lambda.
Global AI on Virtual Tour Oslo - Anomaly Detection using ML.Net on a drone te...Bruno Capuano
Slides used during the session "Anomaly Detection using ML.Net on a drone telemetry from Azure IoT" for the Global AI on Virtual Tour - Oslo on June 2021
2020 08 06 Global XR Talks - Lessons Learned creating a multiplatform AI proj...Bruno Capuano
Slides used during the session "Lessons Learned creating a multiplatform AI project for Azure Kinect and Hololens 2" for the Global XR Talks on the 2020 08 06
2020 06 13 Best of Build 2020 - Canada Community Edition - Artificial Intelli...Bruno Capuano
Slides used during the "Best of Build 2020 - Canada Community Edition" for the Artificial Intelligence session. Shared session with Frank Boucheros. More information on my blog.
Global Azure AI Tour Buenos Aires Argentina, Drones and AIBruno Capuano
Slides used during my session "How to fly a drone with 20 lines of code and use some AI" for the Global AI Tour event. Virtual Mode for Buenos Aires, Argentina.
2020 04 18 Global AI On Tour Monterrey - Program a Drone using AIBruno Capuano
Slides used in my online session "¡Vamos a programar a un dron para que siga rostros!" for the Global AI On Tour Monterrey.
El próximo 18 de Abril estará hablando de drones, Inteligencia Artificial, Docker, y otras sorpresas para el evento gratuito de Global AI On Tour Monterrey !
2020 04 09 Global AI Community Virtual Tour - Drones and AIBruno Capuano
Slides used during my session "Let’s code a drone to follow faces! Using AI, Python, containers and more. As a bonus we will some Enterprise scenarios." as part of the Global AI Community Virtual Tour.
Slides used during the virtual conference, NetCoreConf on April 04, 2020. The session was a introduction to Machine Learning for .Net developers, using ML.Net as the main framework.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
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.
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
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.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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.
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.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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/
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
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
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.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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.
25. @elbruno
MNIST dataset
• The MNIST database of
handwritten digits,
• Has a training set of 60,000
examples, and a test set of 10,000
examples.
• It is a subset of a larger set
available from NIST.
• The digits have been size-
normalized and centered in a
fixed-size image.
32. @elbruno
A machine learning subfield of learning representations of data. Exceptional effective at
learning patterns.
Deep learning algorithms attempt to learn (multiple levels of) representation by using a
hierarchy of multiple layers
If you provide the system tons of information, it begins to understand it and respond in
useful ways.
Deep Learning (DL)
https://www.xenonstack.com/blog/static/public/uploads/media/machine-learning-vs-deep-learning.png
35. @elbruno
Open source software library for numerical computation using data
flow graphs
Developed by Google Brain Team for machine learning and deep
learning and made open-source
TensorFlow provides an extensive suite of functions and classes that
allow users to build various models from scratch
What is TensorFlow?
These slides are adapted from the following Stanford lectures:
https://web.stanford.edu/class/cs20si/2017/lectures/slides_01.pdf
https://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf
36. @elbruno
import tensorflow as tf
a = tf.add(2, 3)
TF automatically names nodes
if you do not
x = 2
y = 3
print a
>> Tensor("Add:0", shape=(), dtype=int32)
Note: a is NOT 5
TensorFlow Graphs
a
37. @elbruno
Keras is a layer on top of TensorFlow that makes it much easier to create neural
networks.
It provides a higher level API for various machine learning routines.
Unless you are performing research into entirely new structures of deep neural
networks it is unlikely that you need to program TensorFlow directly.
Keras is a separate install from TensorFlow. To install Keras, use pip install keras
(after installing TensorFlow).
TensorFlow and Keras
38. @elbruno
Keras Sequential model is used to
create a feed-forward network, by
stacking layers (successive ‘add’
operations).
Shape of the input layer is specified
in the first hidden layer (or the
output layer if network had no
hidden layer).
- Input 2D image is flattened to 1D
vector.
- Dropout (with the rate 0.2) is
applied to the first hidden layer
TensorFlow for Classification: MNIST
40. @elbruno
Pooling layer
Pooling layers are a form of downsampling which
usually follow convolution layers in the neural network.
Applying pooling to a feature map transforms the map
into a smaller representation, and it loses some of the
exact positional information of the features. Therefore it
makes our network more invariant to small
transformations and distortions in the input image by
asking whether a feature appears in a given region of
an image (the pooling region) rather than at a specific
location.
The most common method of pooling is max-pooling,
where the the maximum value of a given region in a
feature map is taken. The example below shows max
pooling being applied with a 2×2 pooling region.
https://shafeentejani.github.io/assets/images/pooling.gif
41. @elbruno
Activation: ReLU
Takes a real-valued number
and thresholds it at zero
𝑅 𝑛 → 𝑅+
𝑛
Most Deep Networks use ReLU nowadays
� Trains much faster
• accelerates the convergence of SGD
• due to linear, non-saturating form
� Less expensive operations
• compared to sigmoid/tanh (exponentials etc.)
• implemented by simply thresholding a matrix at
f 𝑥 = max(0, 𝑥)
http://adilmoujahid.com/images/activation.png
43. @elbruno
Deep Neural Network: Cat vs Dog
https://becominghuman.ai/building-an-image-classifier-using-deep-learning-in-python-totally-from-a-beginners-perspective-be8dbaf22dd8
44. @elbruno
Deep Neural Network: Cat vs Dog
https://becominghuman.ai/building-an-image-classifier-using-deep-learning-in-python-totally-from-a-beginners-perspective-be8dbaf22dd8