Eac4f222d9d468a0c29a71a3830a5c60 c5_w3l08-attentionmodel
•Download as PPTX, PDF•
0 likes•81 views
The document discusses attention models for sequence to sequence learning. It introduces attention mechanisms that allow a model to focus on specific parts of the input sequence when generating each token of the output sequence. Examples are given of attention models for neural machine translation and image caption generation, including the computation of attention weights and visualization of attention maps.
Report
Share
Report
Share
![Andrew Ng
Attention model
𝑥<1> 𝑥<2> 𝑥<3>
𝑥<5>𝑥<4>
jane visite l’Afrique en septembre
[Bahdanau et. al., 2014. Neural machine translation by jointly learning to align and translate]
𝑎<0>](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Mc td
This lecture discusses model-free prediction methods for estimating the value function of an unknown Markov decision process (MDP) from experience without a model of the MDP dynamics. Monte-Carlo (MC) methods learn directly from complete episodes without bootstrapping, while temporal-difference (TD) methods learn online by bootstrapping from incomplete episodes. TD(λ) combines the advantages of MC and TD methods by using eligibility traces to incorporate information from future time steps into the current update.
07 regularization
This document discusses various regularization techniques for deep learning models. It defines regularization as any modification to a learning algorithm intended to reduce generalization error without affecting training error. It then describes several specific regularization methods, including weight decay, norm penalties, dataset augmentation, early stopping, dropout, adversarial training, and tangent propagation. The goal of regularization is to reduce overfitting and improve generalizability of deep learning models.
Dp
This lecture covers planning by dynamic programming. It introduces dynamic programming and its requirements of optimal substructure and overlapping subproblems. It then discusses policy evaluation, policy iteration, and value iteration as the main dynamic programming algorithms. Policy evaluation evaluates a given policy through iterative application of the Bellman expectation equation. Policy iteration alternates between policy evaluation and policy improvement by acting greedily with respect to the value function. Value iteration directly applies the Bellman optimality equation through iterative backups. The lecture also discusses extensions such as asynchronous dynamic programming and prioritized sweeping.
06 mlp
This document provides an overview of deep feedforward networks. It begins with an example of using a network to solve the XOR problem. It then discusses gradient-based learning and backpropagation. Hidden units with rectified linear activations are commonly used. Deeper networks can more efficiently represent functions and generalize better than shallow networks. Architecture design considerations include width, depth, and number of hidden layers. Backpropagation efficiently computes gradients using the chain rule and dynamic programming.
Mdp
This document provides a summary of Lecture 2 on Markov Decision Processes. It begins with an introduction to Markov processes and their properties. Markov decision processes are then introduced as Markov processes where decisions can be made. The key components of MDPs are defined, including states, actions, transition probabilities, rewards and policies. Value functions are also introduced, which estimate the long-term value or return of states and state-action pairs. Examples are provided throughout to illustrate these concepts.
04 numerical
The document discusses numerical concerns for implementing deep learning algorithms. It covers topics like:
1) Algorithms specified with real numbers but implemented with finite bits can lead to rounding errors and instability.
2) Gradient descent, curvature, and saddle points which are important for iterative optimization.
3) Conditioning problems can cause gradient descent to be slow and fail to exploit curvature. Learning rates must account for curvature.
Intro rl
This document provides an overview of an introductory lecture on reinforcement learning. The key points covered include:
- Reinforcement learning involves an agent learning through trial-and-error interactions with an environment by receiving rewards.
- The goal of reinforcement learning is for the agent to select actions that maximize total rewards. This involves making decisions to balance short-term versus long-term rewards.
- Major components of a reinforcement learning agent include its policy, which determines its behavior, its value function which predicts future rewards, and its model which represents its understanding of the environment's dynamics.
Lec7 deeprlbootcamp-svg+scg
Stochastic computation graphs provide a framework for automatically deriving unbiased gradient estimators. They generalize backpropagation to deal with random variables by treating the computation graph as a DAG with both deterministic and stochastic nodes. This allows gradients to be computed through expectations, enabling techniques like policy gradients for reinforcement learning and variational inference. The document describes several policy gradient methods that use stochastic computation graphs to compute gradients, including SVG(0), SVG(1), and DDPG. These methods have been successfully applied to robotics tasks and driving.
Recommended
Mc td
This lecture discusses model-free prediction methods for estimating the value function of an unknown Markov decision process (MDP) from experience without a model of the MDP dynamics. Monte-Carlo (MC) methods learn directly from complete episodes without bootstrapping, while temporal-difference (TD) methods learn online by bootstrapping from incomplete episodes. TD(λ) combines the advantages of MC and TD methods by using eligibility traces to incorporate information from future time steps into the current update.
07 regularization
This document discusses various regularization techniques for deep learning models. It defines regularization as any modification to a learning algorithm intended to reduce generalization error without affecting training error. It then describes several specific regularization methods, including weight decay, norm penalties, dataset augmentation, early stopping, dropout, adversarial training, and tangent propagation. The goal of regularization is to reduce overfitting and improve generalizability of deep learning models.
Dp
This lecture covers planning by dynamic programming. It introduces dynamic programming and its requirements of optimal substructure and overlapping subproblems. It then discusses policy evaluation, policy iteration, and value iteration as the main dynamic programming algorithms. Policy evaluation evaluates a given policy through iterative application of the Bellman expectation equation. Policy iteration alternates between policy evaluation and policy improvement by acting greedily with respect to the value function. Value iteration directly applies the Bellman optimality equation through iterative backups. The lecture also discusses extensions such as asynchronous dynamic programming and prioritized sweeping.
06 mlp
This document provides an overview of deep feedforward networks. It begins with an example of using a network to solve the XOR problem. It then discusses gradient-based learning and backpropagation. Hidden units with rectified linear activations are commonly used. Deeper networks can more efficiently represent functions and generalize better than shallow networks. Architecture design considerations include width, depth, and number of hidden layers. Backpropagation efficiently computes gradients using the chain rule and dynamic programming.
Mdp
This document provides a summary of Lecture 2 on Markov Decision Processes. It begins with an introduction to Markov processes and their properties. Markov decision processes are then introduced as Markov processes where decisions can be made. The key components of MDPs are defined, including states, actions, transition probabilities, rewards and policies. Value functions are also introduced, which estimate the long-term value or return of states and state-action pairs. Examples are provided throughout to illustrate these concepts.
04 numerical
The document discusses numerical concerns for implementing deep learning algorithms. It covers topics like:
1) Algorithms specified with real numbers but implemented with finite bits can lead to rounding errors and instability.
2) Gradient descent, curvature, and saddle points which are important for iterative optimization.
3) Conditioning problems can cause gradient descent to be slow and fail to exploit curvature. Learning rates must account for curvature.
Intro rl
This document provides an overview of an introductory lecture on reinforcement learning. The key points covered include:
- Reinforcement learning involves an agent learning through trial-and-error interactions with an environment by receiving rewards.
- The goal of reinforcement learning is for the agent to select actions that maximize total rewards. This involves making decisions to balance short-term versus long-term rewards.
- Major components of a reinforcement learning agent include its policy, which determines its behavior, its value function which predicts future rewards, and its model which represents its understanding of the environment's dynamics.
Lec7 deeprlbootcamp-svg+scg
Stochastic computation graphs provide a framework for automatically deriving unbiased gradient estimators. They generalize backpropagation to deal with random variables by treating the computation graph as a DAG with both deterministic and stochastic nodes. This allows gradients to be computed through expectations, enabling techniques like policy gradients for reinforcement learning and variational inference. The document describes several policy gradient methods that use stochastic computation graphs to compute gradients, including SVG(0), SVG(1), and DDPG. These methods have been successfully applied to robotics tasks and driving.
Lec5 advanced-policy-gradient-methods
The document summarizes several advanced policy gradient methods for reinforcement learning, including trust region policy optimization (TRPO), proximal policy optimization (PPO), and using the natural policy gradient with the Kronecker-factored approximation (K-FAC). TRPO frames policy optimization as solving a constrained optimization problem to limit policy updates, while PPO uses a clipped objective function as a pessimistic bound. Both methods improve upon vanilla policy gradients. K-FAC provides an efficient way to approximate the natural policy gradient using the Fisher information matrix. The document reviews the theory and algorithms behind these methods.
Lec6 nuts-and-bolts-deep-rl-research
1) When approaching new problems, start with small test problems and use visualization to interpret the learning process. Make early tasks easier by providing better features or shaping rewards.
2) Ongoing development requires continual benchmarking, using multiple random seeds, and automating experiments. Key parameters like discount factor and action frequency require tuning.
3) For policy gradient strategies, monitor policy entropy and KL divergence as diagnostics. Use baseline explained variance and initialize policies for maximum entropy.
Lec4b pong from_pixels
This document summarizes a deep reinforcement learning approach to train a neural network policy for the game of Pong. The policy network maps game screen images to action probabilities. Policy gradients are used to optimize the network by collecting rollouts of the current policy and using reward signals to increase the probability of actions that led to higher rewards. The network is trained by running many iterations of collecting rollouts, calculating policy gradients with advantage weighting, and updating the network parameters to reinforce successful actions.
Lec4a policy-gradients-actor-critic
This document contains slides about policy gradients, an approach to reinforcement learning. It discusses the likelihood ratio policy gradient method, which estimates the gradient of expected return with respect to the policy parameters. The gradient aims to increase the probability of high-reward paths and decrease low-reward paths. The derivation from importance sampling is shown, and it is noted that this suggests looking at more than just the gradient. Fixes for practical use include adding a baseline to reduce variance and exploiting temporal structure in the paths.
Lec3 dqn
This document summarizes Deep Q-Networks (DQN), a deep reinforcement learning algorithm that was able to achieve human-level performance on many Atari 2600 games. The key ideas of DQN include using a deep neural network to approximate the Q-function, experience replay to increase data efficiency, and a separate target network to stabilize learning. DQN has inspired many follow up algorithms, including double DQN, dueling DQN, prioritized experience replay, and noisy networks for better exploration. DQN was able to learn human-level policies directly from pixels and rewards for many Atari games using the same hyperparameters and network architecture.
Lec2 sampling-based-approximations-and-function-fitting
This document provides a summary of sampling-based approximations for reinforcement learning. It discusses using samples to approximate value iteration, policy iteration, and Q-learning when the state-action space is too large to store a table of values. Key points covered include using Q-learning with function approximation instead of a table, using features to generalize Q-values across states, and examples of feature representations like those used for the Tetris domain. Convergence properties of approximate Q-learning are also discussed.
Lec1 intro-mdps-exact-methods
This document provides the schedule and agenda for a deep reinforcement learning bootcamp. The bootcamp will cover the mathematical and algorithmic foundations of deep RL through lectures and hands-on labs. Over the course of two days, participants will learn about Markov decision processes, exact solution methods, deep Q-networks, policy gradients, trust region policy optimization, and more from leading researchers in the field. The schedule details the timing of lectures, breaks, and labs to help participants understand core algorithms and implement many of them.
02 linear algebra
The document summarizes key concepts from chapter 2 of the lecture slides on linear algebra for deep learning. It defines scalars as single numbers and vectors as 1-D arrays of numbers that can be indexed. Matrices are 2-D arrays of numbers that are indexed with two numbers. Tensors generalize this to arrays with more dimensions. The document also discusses matrix operations like transpose, dot product, and inversion which are important for solving systems of linear equations. It introduces norms as functions to measure the size of vectors.
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
A tale of scale & speed: How the US Navy is enabling software delivery from l...
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
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Communications Mining Series - Zero to Hero - Session 1
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
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
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.
Pushing the limits of ePRTC: 100ns holdover for 100 days
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.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
More Related Content
More from Ronald Teo
Lec5 advanced-policy-gradient-methods
The document summarizes several advanced policy gradient methods for reinforcement learning, including trust region policy optimization (TRPO), proximal policy optimization (PPO), and using the natural policy gradient with the Kronecker-factored approximation (K-FAC). TRPO frames policy optimization as solving a constrained optimization problem to limit policy updates, while PPO uses a clipped objective function as a pessimistic bound. Both methods improve upon vanilla policy gradients. K-FAC provides an efficient way to approximate the natural policy gradient using the Fisher information matrix. The document reviews the theory and algorithms behind these methods.
Lec6 nuts-and-bolts-deep-rl-research
1) When approaching new problems, start with small test problems and use visualization to interpret the learning process. Make early tasks easier by providing better features or shaping rewards.
2) Ongoing development requires continual benchmarking, using multiple random seeds, and automating experiments. Key parameters like discount factor and action frequency require tuning.
3) For policy gradient strategies, monitor policy entropy and KL divergence as diagnostics. Use baseline explained variance and initialize policies for maximum entropy.
Lec4b pong from_pixels
This document summarizes a deep reinforcement learning approach to train a neural network policy for the game of Pong. The policy network maps game screen images to action probabilities. Policy gradients are used to optimize the network by collecting rollouts of the current policy and using reward signals to increase the probability of actions that led to higher rewards. The network is trained by running many iterations of collecting rollouts, calculating policy gradients with advantage weighting, and updating the network parameters to reinforce successful actions.
Lec4a policy-gradients-actor-critic
This document contains slides about policy gradients, an approach to reinforcement learning. It discusses the likelihood ratio policy gradient method, which estimates the gradient of expected return with respect to the policy parameters. The gradient aims to increase the probability of high-reward paths and decrease low-reward paths. The derivation from importance sampling is shown, and it is noted that this suggests looking at more than just the gradient. Fixes for practical use include adding a baseline to reduce variance and exploiting temporal structure in the paths.
Lec3 dqn
This document summarizes Deep Q-Networks (DQN), a deep reinforcement learning algorithm that was able to achieve human-level performance on many Atari 2600 games. The key ideas of DQN include using a deep neural network to approximate the Q-function, experience replay to increase data efficiency, and a separate target network to stabilize learning. DQN has inspired many follow up algorithms, including double DQN, dueling DQN, prioritized experience replay, and noisy networks for better exploration. DQN was able to learn human-level policies directly from pixels and rewards for many Atari games using the same hyperparameters and network architecture.
Lec2 sampling-based-approximations-and-function-fitting
This document provides a summary of sampling-based approximations for reinforcement learning. It discusses using samples to approximate value iteration, policy iteration, and Q-learning when the state-action space is too large to store a table of values. Key points covered include using Q-learning with function approximation instead of a table, using features to generalize Q-values across states, and examples of feature representations like those used for the Tetris domain. Convergence properties of approximate Q-learning are also discussed.
Lec1 intro-mdps-exact-methods
This document provides the schedule and agenda for a deep reinforcement learning bootcamp. The bootcamp will cover the mathematical and algorithmic foundations of deep RL through lectures and hands-on labs. Over the course of two days, participants will learn about Markov decision processes, exact solution methods, deep Q-networks, policy gradients, trust region policy optimization, and more from leading researchers in the field. The schedule details the timing of lectures, breaks, and labs to help participants understand core algorithms and implement many of them.
02 linear algebra
The document summarizes key concepts from chapter 2 of the lecture slides on linear algebra for deep learning. It defines scalars as single numbers and vectors as 1-D arrays of numbers that can be indexed. Matrices are 2-D arrays of numbers that are indexed with two numbers. Tensors generalize this to arrays with more dimensions. The document also discusses matrix operations like transpose, dot product, and inversion which are important for solving systems of linear equations. It introduces norms as functions to measure the size of vectors.
More from Ronald Teo (8)
Lec2 sampling-based-approximations-and-function-fitting
Lec2 sampling-based-approximations-and-function-fitting
Recently uploaded
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
A tale of scale & speed: How the US Navy is enabling software delivery from l...
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
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Communications Mining Series - Zero to Hero - Session 1
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
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
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.
Pushing the limits of ePRTC: 100ns holdover for 100 days
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.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
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:
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Recently uploaded (20)
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
A tale of scale & speed: How the US Navy is enabling software delivery from l...
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Eac4f222d9d468a0c29a71a3830a5c60 c5_w3l08-attentionmodel
- 2. Andrew Ng Attention model 𝑥<1> 𝑥<2> 𝑥<3> 𝑥<5>𝑥<4> jane visite l’Afrique en septembre [Bahdanau et. al., 2014. Neural machine translation by jointly learning to align and translate] 𝑎<0>
- 3. Andrew Ng [Bahdanau et. al., 2014. Neural machine translation by jointly learning to align and translate] Computing attention 𝛼<𝑡,𝑡′> [Xu et. al., 2015. Show, attend and tell: Neural image caption generation with visual attention] 𝛼<𝑡,𝑡′> = amount of attention 𝑦<𝑡 > should pay to 𝑎<𝑡′> 𝛼<𝑡,𝑡′> = exp(𝑒<𝑡,𝑡′>) ∑ 𝑡′=1 𝑇 𝑥 exp(𝑒<𝑡,𝑡′>) 𝑠<𝑡−1> 𝑎<𝑡′> 𝑒<𝑡,𝑡′> + 𝑦<𝑡−1> 𝑦<𝑡> 𝑠<𝑡−1> 𝑠<𝑡> 𝑎<0> 𝑥<1> ⋯ 𝑥<2> 𝑥<𝑇𝑥> 𝑥<𝑇𝑥−1>
- 4. Andrew Ng Attention examples July 20th 1969 1969 − 07 − 20 23 April, 1564 1564 − 04 − 23 Visualization of 𝛼<𝑡,𝑡′>: