Instructor: Mat Leonard
Outline
1. Text Processing
Using Python + NLTK
Cleaning
Normalization
Tokenization
Part-of-speech Tagging
Stemming and Lemmatization
2. Feature Extraction
Bag of Words
TF-IDF
Word Embeddings
Word2Vec
GloVe
3. Topic Modeling
Latent Variables
Beta and Dirichlet Distributions
Laten Dirichlet Allocation
4. NLP with Deep Learning
Neural Networks
Recurrent Neural Networks (RNNs)
Word Embeddings
Sentiment Analysis with RNNs
These days we see a lot of buzz about Machine Learning(ML)/Artificial Intelligence(AI), and why not, we all are consumers of ML directly or indirectly, irrespective of our profession. AI and ML is a fantastic field, everyone is excited about it, and rightly so. In this tutorial series, we will try to explore and demystify the complicated world of {maths, equations, and theory} that functions in tandem to bring out the "magic" which we experience on many application(s)/software(s). In this talk we will learn about Supervised Learning, Decision Tree, and shall solve some problem with SageMaker.
Blog: https://dev.to/aws/an-introduction-to-decision-tree-and-ensemble-methods-part-1-24p0
Code: https://github.com/debnsuma/AI-ML-Algo2020/tree/master/01.Decision_Tree
Divya Jain at AI Frontiers : Video SummarizationAI Frontiers
As video content is becoming mainstream, video summarization is becoming a hot research topic in academia and industry. Video thumbnail generation and summarization has been worked on for years, but deep learning and reinforcement learning is changing the landscape and emerging as the winner for optimal frame selection. Recent advances in GANs are improving the quality, aesthetics and relevancy of the frames to represent the original videos. Come join this session to get an understanding of various challenges and emerging solutions around video summarization.
Training at AI Frontiers 2018 - LaiOffer Data Session: How Spark Speedup AI AI Frontiers
Topic: How to use big data to enhance AI
Outline:
1. Spark ETL
Spark SQL
Spark Streaming
2. Spark ML
Spark ML pipeline
Distributed model tuning
Spark ML model and data lineage management
3. Spark XGboost
XGboost introduction
XGboost with Spark
XGboost with GPU
4. Spark Deep Learning pipeline
Transfer learning
Build Spark ML pipeline with TensorFlow
Model selection on distributed TF model
Training at AI Frontiers 2018 - Ni Lao: Weakly Supervised Natural Language Un...AI Frontiers
In this tutorial I will introduce recent work in applying weak supervision and reinforcement learning to Questions Answering (QA) systems. Specifically we discuss the semantic parsing task for which natural language queries are converted to computation steps on knowledge graphs or data tables and produce the expected answers. State-of-the-art results can be achieved by novel memory structure for sequence models and improvements in reinforcement learning algorithms. Related code and experiment setup can be found at https://github.com/crazydonkey200/neural-symbolic-machines. Related paper: https://openreview.net/pdf?id=SyK00v5xx.
Training at AI Frontiers 2018 - Lukasz Kaiser: Sequence to Sequence Learning ...AI Frontiers
Sequence to sequence learning is a powerful way to train deep networks for machine translation, various NLP tasks, but also image generation and recently video and music generation. We will give a hands-on tutorial showing how to use the open-source Tensor2Tensor library to train state-of-the-art models for translation, image generation, and a task of your choice!
These days we see a lot of buzz about Machine Learning(ML)/Artificial Intelligence(AI), and why not, we all are consumers of ML directly or indirectly, irrespective of our profession. AI and ML is a fantastic field, everyone is excited about it, and rightly so. In this tutorial series, we will try to explore and demystify the complicated world of {maths, equations, and theory} that functions in tandem to bring out the "magic" which we experience on many application(s)/software(s). In this talk we will learn about Supervised Learning, Decision Tree, and shall solve some problem with SageMaker.
Blog: https://dev.to/aws/an-introduction-to-decision-tree-and-ensemble-methods-part-1-24p0
Code: https://github.com/debnsuma/AI-ML-Algo2020/tree/master/01.Decision_Tree
Divya Jain at AI Frontiers : Video SummarizationAI Frontiers
As video content is becoming mainstream, video summarization is becoming a hot research topic in academia and industry. Video thumbnail generation and summarization has been worked on for years, but deep learning and reinforcement learning is changing the landscape and emerging as the winner for optimal frame selection. Recent advances in GANs are improving the quality, aesthetics and relevancy of the frames to represent the original videos. Come join this session to get an understanding of various challenges and emerging solutions around video summarization.
Training at AI Frontiers 2018 - LaiOffer Data Session: How Spark Speedup AI AI Frontiers
Topic: How to use big data to enhance AI
Outline:
1. Spark ETL
Spark SQL
Spark Streaming
2. Spark ML
Spark ML pipeline
Distributed model tuning
Spark ML model and data lineage management
3. Spark XGboost
XGboost introduction
XGboost with Spark
XGboost with GPU
4. Spark Deep Learning pipeline
Transfer learning
Build Spark ML pipeline with TensorFlow
Model selection on distributed TF model
Training at AI Frontiers 2018 - Ni Lao: Weakly Supervised Natural Language Un...AI Frontiers
In this tutorial I will introduce recent work in applying weak supervision and reinforcement learning to Questions Answering (QA) systems. Specifically we discuss the semantic parsing task for which natural language queries are converted to computation steps on knowledge graphs or data tables and produce the expected answers. State-of-the-art results can be achieved by novel memory structure for sequence models and improvements in reinforcement learning algorithms. Related code and experiment setup can be found at https://github.com/crazydonkey200/neural-symbolic-machines. Related paper: https://openreview.net/pdf?id=SyK00v5xx.
Training at AI Frontiers 2018 - Lukasz Kaiser: Sequence to Sequence Learning ...AI Frontiers
Sequence to sequence learning is a powerful way to train deep networks for machine translation, various NLP tasks, but also image generation and recently video and music generation. We will give a hands-on tutorial showing how to use the open-source Tensor2Tensor library to train state-of-the-art models for translation, image generation, and a task of your choice!
Percy Liang at AI Frontiers : Pushing the Limits of Machine LearningAI Frontiers
In recent years, machine learning has undoubtedly been hugely successful in driving progress in AI applications. However, as we will explore in this talk, even state-of-the-art systems have "blind spots" which make them generalize poorly out of domain and render them vulnerable to adversarial examples. We then suggest that more unsupervised learning settings can encourage the development of more robust systems. We show positive results on two tasks: (i) text style and attribute transfer, the task of converting a sentence with one attribute (e.g., sentiment) to one with another; and (ii) solving SAT instances (classical problems requiring logical reasoning) using end-to-end neural networks.
Ilya Sutskever at AI Frontiers : Progress towards the OpenAI missionAI Frontiers
I will present several advances in deep learning from OpenAI. First, I will present OpenAI Five, a neural network that learned to play on par with some of the strongest professional Dota 2 teams in the world in an 18-hero version of the game. Next, I will present Dactyl, a human-like robot hand trained entirely in simulation with reinforcement learning that has achieved unprecedented dexterity on a physical robot. I will also present our results on unsupervised learning in language, that show that pre-training and finetuning can achieve a significant improvement over state of the art. Finally, I will present an overview of the historical progress in the field.
Mario Munich at AI Frontiers : Consumer robotics: embedding affordable AI in ...AI Frontiers
The availability of affordable electronics components, powerful embedded microprocessors, and ubiquitous internet access and WiFi in the household has enabled a new generation of connected consumer robots. In 2015, iRobot launched the Roomba 980, introducing intelligent visual navigation to its successful line of vacuum cleaning robots. In 2018, iRobot launched the Roomba i7, equipped with the latest mapping and navigation technology that provides spatial information to the broader ecosystem of connected devices in the home. In this talk, I will describe the challenges and the potential of introducing consumer robots capable of developing spatial context by exploring the physical space of the home, and I will elaborate on the impact of AI in the future of robotics applications. Moreover, I will describe our vision of the Smart Home, an AI-powered home that maintains itself and magically just does the right thing in anticipation of occupant needs. This home will be built on an ecosystem of connected and coordinated robots, sensors, and devices that provides the occupants with a high quality of life by seamlessly responding to the needs of daily living – from comfort to convenience to security to efficiency.
Anima Anandkumar at AI Frontiers : Modern ML : Deep, distributed, Multi-dimen...AI Frontiers
As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. SignSGD is a gradient compression algorithm that only transmits the sign of the stochastic gradients during distributed training. This algorithm uses 32 times less communication per iteration than distributed SGD. We show that signSGD obtains free lunch both in theory and practice: no loss in accuracy while yielding speedups. Pushing the current boundaries of deep learning also requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. These functionalities are available in the Tensorly package with multiple backend interfaces for large-scale deep learning.
Sumit Gupta at AI Frontiers : AI for EnterpriseAI Frontiers
The use of AI for voice search and image recognition is talked about often. Enterprises, however, have different challenges and requirements. In this talk, we will focus on talking about use cases in the enterprise and challenges in building out AI solutions. We will talk about how an Auto-machine learning software for videos and images called PowerAI Vision enables quick AI model training & deployment for various enterprise use cases.
Yuandong Tian at AI Frontiers : Planning in Reinforcement LearningAI Frontiers
Deep Reinforcement Learning (DRL) has made strong progress in many tasks, such as board games, robotics, navigation, neural architecture search, etc. I will present our recent open-sourced DRL frameworks to facilitate game research and development. Our framework is scalable so we can can reproduce AlphaGoZero and AlphaZero using 2000 GPUs, achieving super-human performance of Go AI that beats 4 top-30 professional players. We also show usability of our platform by training agents in real-time strategy games, and show interesting behaviors with a small amount of resource.
Alex Ermolaev at AI Frontiers : Major Applications of AI in HealthcareAI Frontiers
The latest AI advances have the potential to massively improve our health and well being. However, most of the work is yet to be done. In this talk, we will explore the most important opportunities for AI in healthcare. For example, we will explore how AI can diagnose major life-threatening conditions even before those conditions emerge. We will talk about AI ability to recommend dramatically more effective and less harmful treatment plans based on AI understanding of patient's medical history and current conditions. Finally, we will talk about AI role in making our healthcare system effective and affordable for everyone.
Long Lin at AI Frontiers : AI in GamingAI Frontiers
Games have been leveraging AI since the 1950s, when people built a rules-based AI engine that played tic-tac-toe. With technological advances over the years, AI has become increasingly popular and widely used in the gaming industry. The typical characteristics of games and game development makes them an ideal playground for practicing and implementing AI techniques, especially deep learning and reinforcement learning. Most games are well scoped; it is relatively easy to generate and use the data; and states/actions/rewards are relatively clear. In this talk, I will show a couple of use cases where ML/AI helps in-game development and enhances player experience. Examples include AI agents playing game and services that provide personalized experience to players.
Melissa Goldman at AI Frontiers : AI & FinanceAI Frontiers
AI in finance is having wide-ranging impact and solving some of the most critical societal problems. The talk gives overview of the opportunities of applying AI in finance with specific examples and highlights some of the unique challenges financial services firms face in deploying AI at scale.
Li Deng at AI Frontiers : From Modeling Speech/Language to Modeling Financial...AI Frontiers
I will first survey how deep learning has disrupted speech and language processing industries since 2009. Then I will draw connections between the techniques for modeling speech and language and those for financial markets. Finally, I will address three unique technical challenges to financial investment.
Ashok Srivastava at AI Frontiers : Using AI to Solve Complex Economic ProblemsAI Frontiers
Nearly half of all small businesses fail within their first 5 years. However, AI-driven solutions can help solve complex economic problems for consumers and small businesses like missed bill payments, insufficient capital, overinvestment in fixed assets, and more.
Ashok Srivastava discusses technology's role in solving economic problems and details how Intuit is using its unrivaled financial dataset to power prosperity around the world. Leveraging technology enablers like deep learning, natural language processing, and automated reasoning and combining with a delightful end-user experience and sophisticated UX, Intuit is using technology to help its users have more confidence in their financial management.
Rohit Tripathi at AI Frontiers : Using intelligent connectivity and AI to tra...AI Frontiers
We are amidst significant improvements in sensors and device capabilities coupled with enhancements in AI together with promise of low latency, high speed connectivity and availability of options e.g. 5G, NB-IoT. The confluence of these three trends has the potential to transform and simplify the world of IoT. This talk will explore these trends and the related impact to the world of IoT.
Kai-Fu Lee at AI Frontiers : The Era of Artificial IntelligenceAI Frontiers
In this talk, I will talk about the four waves of Artificial Intelligence (AI) , and how AI will permeate every part of our lives in the next decade. I will also talk about how this will be different from previous technology revolutions -- it will be faster and be driven by not one superpower, but two (US and China). AI will add $16 trillion to our global GDP, but also cause many challenges that will be hard to solve. I will talk in particular about AI replacing routine jobs -- the consequences, the proposed solutions that don't work (such as UBI), and end with a blueprint of co-existence between humans and AI.
Jay Yagnik at AI Frontiers : A History Lesson on AIAI Frontiers
We have reached a remarkable point in history with the evolution of AI, from applying this technology to incredible use cases in healthcare, to addressing the world's biggest humanitarian and environmental issues. Our ability to learn task-specific functions for vision, language, sequence and control tasks is getting better at a rapid pace. This talk will survey some of the current advances in AI, compare AI to other fields that have historically developed over time, and calibrate where we are in the relative advancement timeline. We will also speculate about the next inflection points and capabilities that AI can offer down the road, and look at how those might intersect with other emergent fields, e.g. Quantum computing.
Rajarshi Gupta at AI Frontiers : Security is AI’s biggest challenge, AI is Se...AI Frontiers
The progress of AI in the last decade has seemed almost magical. But we will discuss the unique challenges posed by Security and what makes this domain the biggest challenge for AI. Reporting from the frontlines, we will describe the deployment of large-scale production-grade AI systems to combat security breaches, using lessons learned at Avast from defending over 400 million consumers every single day. Topics will cover the recent AI advancements in file-based anti-malware solutions, behavior-based on-device solutions, and network-based IoT security solutions.
Sumit Gulwani at AI Frontiers : Programming by ExamplesAI Frontiers
Programming by examples (PBE) is a new frontier in AI that enables users to create scripts from input-output examples. A killer application is in the space of data wrangling to automate tasks like string/number/date transformations (e.g., converting “FirstName LastName” to “LastName, FirstName”), column splitting, table extraction from log-files, webpages, and PDFs, normalizing semi-structured spreadsheets into structured tables, transforming JSON from one format to another, etc. This presentation will educate the audience about this new PBE-based programming paradigm: its applications, form factors inside different products, and the science behind it.
Yazann Romahi at AI Frontiers : The Pitfalls of Using AI in Financial InvestingAI Frontiers
Because of the success of momentum based strategies, most AI practitioners come into finance thinking they can achieve easy wins by applying AI to time series analysis. We outline how this can be a trap, and other common misconceptions about AI in finance. We discuss the value of new sources of data and how we have used them successfully. By way of example we walk through an application of natural language processing to enhance our equity long/short and event driven hedge fund strategies.
Percy Liang at AI Frontiers : Pushing the Limits of Machine LearningAI Frontiers
In recent years, machine learning has undoubtedly been hugely successful in driving progress in AI applications. However, as we will explore in this talk, even state-of-the-art systems have "blind spots" which make them generalize poorly out of domain and render them vulnerable to adversarial examples. We then suggest that more unsupervised learning settings can encourage the development of more robust systems. We show positive results on two tasks: (i) text style and attribute transfer, the task of converting a sentence with one attribute (e.g., sentiment) to one with another; and (ii) solving SAT instances (classical problems requiring logical reasoning) using end-to-end neural networks.
Ilya Sutskever at AI Frontiers : Progress towards the OpenAI missionAI Frontiers
I will present several advances in deep learning from OpenAI. First, I will present OpenAI Five, a neural network that learned to play on par with some of the strongest professional Dota 2 teams in the world in an 18-hero version of the game. Next, I will present Dactyl, a human-like robot hand trained entirely in simulation with reinforcement learning that has achieved unprecedented dexterity on a physical robot. I will also present our results on unsupervised learning in language, that show that pre-training and finetuning can achieve a significant improvement over state of the art. Finally, I will present an overview of the historical progress in the field.
Mario Munich at AI Frontiers : Consumer robotics: embedding affordable AI in ...AI Frontiers
The availability of affordable electronics components, powerful embedded microprocessors, and ubiquitous internet access and WiFi in the household has enabled a new generation of connected consumer robots. In 2015, iRobot launched the Roomba 980, introducing intelligent visual navigation to its successful line of vacuum cleaning robots. In 2018, iRobot launched the Roomba i7, equipped with the latest mapping and navigation technology that provides spatial information to the broader ecosystem of connected devices in the home. In this talk, I will describe the challenges and the potential of introducing consumer robots capable of developing spatial context by exploring the physical space of the home, and I will elaborate on the impact of AI in the future of robotics applications. Moreover, I will describe our vision of the Smart Home, an AI-powered home that maintains itself and magically just does the right thing in anticipation of occupant needs. This home will be built on an ecosystem of connected and coordinated robots, sensors, and devices that provides the occupants with a high quality of life by seamlessly responding to the needs of daily living – from comfort to convenience to security to efficiency.
Anima Anandkumar at AI Frontiers : Modern ML : Deep, distributed, Multi-dimen...AI Frontiers
As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. SignSGD is a gradient compression algorithm that only transmits the sign of the stochastic gradients during distributed training. This algorithm uses 32 times less communication per iteration than distributed SGD. We show that signSGD obtains free lunch both in theory and practice: no loss in accuracy while yielding speedups. Pushing the current boundaries of deep learning also requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. These functionalities are available in the Tensorly package with multiple backend interfaces for large-scale deep learning.
Sumit Gupta at AI Frontiers : AI for EnterpriseAI Frontiers
The use of AI for voice search and image recognition is talked about often. Enterprises, however, have different challenges and requirements. In this talk, we will focus on talking about use cases in the enterprise and challenges in building out AI solutions. We will talk about how an Auto-machine learning software for videos and images called PowerAI Vision enables quick AI model training & deployment for various enterprise use cases.
Yuandong Tian at AI Frontiers : Planning in Reinforcement LearningAI Frontiers
Deep Reinforcement Learning (DRL) has made strong progress in many tasks, such as board games, robotics, navigation, neural architecture search, etc. I will present our recent open-sourced DRL frameworks to facilitate game research and development. Our framework is scalable so we can can reproduce AlphaGoZero and AlphaZero using 2000 GPUs, achieving super-human performance of Go AI that beats 4 top-30 professional players. We also show usability of our platform by training agents in real-time strategy games, and show interesting behaviors with a small amount of resource.
Alex Ermolaev at AI Frontiers : Major Applications of AI in HealthcareAI Frontiers
The latest AI advances have the potential to massively improve our health and well being. However, most of the work is yet to be done. In this talk, we will explore the most important opportunities for AI in healthcare. For example, we will explore how AI can diagnose major life-threatening conditions even before those conditions emerge. We will talk about AI ability to recommend dramatically more effective and less harmful treatment plans based on AI understanding of patient's medical history and current conditions. Finally, we will talk about AI role in making our healthcare system effective and affordable for everyone.
Long Lin at AI Frontiers : AI in GamingAI Frontiers
Games have been leveraging AI since the 1950s, when people built a rules-based AI engine that played tic-tac-toe. With technological advances over the years, AI has become increasingly popular and widely used in the gaming industry. The typical characteristics of games and game development makes them an ideal playground for practicing and implementing AI techniques, especially deep learning and reinforcement learning. Most games are well scoped; it is relatively easy to generate and use the data; and states/actions/rewards are relatively clear. In this talk, I will show a couple of use cases where ML/AI helps in-game development and enhances player experience. Examples include AI agents playing game and services that provide personalized experience to players.
Melissa Goldman at AI Frontiers : AI & FinanceAI Frontiers
AI in finance is having wide-ranging impact and solving some of the most critical societal problems. The talk gives overview of the opportunities of applying AI in finance with specific examples and highlights some of the unique challenges financial services firms face in deploying AI at scale.
Li Deng at AI Frontiers : From Modeling Speech/Language to Modeling Financial...AI Frontiers
I will first survey how deep learning has disrupted speech and language processing industries since 2009. Then I will draw connections between the techniques for modeling speech and language and those for financial markets. Finally, I will address three unique technical challenges to financial investment.
Ashok Srivastava at AI Frontiers : Using AI to Solve Complex Economic ProblemsAI Frontiers
Nearly half of all small businesses fail within their first 5 years. However, AI-driven solutions can help solve complex economic problems for consumers and small businesses like missed bill payments, insufficient capital, overinvestment in fixed assets, and more.
Ashok Srivastava discusses technology's role in solving economic problems and details how Intuit is using its unrivaled financial dataset to power prosperity around the world. Leveraging technology enablers like deep learning, natural language processing, and automated reasoning and combining with a delightful end-user experience and sophisticated UX, Intuit is using technology to help its users have more confidence in their financial management.
Rohit Tripathi at AI Frontiers : Using intelligent connectivity and AI to tra...AI Frontiers
We are amidst significant improvements in sensors and device capabilities coupled with enhancements in AI together with promise of low latency, high speed connectivity and availability of options e.g. 5G, NB-IoT. The confluence of these three trends has the potential to transform and simplify the world of IoT. This talk will explore these trends and the related impact to the world of IoT.
Kai-Fu Lee at AI Frontiers : The Era of Artificial IntelligenceAI Frontiers
In this talk, I will talk about the four waves of Artificial Intelligence (AI) , and how AI will permeate every part of our lives in the next decade. I will also talk about how this will be different from previous technology revolutions -- it will be faster and be driven by not one superpower, but two (US and China). AI will add $16 trillion to our global GDP, but also cause many challenges that will be hard to solve. I will talk in particular about AI replacing routine jobs -- the consequences, the proposed solutions that don't work (such as UBI), and end with a blueprint of co-existence between humans and AI.
Jay Yagnik at AI Frontiers : A History Lesson on AIAI Frontiers
We have reached a remarkable point in history with the evolution of AI, from applying this technology to incredible use cases in healthcare, to addressing the world's biggest humanitarian and environmental issues. Our ability to learn task-specific functions for vision, language, sequence and control tasks is getting better at a rapid pace. This talk will survey some of the current advances in AI, compare AI to other fields that have historically developed over time, and calibrate where we are in the relative advancement timeline. We will also speculate about the next inflection points and capabilities that AI can offer down the road, and look at how those might intersect with other emergent fields, e.g. Quantum computing.
Rajarshi Gupta at AI Frontiers : Security is AI’s biggest challenge, AI is Se...AI Frontiers
The progress of AI in the last decade has seemed almost magical. But we will discuss the unique challenges posed by Security and what makes this domain the biggest challenge for AI. Reporting from the frontlines, we will describe the deployment of large-scale production-grade AI systems to combat security breaches, using lessons learned at Avast from defending over 400 million consumers every single day. Topics will cover the recent AI advancements in file-based anti-malware solutions, behavior-based on-device solutions, and network-based IoT security solutions.
Sumit Gulwani at AI Frontiers : Programming by ExamplesAI Frontiers
Programming by examples (PBE) is a new frontier in AI that enables users to create scripts from input-output examples. A killer application is in the space of data wrangling to automate tasks like string/number/date transformations (e.g., converting “FirstName LastName” to “LastName, FirstName”), column splitting, table extraction from log-files, webpages, and PDFs, normalizing semi-structured spreadsheets into structured tables, transforming JSON from one format to another, etc. This presentation will educate the audience about this new PBE-based programming paradigm: its applications, form factors inside different products, and the science behind it.
Yazann Romahi at AI Frontiers : The Pitfalls of Using AI in Financial InvestingAI Frontiers
Because of the success of momentum based strategies, most AI practitioners come into finance thinking they can achieve easy wins by applying AI to time series analysis. We outline how this can be a trap, and other common misconceptions about AI in finance. We discuss the value of new sources of data and how we have used them successfully. By way of example we walk through an application of natural language processing to enhance our equity long/short and event driven hedge fund strategies.
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/
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
19. Challenges: Context
The old Welshman came home toward daylight, spattered
with candle-grease, smeared with clay, and almost worn
out. He found Huck still in the bed that had been provided
for him, and delirious with fever. The physicians were all at
the cave, so the Widow Douglas came and took charge of
the patient.
—The Adventures of Tom Sawyer, Mark Twain
20. Process
“Mary went back home. …”
Transform
Analyze
Predict
Present
{<“mary”, “go”, “home”>, … }
{<0.4, 0.8, 0.3, 0.1, 0.7>, …}
34. Bag of Words
“Little House on the Prairie” {“littl”, “hous”, “prairi”}
“Mary had a Little Lamb” {“mari”, “littl”, “lamb”}
“The Silence of the Lambs” {“silenc”, “lamb”}
“Twinkle Twinkle Little Star” {“twinkl”, “littl”, “star”} ?
35. Bag of Words
littl hous prairi mari
lamb silenc twinkl star
“Little House on the Prairie”
“Mary had a Little Lamb”
“The Silence of the Lambs”
“Twinkle Twinkle Little Star”
corpus (D) vocabulary (V)
36. Bag of Words
littl hous prairi mari lamb silenc twinkl star
“Little House on the Prairie”
“Mary had a Little Lamb”
“The Silence of the Lambs”
“Twinkle Twinkle Little Star”
37. 1 1 1 0 0 0 0 0
1 0 0 1 1 0 0 0
0 0 0 0 1 1 0 0
1 0 0 0 0 0 2 1
Bag of Words
littl hous prairi mari lamb silenc twinkl star
“Little House on the Prairie”
“Mary had a Little Lamb”
“The Silence of the Lambs”
“Twinkle Twinkle Little Star”
Document-Term Matrix term frequency
38. Document Similarity
1 1 1 0 0 0 0 0
1 0 0 1 1 0 0 0
“Little House on the Prairie”
“Mary had a Little Lamb”
littl hous prairi mari lamb silenc twinkl star
a
b
a·b = ∑ a0b0 + a1b1 + … + anbn = 1 + 0 + 0 + 0 + 0 + 0 + 0 + 01 dot product
39. Document Similarity
1 1 1 0 0 0 0 0
1 0 0 1 1 0 0 0
“Little House on the Prairie”
“Mary had a Little Lamb”
littl hous prairi mari lamb silenc twinkl star
a
b
a·b = ∑ a0b0 + a1b1 + … + anbn = 1 dot product
cos(θ) =
a·b
‖a‖·‖b‖ √3×√3
=
1
=
1
3
cosine similarity
40. 1 1 1 0 0 0 0 0
1 0 0 1 1 0 0 0
0 0 0 0 1 1 0 0
1 0 0 0 0 0 2 1
3 1 1 1 2 1 1 1
Term Specificity
littl hous prairi mari lamb silenc twinkl star
“Little House on the Prairie”
“Mary had a Little Lamb”
“The Silence of the Lambs”
“Twinkle Twinkle Little Star”
document frequency
/3
/3
/3
/3
/1
/1
/1
/1
/1
/1
/1
/1
/1
/1
/1
/1
/2
/2
/2
/2
/1
/1
/1
/1
/1
/1
/1
/1
/1
/1
/1
/1
41. Term Specificity
1/3 1 1 0 0 0 0 0
1/3 0 0 1 1/2 0 0 0
0 0 0 0 1/2 1 0 0
1/3 0 0 0 0 0 2 1
littl hous prairi mari lamb silenc twinkl star
“Little House on the Prairie”
“Mary had a Little Lamb”
“The Silence of the Lambs”
“Twinkle Twinkle Little Star”
42. TF-IDF
tfidf(t, d, D) = tf(t, d) · idf(t, D)
term frequency
inverse document frequency
count(t, d)/|d|
log(|D|/|{d∈D : t∈d}|)
62. LDA: Parameter Estimation
Choose |Z| = k
Initialize distribution parameters
Sample <document, words>
Update distribution parameters
expectation
maximization
63. LDA: Use Cases
• Topic modeling, document categorization.
• Mixture of topics in a new document: P(z | w, α, β)
• Generate collections of words with desired mixture.
64. LDA: Further Reading
David Blei, Andrew Ng, Michael Jordan, 2003. Latent Dirichlet Allocation,
In Journal of Machine Learning Research, vol. 3, pp. 993-102.
Thomas Boggs, 2014. Visualizing Dirichlet Distributions with matplotlib.
115. One-hot encoding
What a great movie!
aardvark
zygote
a
great
m
ovie
rhinoceros
cookie
figurine
w
hat
1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0
a
great
m
ovie
w
hat
117. 2. Examining the data
What a great movie!
aardvark
zygote
a
great
m
ovie
rhinoceros
cookie
figurine
w
hat
1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 17 18 19 20 21 22 23 24 25
a
great
m
ovie
w
hat
[1, 7, 15, 23]
118. 3. One-hot encoding the input
What a great movie!
aardvark
zygote
a
great
m
ovie
rhinoceros
cookie
figurine
w
hat
1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 17 18 19 20 21 22 23 24 25
a
great
m
ovie
w
hat
[1, 7, 15, 23]
1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0
127. One-hot encoding
“I expected this movie to be much better”
“This movie is much better than I expected”
I
expected
m
ovie
to
1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 1 0 0 1 0
be
better
m
uch
is
this
I
expected
m
ovie
than
1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0
be
better
m
uch
this
143. Word2Vec: Recap
• Robust, distributed representation.
• Vector size independent of vocabulary.
• Train once, store in lookup table.
• Deep learning ready!
144. Word2Vec: Further Reading
Tomas Mikolov, et al., 2013. Distributed Representation of Words and
Phrases and their Compositionality, In Advances of Neural Information
Processing Systems (NIPS), pp. 3111-3119.
Adrian Colyer, 2016. The amazing power of word vectors.