In this presentation we discuss the hypothesis of MaxEnt models, describe the role of feature functions and their applications to Natural Language Processing (NLP). The training of the classifier is discussed in a later presentation.
This is the first lecture on Applied Machine Learning. The course focuses on the emerging and modern aspects of this subject such as Deep Learning, Recurrent and Recursive Neural Networks (RNN), Long Short Term Memory (LSTM), Convolution Neural Networks (CNN), Hidden Markov Models (HMM). It deals with several application areas such as Natural Language Processing, Image Understanding etc. This presentation provides the landscape.
Artificial Intelligence Course: Linear models ananth
In this presentation we present the linear models: Regression and Classification. We illustrate with several examples. Concepts such as underfitting (Bias) and overfitting (Variance) are presented. Linear models can be used as stand alone classifiers for simple cases and they are essential building blocks as a part of larger deep learning networks
Deep Learning For Practitioners, lecture 2: Selecting the right applications...ananth
In this presentation we articulate when deep learning techniques yield best results from a practitioner's view point. Do we apply deep learning techniques for every machine learning problem? What characteristics of an application lends itself suitable for deep learning? Does more data automatically imply better results regardless of the algorithm or model? Does "automated feature learning" obviate the need for data preprocessing and feature design?
Artificial Neural Networks have been very successfully used in several machine learning applications. They are often the building blocks when building deep learning systems. We discuss the hypothesis, training with backpropagation, update methods, regularization techniques.
Deep Learning in Recommender Systems - RecSys Summer School 2017Balázs Hidasi
This is the presentation accompanying my tutorial about deep learning methods in the recommender systems domain. The tutorial consists of a brief general overview of deep learning and the introduction of the four most prominent research direction of DL in recsys as of 2017. Presented during RecSys Summer School 2017 in Bolzano, Italy.
Discusses the concept of Language Models in Natural Language Processing. The n-gram models, markov chains are discussed. Smoothing techniques such as add-1 smoothing, interpolation and discounting methods are addressed.
This presentation is a part of ML Course and this deals with some of the basic concepts such as different types of learning, definitions of classification and regression, decision surfaces etc. This slide set also outlines the Perceptron Learning algorithm as a starter to other complex models to follow in the rest of the course.
This is the first lecture on Applied Machine Learning. The course focuses on the emerging and modern aspects of this subject such as Deep Learning, Recurrent and Recursive Neural Networks (RNN), Long Short Term Memory (LSTM), Convolution Neural Networks (CNN), Hidden Markov Models (HMM). It deals with several application areas such as Natural Language Processing, Image Understanding etc. This presentation provides the landscape.
Artificial Intelligence Course: Linear models ananth
In this presentation we present the linear models: Regression and Classification. We illustrate with several examples. Concepts such as underfitting (Bias) and overfitting (Variance) are presented. Linear models can be used as stand alone classifiers for simple cases and they are essential building blocks as a part of larger deep learning networks
Deep Learning For Practitioners, lecture 2: Selecting the right applications...ananth
In this presentation we articulate when deep learning techniques yield best results from a practitioner's view point. Do we apply deep learning techniques for every machine learning problem? What characteristics of an application lends itself suitable for deep learning? Does more data automatically imply better results regardless of the algorithm or model? Does "automated feature learning" obviate the need for data preprocessing and feature design?
Artificial Neural Networks have been very successfully used in several machine learning applications. They are often the building blocks when building deep learning systems. We discuss the hypothesis, training with backpropagation, update methods, regularization techniques.
Deep Learning in Recommender Systems - RecSys Summer School 2017Balázs Hidasi
This is the presentation accompanying my tutorial about deep learning methods in the recommender systems domain. The tutorial consists of a brief general overview of deep learning and the introduction of the four most prominent research direction of DL in recsys as of 2017. Presented during RecSys Summer School 2017 in Bolzano, Italy.
Discusses the concept of Language Models in Natural Language Processing. The n-gram models, markov chains are discussed. Smoothing techniques such as add-1 smoothing, interpolation and discounting methods are addressed.
This presentation is a part of ML Course and this deals with some of the basic concepts such as different types of learning, definitions of classification and regression, decision surfaces etc. This slide set also outlines the Perceptron Learning algorithm as a starter to other complex models to follow in the rest of the course.
Generative Adversarial Networks : Basic architecture and variantsananth
In this presentation we review the fundamentals behind GANs and look at different variants. We quickly review the theory such as the cost functions, training procedure, challenges and go on to look at variants such as CycleGAN, SAGAN etc.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Naive Bayes Classifier is a machine learning technique that is exceedingly useful to address several classification problems. It is often used as a baseline classifier to benchmark results. It is also used as a standalone classifier for tasks such as spam filtering where the naive assumption (conditional independence) made by the classifier seem reasonable. In this presentation we discuss the mathematical basis for the Naive Bayes and illustrate with examples
This presentation discusses decision trees as a machine learning technique. This introduces the problem with several examples: cricket player selection, medical C-Section diagnosis and Mobile Phone price predictor. It discusses the ID3 algorithm and discusses how the decision tree is induced. The definition and use of the concepts such as Entropy, Information Gain are discussed.
Mathematical Background for Artificial Intelligenceananth
Mathematical background is essential for understanding and developing AI and Machine Learning applications. In this presentation we give a brief tutorial that encompasses basic probability theory, distributions, mixture models, anomaly detection, graphical representations such as Bayesian Networks, etc.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Deep Learning Enabled Question Answering System to Automate Corporate HelpdeskSaurabh Saxena
Studied feasibility of applying state-of-the-art deep learning models like end-to-end memory networks and neural attention- based models to the problem of machine comprehension and subsequent question answering in corporate settings with huge
amount of unstructured textual data. Used pre-trained embeddings like word2vec and GLove to avoid huge training costs.
With the explosive growth of online information, recommender system has been an effective tool to overcome information overload and promote sales. In recent years, deep learning's revolutionary advances in speech recognition, image analysis and natural language processing have gained significant attention. Meanwhile, recent studies also demonstrate its efficacy in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performance. In this talk, I will present recent development of deep learning based recommender models and highlight some future challenges and open issues of this research field.
안녕하세요 딥러닝 논문읽기 모임입니다 지난달 구글에서 발표한, 'H-Transformer-1D Paper : Fast One Dimensional Hierarchical Attention For Sequences review!!' 라는 제목의 논문입니다!
제목에서 알 수 있듯, 논문은 시퀀스 처리를 위한 1차원 Hierarchical Attention인데
알고리즘이 더 빠르다라고 언급하고 있는 논문입니다
논문 제목에서 보시다시피 이제 SelfAttention, 트랜스포머의 전신이죠
셀프Attention이 쿼드라틱 컴프레시티를 가진다는 거는
너무나도 많이 알려진 사실이고 이것을 해결하기 위한 다양한 논문들이 많이 나왔습니다 이 논문도 그러한 연구의 연장선상에 있는 한 논문인데요
이 논문 같은 경우에는 Attention 매트릭스를 로우 스트럭처로 근사하는데
또 이런 수치해석적인 기법을 적용해보자 라는 컨셉에서 나온 논문입니다.
오늘 논문 리뷰를 위해 자연어 처리 진명훈님이 자세한 리뷰 도와주셨습니다!
오늘도 많은 관심 미리 감사드립니다!
This is slides used at Arithmer seminar given by Dr. Masaaki Uesaka at Arithmer inc.
It is a summary of recent methods for quality assurance of machine learning model.
Arithmer Seminar is weekly held, where professionals from within our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Generating Natural-Language Text with Neural NetworksJonathan Mugan
Automatic text generation enables computers to summarize text, to have conversations in customer-service and other settings, and to customize content based on the characteristics and goals of the human interlocutor. Using neural networks to automatically generate text is appealing because they can be trained through examples with no need to manually specify what should be said when. In this talk, we will provide an overview of the existing algorithms used in neural text generation, such as sequence2sequence models, reinforcement learning, variational methods, and generative adversarial networks. We will also discuss existing work that specifies how the content of generated text can be determined by manipulating a latent code. The talk will conclude with a discussion of current challenges and shortcomings of neural text generation.
This was presented to software developers with the goal of introducing them to basic machine learning workflow, code snippets, possibilities and state-of-the-art in NLP and give some clues on where to get started.
Lecture 2 Basic Concepts in Machine Learning for Language TechnologyMarina Santini
Definition of Machine Learning
Type of Machine Learning:
Classification
Regression
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning:
Supervised Classification
Training set
Hypothesis class
Empirical error
Margin
Noise
Inductive bias
Generalization
Model assessment
Cross-Validation
Classification in NLP
Types of Classification
Generative Adversarial Networks : Basic architecture and variantsananth
In this presentation we review the fundamentals behind GANs and look at different variants. We quickly review the theory such as the cost functions, training procedure, challenges and go on to look at variants such as CycleGAN, SAGAN etc.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Naive Bayes Classifier is a machine learning technique that is exceedingly useful to address several classification problems. It is often used as a baseline classifier to benchmark results. It is also used as a standalone classifier for tasks such as spam filtering where the naive assumption (conditional independence) made by the classifier seem reasonable. In this presentation we discuss the mathematical basis for the Naive Bayes and illustrate with examples
This presentation discusses decision trees as a machine learning technique. This introduces the problem with several examples: cricket player selection, medical C-Section diagnosis and Mobile Phone price predictor. It discusses the ID3 algorithm and discusses how the decision tree is induced. The definition and use of the concepts such as Entropy, Information Gain are discussed.
Mathematical Background for Artificial Intelligenceananth
Mathematical background is essential for understanding and developing AI and Machine Learning applications. In this presentation we give a brief tutorial that encompasses basic probability theory, distributions, mixture models, anomaly detection, graphical representations such as Bayesian Networks, etc.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Deep Learning Enabled Question Answering System to Automate Corporate HelpdeskSaurabh Saxena
Studied feasibility of applying state-of-the-art deep learning models like end-to-end memory networks and neural attention- based models to the problem of machine comprehension and subsequent question answering in corporate settings with huge
amount of unstructured textual data. Used pre-trained embeddings like word2vec and GLove to avoid huge training costs.
With the explosive growth of online information, recommender system has been an effective tool to overcome information overload and promote sales. In recent years, deep learning's revolutionary advances in speech recognition, image analysis and natural language processing have gained significant attention. Meanwhile, recent studies also demonstrate its efficacy in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performance. In this talk, I will present recent development of deep learning based recommender models and highlight some future challenges and open issues of this research field.
안녕하세요 딥러닝 논문읽기 모임입니다 지난달 구글에서 발표한, 'H-Transformer-1D Paper : Fast One Dimensional Hierarchical Attention For Sequences review!!' 라는 제목의 논문입니다!
제목에서 알 수 있듯, 논문은 시퀀스 처리를 위한 1차원 Hierarchical Attention인데
알고리즘이 더 빠르다라고 언급하고 있는 논문입니다
논문 제목에서 보시다시피 이제 SelfAttention, 트랜스포머의 전신이죠
셀프Attention이 쿼드라틱 컴프레시티를 가진다는 거는
너무나도 많이 알려진 사실이고 이것을 해결하기 위한 다양한 논문들이 많이 나왔습니다 이 논문도 그러한 연구의 연장선상에 있는 한 논문인데요
이 논문 같은 경우에는 Attention 매트릭스를 로우 스트럭처로 근사하는데
또 이런 수치해석적인 기법을 적용해보자 라는 컨셉에서 나온 논문입니다.
오늘 논문 리뷰를 위해 자연어 처리 진명훈님이 자세한 리뷰 도와주셨습니다!
오늘도 많은 관심 미리 감사드립니다!
This is slides used at Arithmer seminar given by Dr. Masaaki Uesaka at Arithmer inc.
It is a summary of recent methods for quality assurance of machine learning model.
Arithmer Seminar is weekly held, where professionals from within our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Generating Natural-Language Text with Neural NetworksJonathan Mugan
Automatic text generation enables computers to summarize text, to have conversations in customer-service and other settings, and to customize content based on the characteristics and goals of the human interlocutor. Using neural networks to automatically generate text is appealing because they can be trained through examples with no need to manually specify what should be said when. In this talk, we will provide an overview of the existing algorithms used in neural text generation, such as sequence2sequence models, reinforcement learning, variational methods, and generative adversarial networks. We will also discuss existing work that specifies how the content of generated text can be determined by manipulating a latent code. The talk will conclude with a discussion of current challenges and shortcomings of neural text generation.
This was presented to software developers with the goal of introducing them to basic machine learning workflow, code snippets, possibilities and state-of-the-art in NLP and give some clues on where to get started.
Lecture 2 Basic Concepts in Machine Learning for Language TechnologyMarina Santini
Definition of Machine Learning
Type of Machine Learning:
Classification
Regression
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning:
Supervised Classification
Training set
Hypothesis class
Empirical error
Margin
Noise
Inductive bias
Generalization
Model assessment
Cross-Validation
Classification in NLP
Types of Classification
Overview of TensorFlow For Natural Language Processingananth
TensorFlow open sourced recently by Google is one of the key frameworks that support development of deep learning architectures. In this slideset, part 1, we get started with a few basic primitives of TensorFlow. We will also discuss when and when not to use TensorFlow.
In this presentation we discuss several concepts that include Word Representation using SVD as well as neural networks based techniques. In addition we also cover core concepts such as cosine similarity, atomic and distributed representations.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
This Project Aimed at doing a comprehensive study of Different Machine Learning Approaches on Sentiment Analysis of Movie Reviews. Support Vector Machines were the one that Performed Most Accurately with Radial Basis Function. Lots of Other kernel functions and Kernel Parameters were tried to find the optimal one. We achieved accuracy up to 83%.
This presentation is about Sentiment analysis Using Machine Learning which is a modern way to perform sentiment analysis operation. it has various techniques and algorithm described and compared for SA
The discrete or atomic representation of words don't scale well to support rich semantics. Distributed representations associate a word with a vector based on the context in which the word occurs. In this presentation we describe the problem of word representation with a few illustrations and also describe the approach taken by word2vec. We also discuss the limitations of using a static database approach.
Deep learning is receiving phenomenal attention due to breakthrough results in several AI tasks and significant research investment by top technology companies like Google, Facebook, Microsoft, IBM. For someone who has not been introduced to this technology, it may be daunting to learn several concepts such as feature learning, Restricted Boltzmann Machines, Autoencoders, etc all at once and start applying it to their own AI applications. This presentation is the first of several in this series that is intended at practitioners.
Introduction to Statistical Machine Learningmahutte
This course provides a broad introduction to the methods and practice
of statistical machine learning, which is concerned with the development
of algorithms and techniques that learn from observed data by
constructing stochastic models that can be used for making predictions
and decisions. Topics covered include Bayesian inference and maximum
likelihood modeling; regression, classi¯cation, density estimation,
clustering, principal component analysis; parametric, semi-parametric,
and non-parametric models; basis functions, neural networks, kernel
methods, and graphical models; deterministic and stochastic
optimization; over¯tting, regularization, and validation.
Reference Scope Identification of Citances Using Convolutional Neural NetworkSaurav Jha
In the task of summarization of a scientific paper, a lot of information stands to be gained about a reference paper, from the papers that cite it. Automatically generating the reference scope (the span of cited text) in a reference paper, corresponding to citances (sentences in the citing papers that cite it) has great significance in preparing a structured summary of the reference paper. We treat this task as a binary classification problem, by extracting feature vectors from pairs of citances and reference sentences. These features are lexical, corpus-based, surface and knowledge-based. We extend the current feature set employed for reference-citance pair identification in the current state-of-the-art system. Using these features, we present a novel classification approach for this task, that employs a deep Convolutional Neural Network along with two boosting ensemble algorithms. We outperform the existing state-of-the- art for distinguishing between cited spans and non-cited spans of text in the reference paper.
Text Classification with Lucene/Solr, Apache Hadoop and LibSVMlucenerevolution
In this session we will show how to build a text classifier using the Apache Lucene/Solr with libSVM libraries. We classify our corpus of job offers into a number of predefined categories. Each indexed document (a job offer) then belongs to zero, one or more categories. Known machine learning techniques for text classification include naïve bayes model, logistic regression, neural network, support vector machine (SVM), etc. We use Lucene/Solr to construct the features vector. Then we use the libsvm library known as the reference implementation of the SVM model to classify the document. We construct as many one-vs-all svm classifiers as there are classes in our setting, then using the Hadoop MapReduce Framework we reconcile the result of our classifiers. The end result is a scalable multi-class classifier. Finally we outline how the classifier is used to enrich basic solr keyword search.
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
This presentation discusses the state space problem formulation and different search techniques to solve these. Techniques such as Breadth First, Depth First, Uniform Cost and A star algorithms are covered with examples. We also discuss where such techniques are useful and the limitations.
This is the first lecture of the AI course offered by me at PES University, Bangalore. In this presentation we discuss the different definitions of AI, the notion of Intelligent Agents, distinguish an AI program from a complex program such as those that solve complex calculus problems (see the integration example) and look at the role of Machine Learning and Deep Learning in the context of AI. We also go over the course scope and logistics.
Deep Learning techniques have enabled exciting novel applications. Recent advances hold lot of promise for speech based applications that include synthesis and recognition. This slideset is a brief overview that presents a few architectures that are the state of the art in contemporary speech research. These slides are brief because most concepts/details were covered using the blackboard in a classroom setting. These slides are meant to supplement the lecture.
This slide set on convolutional neural networks is meant to be supplementary material to the slides from Andrej Karpathy's course. In this slide set we explain the motivation for CNN and also describe how to understand CNN coming from a standard feed forward neural networks perspective. For detailed architecture and discussions refer the original slides. I might post more detailed slides later.
In this presentation we describe the formulation of the HMM model as consisting of states that are hidden that generate the observables. We introduce the 3 basic problems: Finding the probability of a sequence of observation given the model, the decoding problem of finding the hidden states given the observations and the model and the training problem of determining the model parameters that generate the given observations. We discuss the Forward, Backward, Viterbi and Forward-Backward algorithms.
Natural Language Processing: L03 maths fornlpananth
This presentation discusses probability theory basics, Naive Bayes Classifier with some practical examples. This also introduces graph models for representing joint probability distributions.
Words and sentences are the basic units of text. In this lecture we discuss basics of operations on words and sentences such as tokenization, text normalization, tf-idf, cosine similarity measures, vector space models and word representation
Natural Language Processing: L01 introductionananth
This presentation introduces the course Natural Language Processing (NLP) by enumerating a number of applications, course positioning, challenges presented by Natural Language text and emerging approaches to topics like word representation.
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
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
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.
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
2. MaxEnt Classifier
• This is a powerful model that has equivalence to logistic regression
• Many NLP problems can be reformulated as classification problems
• E.g. Language Modelling, Tagging Problems
• MaxEnt is widely used for various text processing tasks.
• Task is to estimate the probability of a class given the context
• The term context may refer to a single word or group of words
• In a large text corpora contains information on the cooccurrence of
classes and specific contexts
3. Problem: MaxEnt (Refer paper by
Ratnaparkhi)
• Let p(a, b) be the probability of class a occurring with context b
• Given the sparsity of words in b and also limited training data, it is not
possible to completely specify p(a, b)
• Given the sparse evidence about a’s and b’s our goal is to estimate
the probability model p(a, b)
5. Representing Evidence
• One way to represent evidence is to encode useful facts as features
and to impose constraints on the values of those feature expectations
• A feature is a binary valued function (indicator function):
• 𝑓𝑖: ε ⟶ 0, 1
• Given k features the constraints have the form:
• Expectation value of the model for the feature fj = Observed Expectation
value for the feature fj
• 𝑥∈𝜖 𝑝 𝑥 𝑓𝑗 x = 𝑥∈𝜖 𝑝 𝑥 𝑓𝑗 x
• The principle of maximum entropy requires:
6. Motivating Problems for Log-linear Models
• Language Model: Given the context (that is, words w1, w2, …, wi-1 ) predict the word wi
• Consider the examples:
• A natural number (i.e. 1, 2, 3, 4, 5, 6, etc.) is called a prime number (or a prime) if it has exactly two positive divisors, 1
and the number itself. Natural numbers greater than 1 that are not prime are called composite.
• Asked about the speculation that he may be inducted into the Cabinet, Parrikar said, “I can comment on it only after
meeting the Prime Minister. Let the Prime Minister who has invited me comment”
• Prime Minister Narendra Modi is likely to expand his Cabinet on Sunday, according to Times Now
• “The prime focus of this release of our product is to simplify the user interface”
• N-gram models
• Uses the context of previous (n-1) words to predict the nth word
• A trigram model approach uses 2 previous words
• Sometimes the accuracy can be improved if other features of the input are taken in to consideration as opposed to
using only a very limited context
• The n-gram LM techniques are not flexible enough to include additional features, such as the total length of sentence,
presence of certain specific words, identity of the author etc. Note: One might include extra features like author’s
name etc and compute conditional probabilities but such extensions to the conventional trigram approach becomes
quickly unwieldy
• Log-linear models can be used to include the additional features and improve the performance
7. The general problem
• We have an input domain X
• For example: A sequence of words
• There is a finite label set Y
• For example: The space of all possible words – that is the vocabulary
• Our goal is to determine P(y|x) for any x, y where x is in the input
space and y is in the space of labels
• For example: Given an input sentence (that is x, a sequence of words),
determine the next word in the sequence - that is P(wi | w1..wn)
8. Feature Vector
• A feature is a function fk(x, y) ∈ ℝ
• Often the features used in Log-linear models for typical NLP
applications are binary functions that are also called indicator
functions: fk(x, y) ∈ {0, 1}
• If we have m features then a feature vector f(x, y) ∈ ℝ 𝑚
• The number and choice of features for a given input is arbitrary. The
system developer can design these with an intuition of the problem
space he is addressing.
9. Features in Log-Linear Models
• Features are pieces of elementary pieces of evidence that link aspects
of what we observe x with a label y that we want to predict (Ref: C
Manning)
• A feature is a function with a bounded real value
𝑓: 𝑋 ∗ 𝑌 → ℝ
• Example:
• Consider a sentence: “Gandhi was born on 2 October 1869 in Porbandar”
• f1(x, y) = [y = PERSON and wi = isCapitalized and wi+1 = (“was” | “is”) and wi+2 =
VERB]
• f2(x, y) = [y = LOCATION and wi = isCapitalized and wi+1 = (“was” | “is”) and wi+2
= VERB]
• f3(x, y) = [y = DATE and wi = CD and wi-1 = (“on”) and wi-2 = VERB]
10. Feature Vector Representations
• Consider the examples:
• A natural number (i.e. 1, 2, 3, 4, 5, 6, etc.) is called
a prime number (or a prime) if it has exactly two
positive divisors, 1 and the number itself.
Natural numbers greater than 1 that are
not prime are called composite.
• Asked about the speculation that he may be inducted
into the Cabinet, Parrikar said, “I can comment on it
only after meeting the Prime Minister. Let the Prime
Minister who has invited me comment”
• Prime Minister Narendra Modi is likely to expand his
Cabinet on Sunday, according to Times Now
• “The prime focus of this release of our product is to
simplify the user interface”
• Exercise:
• What are the possible features we may consider for
representing the Trigram LM problem?
• How do we extend this set of trigram features in to a
more powerful set of features?
11. Parameter Vector
• Given the feature vector f(x, y) ∈ ℝ 𝑚 we can define the parameter
vector v ∈ ℝ 𝑚
• Each (x, y) is mapped to a score which is the dot product of the
parameter vector and the feature vector:
𝑣. 𝑓 𝑥, 𝑦 =
𝑘=1
𝑚
𝑣 𝑘 𝑓𝑘
12. Log-linear model - definition
• Let the Input domain X and label space Y
• Our goal is to determine P(y|x)
• A feature is a function: 𝑓: 𝑋 × 𝑌 → ℝ
• We have m features that constitute a feature vector: 𝑓 𝑥, 𝑦 ∈ ℝ 𝑚
• We also have the parameter vector: 𝑣 ∈ ℝ 𝑚
• We define the log-linear model as:
𝒑 𝒚 𝒙; 𝒗 =
𝒆 𝒗.𝒇 𝒙,𝒚
𝒚′∈𝒀
𝒆 𝒗.𝒇 𝒙,𝒚′
