Deep Learning Overview

The Netflix experience is driven by a number of Machine Learning algorithms: personalized ranking, page generation, search, similarity, ratings, etc. On the 6th of January, we simultaneously launched Netflix in 130 new countries around the world, which brings the total to over 190 countries. Preparing for such a rapid expansion while ensuring each algorithm was ready to work seamlessly created new challenges for our recommendation and search teams. In this post, we highlight the four most interesting challenges we’ve encountered in making our algorithms operate globally and, most importantly, how this improved our ability to connect members worldwide with stories they'll love.

Eugene Klyuchnikov, Business Intelligence Lead, TourRadar ~You ask, we don't answer (yet). How to build a perfect ML-based question answering model which doesn't work.~

Here are some key terms that are similar to "champagne": - Sparkling wines - French champagne - Cognac - Rosé - White wine - Sparkling wine - Wine - Burgundy - Bordeaux - Cava - Prosecco Some specific champagne brands that are similar terms include Moët, Veuve Clicquot, Dom Pérignon, Taittinger, and Bollinger. Grape varieties used in champagne production like Chardonnay and Pinot Noir could also be considered similar terms.

The document discusses pedagogical approaches for teaching primary computing. It provides objectives around the primary computing curriculum and computational thinking concepts. It then describes several unplugged activities that can be used to develop computational thinking without computers, such as writing algorithms for making sandwiches or drawing characters. Finally, it discusses strategies for teaching computing, including developing independence, paired programming, debugging, differentiation, and assessment.

This document provides an overview of machine learning including definitions of common techniques like supervised learning, unsupervised learning, and reinforcement learning. It discusses applications of machine learning across various domains like vision, natural language processing, and speech recognition. Additionally, it outlines machine learning life cycles and lists tools, technologies, and resources for learning and practicing machine learning.

This document provides information about a software development career and learning to code. It discusses the growing demand for software developers and how the number of developers has doubled every 5 years. It notes that half of developers have less than 5 years of experience. The document recommends focusing on skills, talent, character, motivation, strategy, attitude, and luck to succeed as a developer. It emphasizes the importance of lifelong learning as technologies and best practices constantly change. It also outlines different coding career paths and domains like AI, security, and blockchain.

In this talk, we present a general multi-armed bandit framework for recommendations on the Netflix homepage. We present two example case studies using MABs at Netflix - a) Artwork Personalization to recommend personalized visuals for each of our members for the different titles and b) Billboard recommendation to recommend the right title to be watched on the Billboard.

This document provides an overview of machine learning concepts including supervised learning pipelines, different classifier types, and what makes a good feature for classification. It discusses machine learning algorithms learning from examples and experience, and highlights scikit-learn as an open source machine learning library. Examples are given around classifying dog breeds based on height, showing how features can capture different types of information and the importance of avoiding redundant or useless features.

Netflix uses machine learning and algorithms to power recommendations for over 69 million members across more than 50 countries. They experiment with a wide range of algorithms including regression, matrix factorization, deep neural networks, and more. Some lessons learned are to first build an offline experimentation framework with clear metrics, consider distribution from the start, and design production code to also support experimentation. The goal is to efficiently iterate experiments and smoothly implement successful models in production.

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.

This document provides an overview of machine intelligence and its everyday applications. It discusses artificial narrow and general intelligence, machine learning approaches including supervised and unsupervised learning, and deep learning and neural networks. It also demonstrates examples of computer vision, natural language processing, machine translation and other AI applications like cancer detection, image captioning and voice synthesis. The conclusion encourages embracing AI to improve applications.

This document discusses new approaches to natural language processing systems and how they can be improved. It notes that current NLP systems have limitations in areas like translation, information retrieval, understanding context and searching for relations. It suggests that NLP systems could be enhanced by reviewing current tools, understanding how humans are able to process language more effectively, and incorporating human-like characteristics like continual learning, motivation and the ability to learn from any source. Next steps proposed include developing new ways to store and access knowledge, understanding how humans learn, and creating systems that can understand users' intentions.

Artificial intelligence and machine learning are advancing rapidly. Neural networks allow computers to learn from large amounts of data through supervised, unsupervised, and reinforcement learning. Applications include computer vision, natural language processing, adaptive websites, speech recognition, and autonomous vehicles. Advancements have been enabled by cheap parallel computing, vast data availability, improved algorithms, and cloud infrastructure. Open questions remain around how neural networks work and how to ensure AI is beneficial to humanity.

Machine learning is used extensively at Q&A sites like Quora to improve user experience. Some applications include answer ranking to determine the best answers, feed ranking to present the most interesting stories, asking users to answer questions, recommending new topics and users, detecting duplicate questions, and moderating content. Quora uses a variety of machine learning models and does extensive experimentation and A/B testing to optimize different metrics.

This document provides an introduction to machine learning. It discusses key machine learning concepts like supervised learning, unsupervised learning, reinforcement learning, batch learning, online learning, instance-based learning, and model-based learning. It also discusses applications of machine learning like spam filtering, clustering, and anomaly detection. Machine learning algorithms like artificial neural networks and deep learning are also introduced. The document aims to explain machine learning concepts and techniques in a clear and intuitive manner using examples.

Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning. This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.

Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning. This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.

This document provides an introduction to machine learning, including definitions of machine learning, why it is needed, and the main types of machine learning algorithms. It describes supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. For each type, it provides examples and brief explanations. It also discusses applications of machine learning and the differences between machine learning and deep learning.

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.

This document discusses machine learning applications and different machine learning techniques. It provides examples of common machine learning applications such as image recognition, speech recognition, traffic prediction, product recommendations, self-driving cars, email filtering, and virtual assistants. It also discusses supervised learning for classification and regression problems, unsupervised learning for exploring patterns in unlabeled data, and reinforcement learning where agents learn through trial-and-error interactions with an environment.

Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning. This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.

This document provides an overview of machine learning, including definitions, types, steps, and applications. It defines machine learning as a field that gives computers the ability to learn without being explicitly programmed. The document outlines the main types of machine learning as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. It also describes the typical steps in a machine learning process as gathering data, preparing data, choosing a model, training, evaluation, and prediction. Examples of machine learning applications discussed include prediction, image recognition, natural language processing, and personal assistants. Popular machine learning languages and packages are also listed.

The document discusses various topics related to artificial intelligence including definitions of AI, goals of AI, whether machines can think, the Turing test, types of AI tasks including mundane, formal and expert tasks, technologies based on AI such as machine learning, natural language processing, computer vision, and applications of AI such as in healthcare, gaming, finance, data security, social media, travel and more.

Machine learning involves computers improving their ability to complete tasks through experience. A machine learning problem is well-defined if it identifies: 1) the class of tasks, 2) a performance measure to improve on, and 3) the source of training experience. For example, a program that learns to play checkers would improve its ability to win games (performance measure) by playing practice games against itself (training experience) for checkers games (class of tasks). How machines learn involves inputting past data, abstracting that data using algorithms, and generalizing the abstraction to make decisions.

Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning. This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.

This document discusses the complexity of artificial intelligence and machine learning. It notes that complexity arises from big data's volume, variety, velocity and veracity, as well as from knowledge representation, unlabeled data, feature engineering, hardware limitations, and the stack of methods and technologies used. High performance computing techniques like in-memory data fabrics and GPU machines can help address these complexities. Topological data analysis is also mentioned as a technique that can help with complexity through properties like coordinate and deformation invariance and compressed representations.

Supervised learning is a machine learning technique where models are trained using labeled examples to predict or classify new examples. It involves mapping input variables to output variables using labeled training data to build a model that can predict the correct output for a new input. Some common supervised learning algorithms include classification algorithms like logistic regression and decision trees, and regression algorithms like linear regression. Supervised learning is used for tasks like image classification, fraud detection, and spam filtering.

Testing AI involves validating that AI systems perform as intended and are free of unintended behaviors. This includes testing the training data, model architecture, and system outputs. Challenges include the inability to test all possible inputs and scenarios, as well as accurately interpreting ambiguous or uncertain outputs. Emerging techniques use machine learning to automatically generate test cases, fuzz testing to introduce adversarial inputs, and model analysis to evaluate behaviors. Proper testing is crucial to ensure AI systems do not negatively impact users or society.

Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning. This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.

Introduction to Artificial Intelligence ( AI)

This document discusses artificial intelligence, machine learning, deep learning, and data science. It defines each term and explains the relationships between them. AI is the overarching field, while machine learning and deep learning are subsets of AI. Machine learning allows machines to improve performance over time without human intervention by learning from examples, and deep learning uses artificial neural networks with many layers to closely mimic the human brain. The document provides an example of a fruit detection system using deep learning that trains a neural network to detect ripe fruit for automated harvesting.

A Deep Look into AI and its Impact on Programmatic Advertising Learn about why AI is important for the advertising industry, and the tremendous impacts it has on marketing and consumers. o What is AI? There are a lot of misconceptions regarding what it is and a lot of deception around it as well. We'll clear that up and separate the pretenders from the contenders. o When to use AI? We'll discuss the ideal use cases for investing in AI technologies and specific examples in marketing. o A Case Study in Advertising. Yang will take you step by step through how StackAdapt solved a particular problem -- starting with a basic algorithm to a full-fledged AI powered system. o What the future looks like. How close are we really to killer robots? In order for AI to evolve further, there are challenges that need to be solved.

Unit 1 of the document introduces artificial intelligence and machine learning. It discusses how AI solves real-world problems by simulating human intelligence and modeling problem-solving processes. It also covers machine learning models like supervised, unsupervised, and reinforcement learning. Additionally, it introduces popular Python libraries for artificial intelligence like NumPy, Pandas, scikit-learn, and TensorFlow. The role of Python in AI is also discussed along with Anaconda and how to install Python libraries.

The document discusses different types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of each type, such as using labeled data to classify emails as spam or not spam for supervised learning, grouping fruits by color without labels for unsupervised learning, and using rewards to guide an agent through a maze for reinforcement learning. The document also covers applications of machine learning across different domains like banking, biomedical, computer, and environment.

Computer vision is a branch of computer science which deals with recognising objects, people and identifying patterns in visuals. It is basically analogous to the vision of an animal. Topics covered: 1. Overview of Machine Learning 2. Basics of Deep Learning 3. What is computer vision and its use-cases? 4. Various algorithms used in Computer Vision (mostly CNN) 5. Live hands-on demo of either Auto Cameraman or Face recognition system 6. What next?

This document discusses recurrent neural networks (RNNs) and their applications. It begins by explaining that RNNs can process input sequences of arbitrary lengths, unlike other neural networks. It then provides examples of RNN applications, such as predicting time series data, autonomous driving, natural language processing, and music generation. The document goes on to describe the fundamental concepts of RNNs, including recurrent neurons, memory cells, and different types of RNN architectures for processing input/output sequences. It concludes by demonstrating how to implement basic RNNs using TensorFlow's static_rnn function.

Natural Language Processing (NLP) is a field of artificial intelligence that deals with interactions between computers and human languages. NLP aims to program computers to process and analyze large amounts of natural language data. Some common NLP tasks include speech recognition, text classification, machine translation, question answering, and more. Popular NLP tools include Stanford CoreNLP, NLTK, OpenNLP, and TextBlob. Vectorization is commonly used to represent text in a way that can be used for machine learning algorithms like calculating text similarity. Tf-idf is a common technique used to weigh words based on their frequency and importance.

- Naive Bayes is a classification technique based on Bayes' theorem that uses "naive" independence assumptions. It is easy to build and can perform well even with large datasets. - It works by calculating the posterior probability for each class given predictor values using the Bayes theorem and independence assumptions between predictors. The class with the highest posterior probability is predicted. - It is commonly used for text classification, spam filtering, and sentiment analysis due to its fast performance and high success rates compared to other algorithms.

An autoencoder is an artificial neural network that is trained to copy its input to its output. It consists of an encoder that compresses the input into a lower-dimensional latent-space encoding, and a decoder that reconstructs the output from this encoding. Autoencoders are useful for dimensionality reduction, feature learning, and generative modeling. When constrained by limiting the latent space or adding noise, autoencoders are forced to learn efficient representations of the input data. For example, a linear autoencoder trained with mean squared error performs principal component analysis.

The document discusses challenges in training deep neural networks and solutions to those challenges. Training deep neural networks with many layers and parameters can be slow and prone to overfitting. A key challenge is the vanishing gradient problem, where the gradients shrink exponentially small as they propagate through many layers, making earlier layers very slow to train. Solutions include using initialization techniques like He initialization and activation functions like ReLU and leaky ReLU that do not saturate, preventing gradients from vanishing. Later improvements include the ELU activation function.

The document provides information about key-value RDD transformations and actions in Spark. It defines transformations like keys(), values(), groupByKey(), combineByKey(), sortByKey(), subtractByKey(), join(), leftOuterJoin(), rightOuterJoin(), and cogroup(). It also defines actions like countByKey() and lookup() that can be performed on pair RDDs. Examples are given showing how to use these transformations and actions to manipulate key-value RDDs.

Big Data with Hadoop & Spark Training: http://bit.ly/2kyRTuW This CloudxLab Advanced Spark Programming tutorial helps you to understand Advanced Spark Programming in detail. Below are the topics covered in this slide: 1) Shared Variables - Accumulators & Broadcast Variables 2) Accumulators and Fault Tolerance 3) Custom Accumulators - Version 1.x & Version 2.x 4) Examples of Broadcast Variables 5) Key Performance Considerations - Level of Parallelism 6) Serialization Format - Kryo 7) Memory Management 8) Hardware Provisioning

Big Data with Hadoop & Spark Training: http://bit.ly/2sm9c61 This CloudxLab Introduction to Spark SQL & DataFrames tutorial helps you to understand Spark SQL & DataFrames in detail. Below are the topics covered in this slide: 1) Loading XML 2) What is RPC - Remote Process Call 3) Loading AVRO 4) Data Sources - Parquet 5) Creating DataFrames From Hive Table 6) Setting up Distributed SQL Engine

Big Data with Hadoop & Spark Training: http://bit.ly/2sf2z6i This CloudxLab Introduction to Spark SQL & DataFrames tutorial helps you to understand Spark SQL & DataFrames in detail. Below are the topics covered in this slide: 1) Introduction to DataFrames 2) Creating DataFrames from JSON 3) DataFrame Operations 4) Running SQL Queries Programmatically 5) Datasets 6) Inferring the Schema Using Reflection 7) Programmatically Specifying the Schema

(Big Data with Hadoop & Spark Training: http://bit.ly/2IUsWca This CloudxLab Running in a Cluster tutorial helps you to understand running Spark in the cluster in detail. Below are the topics covered in this tutorial: 1) Spark Runtime Architecture 2) Driver Node 3) Scheduling Tasks on Executors 4) Understanding the Architecture 5) Cluster Managers 6) Executors 7) Launching a Program using spark-submit 8) Local Mode & Cluster-Mode 9) Installing Standalone Cluster 10) Cluster Mode - YARN 11) Launching a Program on YARN 12) Cluster Mode - Mesos and AWS EC2 13) Deployment Modes - Client and Cluster 14) Which Cluster Manager to Use? 15) Common flags for spark-submit

Big Data with Hadoop & Spark Training: http://bit.ly/2LCTufA This CloudxLab Introduction to SparkR tutorial helps you to understand SparkR in detail. Below are the topics covered in this tutorial: 1) SparkR (R on Spark) 2) SparkR DataFrames 3) Launch SparkR 4) Creating DataFrames from Local DataFrames 5) DataFrame Operation 6) Creating DataFrames - From JSON 7) Running SQL Queries from SparkR

1) NoSQL databases are non-relational and schema-free, providing alternatives to SQL databases for big data and high availability applications. 2) Common NoSQL database models include key-value stores, column-oriented databases, document databases, and graph databases. 3) The CAP theorem states that a distributed data store can only provide two out of three guarantees around consistency, availability, and partition tolerance.

Big Data with Hadoop & Spark Training: http://bit.ly/2sh5b3E This CloudxLab Hadoop Streaming tutorial helps you to understand Hadoop Streaming in detail. Below are the topics covered in this tutorial: 1) Hadoop Streaming and Why Do We Need it? 2) Writing Streaming Jobs 3) Testing Streaming jobs and Hands-on on CloudxLab

This document provides instructions for getting started with TensorFlow using a free CloudxLab. It outlines the following steps: 1. Open CloudxLab and enroll if not already enrolled. Otherwise go to "My Lab". 2. In "My Lab", open Jupyter and run commands to clone an ML repository containing TensorFlow examples. 3. Go to the deep learning folder in Jupyter and open the TensorFlow notebook to get started with examples.

In this tutorial, we will learn the the following topics - + The Curse of Dimensionality + Main Approaches for Dimensionality Reduction + PCA - Principal Component Analysis + Kernel PCA + LLE + Other Dimensionality Reduction Techniques

In this tutorial, we will learn the the following topics - + Voting Classifiers + Bagging and Pasting + Random Patches and Random Subspaces + Random Forests + Boosting + Stacking

In this tutorial, we will learn the the following topics - + Training and Visualizing a Decision Tree + Making Predictions + Estimating Class Probabilities + The CART Training Algorithm + Computational Complexity + Gini Impurity or Entropy? + Regularization Hyperparameters + Regression + Instability

In this tutorial, we will learn the the following topics - + Linear SVM Classification + Soft Margin Classification + Nonlinear SVM Classification + Polynomial Kernel + Adding Similarity Features + Gaussian RBF Kernel + Computational Complexity + SVM Regression

Big Data with Hadoop & Spark Training: http://bit.ly/2wLh5aF This CloudxLab Introduction to Linux helps you to understand Linux in detail. Below are the topics covered in this tutorial: 1) Linux Overview 2) Linux Components - The Programs, The Kernel, The Shell 3) Overview of Linux File System 4) Connect to Linux Console 5) Linux - Quick Start Commands 6) Overview of Linux File System

Password Rotation in 2024 is still Relevant

The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries: 1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes. 2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions. 3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines. 4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors. 5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering. 6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands. 7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems. 8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering. 9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively. Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.

As a popular open-source library for analytics engineering, dbt is often used in combination with Airflow. Orchestrating and executing dbt models as DAGs ensures an additional layer of control over tasks, observability, and provides a reliable, scalable environment to run dbt models. This webinar will cover a step-by-step guide to Cosmos, an open source package from Astronomer that helps you easily run your dbt Core projects as Airflow DAGs and Task Groups, all with just a few lines of code. We’ll walk through: - Standard ways of running dbt (and when to utilize other methods) - How Cosmos can be used to run and visualize your dbt projects in Airflow - Common challenges and how to address them, including performance, dependency conflicts, and more - How running dbt projects in Airflow helps with cost optimization Webinar given on 9 July 2024

If you’ve ever had to analyze a map or GPS data, chances are you’ve encountered and even worked with coordinate systems. As historical data continually updates through GPS, understanding coordinate systems is increasingly crucial. However, not everyone knows why they exist or how to effectively use them for data-driven insights. During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to maintain and transform your data’s coordinate systems in an easy-to-digest way, accurately representing the geographical space that it exists within. During this webinar, you will have the chance to: - Enhance Your Understanding: Gain a clear overview of what coordinate systems are and their value - Learn Practical Applications: Why we need datams and projections, plus units between coordinate systems - Maximize with FME: Understand how FME handles coordinate systems, including a brief summary of the 3 main reprojectors - Custom Coordinate Systems: Learn how to work with FME and coordinate systems beyond what is natively supported - Look Ahead: Gain insights into where FME is headed with coordinate systems in the future Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!

This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator. Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/ Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.

Today’s digitally connected world presents a wide range of security challenges for enterprises. Insider security threats are particularly noteworthy because they have the potential to cause significant harm. Unlike external threats, insider risks originate from within the company, making them more subtle and challenging to identify. This blog aims to provide a comprehensive understanding of insider security threats, including their types, examples, effects, and mitigation techniques.

To help you choose the best DiskWarrior alternative, we've compiled a comparison table summarizing the features, pros, cons, and pricing of six alternatives.

Six months into 2024, and it is clear the privacy ecosystem takes no days off!! Regulators continue to implement and enforce new regulations, businesses strive to meet requirements, and technology advances like AI have privacy professionals scratching their heads about managing risk. What can we learn about the first six months of data privacy trends and events in 2024? How should this inform your privacy program management for the rest of the year? Join TrustArc, Goodwin, and Snyk privacy experts as they discuss the changes we’ve seen in the first half of 2024 and gain insight into the concrete, actionable steps you can take to up-level your privacy program in the second half of the year. This webinar will review: - Key changes to privacy regulations in 2024 - Key themes in privacy and data governance in 2024 - How to maximize your privacy program in the second half of 2024

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Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.

These fighter aircraft have uses outside of traditional combat situations. They are essential in defending India's territorial integrity, averting dangers, and delivering aid to those in need during natural calamities. Additionally, the IAF improves its interoperability and fortifies international military alliances by working together and conducting joint exercises with other air forces.

Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)

Everything that I found interesting about engineering leadership last month

accommodate the strengths, weaknesses, threats and opportunities of autonomous vehicles

Blockchain technology is transforming industries and reshaping the way we conduct business, manage data, and secure transactions. Whether you're new to blockchain or looking to deepen your knowledge, our guidebook, "Blockchain for Dummies", is your ultimate resource.

YOUR RELIABLE WEB DESIGN & DEVELOPMENT TEAM — FOR LASTING SUCCESS WPRiders is a web development company specialized in WordPress and WooCommerce websites and plugins for customers around the world. The company is headquartered in Bucharest, Romania, but our team members are located all over the world. Our customers are primarily from the US and Western Europe, but we have clients from Australia, Canada and other areas as well. Some facts about WPRiders and why we are one of the best firms around: More than 700 five-star reviews! You can check them here. 1500 WordPress projects delivered. We respond 80% faster than other firms! Data provided by Freshdesk. We’ve been in business since 2015. We are located in 7 countries and have 22 team members. With so many projects delivered, our team knows what works and what doesn’t when it comes to WordPress and WooCommerce. Our team members are: - highly experienced developers (employees & contractors with 5 -10+ years of experience), - great designers with an eye for UX/UI with 10+ years of experience - project managers with development background who speak both tech and non-tech - QA specialists - Conversion Rate Optimisation - CRO experts They are all working together to provide you with the best possible service. We are passionate about WordPress, and we love creating custom solutions that help our clients achieve their goals. At WPRiders, we are committed to building long-term relationships with our clients. We believe in accountability, in doing the right thing, as well as in transparency and open communication. You can read more about WPRiders on the About us page.

Are you interested in dipping your toes in the cloud native observability waters, but as an engineer you are not sure where to get started with tracing problems through your microservices and application landscapes on Kubernetes? Then this is the session for you, where we take you on your first steps in an active open-source project that offers a buffet of languages, challenges, and opportunities for getting started with telemetry data. The project is called openTelemetry, but before diving into the specifics, we’ll start with de-mystifying key concepts and terms such as observability, telemetry, instrumentation, cardinality, percentile to lay a foundation. After understanding the nuts and bolts of observability and distributed traces, we’ll explore the openTelemetry community; its Special Interest Groups (SIGs), repositories, and how to become not only an end-user, but possibly a contributor.We will wrap up with an overview of the components in this project, such as the Collector, the OpenTelemetry protocol (OTLP), its APIs, and its SDKs. Attendees will leave with an understanding of key observability concepts, become grounded in distributed tracing terminology, be aware of the components of openTelemetry, and know how to take their first steps to an open-source contribution! Key Takeaways: Open source, vendor neutral instrumentation is an exciting new reality as the industry standardizes on openTelemetry for observability. OpenTelemetry is on a mission to enable effective observability by making high-quality, portable telemetry ubiquitous. The world of observability and monitoring today has a steep learning curve and in order to achieve ubiquity, the project would benefit from growing our contributor community.

Have you noticed the OpenSSF Scorecard badges on the official Dart and Flutter repos? It's Google's way of showing that they care about security. Practices such as pinning dependencies, branch protection, required reviews, continuous integration tests etc. are measured to provide a score and accompanying badge. You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter. The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.

Support en anglais diffusé lors de l'événement 100% IA organisé dans les locaux parisiens d'Iguane Solutions, le mardi 2 juillet 2024 : - Présentation de notre plateforme IA plug and play : ses fonctionnalités avancées, telles que son interface utilisateur intuitive, son copilot puissant et des outils de monitoring performants. - REX client : Cyril Janssens, CTO d’ easybourse, partage son expérience d’utilisation de notre plateforme IA plug & play.

Widya Salim and Victor Ma will outline the causal impact analysis, framework, and key learnings used to quantify the impact of reducing Twitter's network latency.