Introduction to Deep Learning | CloudxLab

Video: https://youtu.be/65RV3O4UR3w Semi-Supervised Learning is a technique that combines the benefits of supervised learning (performance, intuitiveness) with the ability to use cheap unlabeled data (unsupervised learning). With all the cheap data available, Semi Supervised Learning will get bigger in the coming months. This episode of Machine Learning Made Simple will go into SSL, how it works, transduction vs induction, the assumptions SSL algorithms make, and how SSL compares to human learning. About Machine Learning Made Simple: Machine Learning Made Simple is a playlist that aims to break down complex Machine Learning and AI topics into digestible videos. With this playlist, you can dive head first into the world of ML implementation and/or research. Feel free to drop any feedback you might have down below.

This document provides an overview of deep learning presented by Yann LeCun and Marc'Aurelio Ranzato at an ICML tutorial in 2013. It discusses how deep learning learns hierarchical representations through multiple stages of non-linear feature transformations, inspired by the hierarchical structure of the mammalian visual cortex. It also compares different types of deep learning architectures and training protocols.

Review presentation about Semi-Supervised techniques in Machine Learning. Presentation was done as part of Montreal Data series.

This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart

The document summarizes key concepts in machine learning, including defining learning, types of learning (induction vs discovery, guided learning vs learning from raw data, etc.), generalisation and specialisation, and some simple learning algorithms like Find-S and the candidate elimination algorithm. It discusses how learning can be viewed as searching a generalisation hierarchy to find a hypothesis that covers the examples. The candidate elimination algorithm maintains the version space - the set of hypotheses consistent with the training examples - by updating the general and specific boundaries as new examples are processed.

Summary of the paper, "Self-training with Noisy Student improves ImageNet classification" by Qizhe Xie et al.

Reinforcement learning is a machine learning technique where an agent learns to act by interacting with an environment. The agent takes actions and receives rewards, with the goal of maximizing total reward over time. Real-world reinforcement learning is challenging due to large state spaces and delayed rewards. However, it can be made more tractable by framing problems as contextual bandits, where rewards are immediate and state does not depend on past actions. Contextual bandits can then be solved using supervised learning techniques by addressing the partial information problem inherent to reinforcement learning.

An introduction to Contextual Bandits, including motivation, evaluation, optimisation, and real-world use cases.

This document provides an overview of machine learning applications in natural language processing and text classification. It discusses common machine learning tasks like part-of-speech tagging, named entity extraction, and text classification. Popular machine learning algorithms for classification are described, including k-nearest neighbors, Rocchio classification, support vector machines, bagging, and boosting. The document argues that machine learning can be used to solve complex real-world problems and that text processing is one area with many potential applications of these techniques.

Machine learning involves improving a system's performance on a task over time based on experience. It is defined as a computer program improving its ability to complete a task based on experience as measured by a performance metric. Learning modifies an agent's decision mechanisms to improve performance. A learning agent consists of a learning element that improves over time, a performance element that acts, a critic that provides feedback, and a problem generator that suggests new experiences.

1) More data is not always better than better models. Sometimes, better modeling techniques are needed rather than just collecting more data. 2) Ensembles of different models generally perform better than any single model and are commonly used in practice. Feature engineering to create new inputs for ensembles can improve their effectiveness. 3) Implicit signals from user behavior usually provide more useful information than explicit feedback, but both should be used to best represent users' long-term goals.

Slides with some useful tips and tricks how to win data science competitions in kaggle. I hope someone finds this useful or inspirational.

The document discusses various topics related to machine learning including machine learning applications like spam filtering and recommendation systems. It provides definitions and examples of different machine learning categories like supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves providing input and desired output for classification while unsupervised learning allows machines to classify without prior information. Reinforcement learning uses rewards and penalties to direct unsupervised learning through experiences.

This document presents a lab seminar on semi-supervised learning. It begins with background on semi-supervised learning and examples of applications. It then discusses common semi-supervised learning methods like EM with generative models, co-training, transductive SVMs, and graph-based methods. Next, it covers assumptions of semi-supervised learning, noting the utility of unlabeled data depends on problem structure matching model assumptions. Finally, it proposes future work on multi-edge graph-based semi-supervised learning.

This document contains an icebreaker game to help people get to know each other. It includes 7 questions with scrambled letters that must be unscrambled in 20 seconds. The questions cover topics related to artificial intelligence and machine learning like neural networks, machine learning, optimization, linear models, artificial intelligence, logistic regression, and functions.

This document discusses best practices for setting up development and test sets for machine learning models. It recommends that the dev and test sets: 1) Should reflect the actual data distribution you want your model to perform well on, rather than just being a random split of your training data. 2) Should come from the same data distribution. Having mismatched dev and test sets makes progress harder to measure. 3) The dev set should be large enough, typically thousands to tens of thousands of examples, to detect small performance differences as models are improved. The test set size depends on desired confidence in overall performance.

Learning model,relevance based learning, instance based learning, reinforcement learning, passive learning, active learning, RBL,KBL

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.

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 deep learning and neural networks. It begins with definitions of machine learning, artificial intelligence, and the different types of machine learning problems. It then introduces deep learning, explaining that it uses neural networks with multiple layers to learn representations of data. The document discusses why deep learning works better than traditional machine learning for complex problems. It covers key concepts like activation functions, gradient descent, backpropagation, and overfitting. It also provides examples of applications of deep learning and popular deep learning frameworks like TensorFlow. Overall, the document gives a high-level introduction to deep learning concepts and techniques.

IgmGuru takes great pride in introducing the well-curated deep learning with tensorflow training in which industry leaders and academia has been consulted while preparing this course.

IgmGuru takes great pride in introducing the well-curated Deep Learning with TensorFlow course in which industry leaders and academia has been consulted while preparing this course. IgmGuru is very enthusiastic about the Deep Learning with TensorFlow course as we will go through some of the famous use cases and prepare the learners to face industry-related challenges. Deep Learning is loosely inspired by the ways humans process information and then communicate through our own biological neural networks. These learning algorithms are able to process vast amounts of data to build meaningful relationships amongst them.

IgmGuru takes great pride in introducing the well-curated deep learning with tensorflow course in which industry leaders and academia has been consulted while preparing this course. IgmGuru is very enthusiastic about the Deep Learning with TensorFlow course as we will go through some of the famous use cases and prepare the learners to face industry-related challenges. Deep Learning is loosely inspired by the ways humans process information and then communicate through our own biological neural networks. These learning algorithms are able to process vast amounts of data to build meaningful relationships amongst them. https://www.igmguru.com/machine-learning-ai/deep-learning-tensorflow-training/

In This Data Science course ( Graduate Program ) I will focus on understanding business intelligence systems and helping future managers use and understand analytics, Business Intelligence emphasizing the applications and implementations behind the concepts. a solid foundation of BI that is reinforced with hands-on practice. The course is also designed as an introduction to programming and statistics for students from many different majors. It teaches practical techniques that apply across many disciplines and also serves as the technical foundation for more advanced courses in data science, statistics, and computer science.

The document provides tips and guidelines for designing effective user interfaces for learning environments. It discusses principles of user interface design such as reducing extraneous cognitive load on users. Specific tips include using an F-shaped reading pattern to structure content, embedding instructions in the interface context, and testing designs with users. The key recommendation is that interface design should make tasks easy to complete while keeping users engaged in the learning process.

How To Optimize Your Tech Recruiting Stack Patrick Christell, Senior Sourcer at Hire4ce, meets all the qualifications of “MASTER.” We’re talking a Full-Lifecycle Recruiter, Project Manager and Agile sourcing pod-builder with seven-plus years of progressive experience recruiting for technology companies across the boards. He also has a rather impressive tech stack, which is what this is all about. Patrick is here to give you 60-minutes of training and live Q&A that will help you learn to recruit top talent. In this webinar we will cover: - How to search. Tools like Hiretual, Seekout, AmazingHiring (and their plusses and minuses). The difference between searching for senior-level engineers, how to know if you are on a purple squirrel hunt, and what to with a BONUS live demo that iterates a single string. - How to run a sourcing pod. Learn how Patrick creates his own CRM that can do outreach and reporting - How to understand tech without being a techie. What a software stack even is, understanding how it fits together, learning what each part of the stack technologies are associated with. - How to engage talent. Why a mixture of broad spectrum outreach and personalized outreach is best. What cadence works best in 2019. Why only using inmails screws you, and how to leverage the phone even if you hate using it (TextNow). Nobody’s got time for a floppy stack. Let Patrick show you how to build in functionality and results.

This document provides an overview of deep learning concepts including: - Deep learning uses neural networks inspired by the human brain to learn representations of data without being explicitly programmed. - Key deep learning concepts are explained such as convolutional neural networks, activation functions, gradient descent, and overfitting. - TensorFlow is introduced as an open-source library for machine learning that allows for implementing deep learning models at scale. - Applications of deep learning like computer vision, natural language processing, and recommender systems are discussed.

The modern employee has 1% of their week to focus on training. What can they do with that roughly 24 minutes a week? Turns out, a lot. Armed with digestible and easily accessible microlearning experiences, we can create meaningful changes in behavior across our organizations. Along the way, we can help elevate the role of L&D from order takers to change makers. Join Alex Khurgin, Director of Learning Innovation at Grovo, as he explains the importance of leveraging microlearning when training modern employees and how to create a microlearning strategy of your own to meet the needs of your audience and goals of your company. In this session, you’ll learn to: Overcome the three misconceptions that block most L&D initiatives from being successful Create microlearning experiences that capture attention, motivate action, and make learninstick Prove and report on behavior change, not meaningless learning metrics

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.

The document discusses common mistakes made by developers. It addresses 7 common mistakes: thinking the job is only about coding; blaming unclear requirements on others; prioritizing speed over quality; focusing only on coding without understanding the problem domain; avoiding asking for help; considering testing to be someone else's job; and blaming limits on management rather than taking responsibility. For each mistake, it provides a counterpoint and tips to avoid the mistake and improve development practices.

In this Python Machine Learning Tutorial, Machine Learning also termed ML. It is a subset of AI (Artificial Intelligence) and aims to grants computers the ability to learn by making use of statistical techniques. It deals with algorithms that can look at data to learn from it and make predictions.

The document discusses various aspects of testing in an agile environment. It covers roles like testers, product owners, and scrum masters. It also discusses testing practices like unit testing, exploratory testing, test automation. Other topics include test levels like unit, integration and end-to-end testing. Team dynamics and how testing fits in the agile process are also summarized.

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.

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.

The document discusses techniques for teaching programming to novices. It describes some of the challenges of teaching programming, including that students need to develop the right mental models of how computers work and that programming involves multiple levels of abstraction. It then discusses the "teacher's toolkit", which includes techniques like pair programming, use-modify-create, PRIMM, peer instruction, worked examples, and Parson's problems. These techniques aim to reduce cognitive load, engage students collaboratively, and help students understand programs by reading code before writing it themselves. The document emphasizes that teachers play a key role and can draw on strategies from their existing toolkit to help students learn programming.

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

MuleSoft Meetup on APM and IDP

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.

Cybersecurity is a major concern in today's connected digital world. Threats to organizations are constantly evolving and have the potential to compromise sensitive information, disrupt operations, and lead to significant financial losses. Traditional cybersecurity techniques often fall short against modern attackers. Therefore, advanced techniques for cyber security analysis and anomaly detection are essential for protecting digital assets. This blog explores these cutting-edge methods, providing a comprehensive overview of their application and importance.

This presentation, delivered at the Postgres Bangalore (PGBLR) Meetup-2 on June 29th, 2024, dives deep into connection pooling for PostgreSQL databases. Aakash M, a PostgreSQL Tech Lead at Mydbops, explores the challenges of managing numerous connections and explains how connection pooling optimizes performance and resource utilization. Key Takeaways: * Understand why connection pooling is essential for high-traffic applications * Explore various connection poolers available for PostgreSQL, including pgbouncer * Learn the configuration options and functionalities of pgbouncer * Discover best practices for monitoring and troubleshooting connection pooling setups * Gain insights into real-world use cases and considerations for production environments This presentation is ideal for: * Database administrators (DBAs) * Developers working with PostgreSQL * DevOps engineers * Anyone interested in optimizing PostgreSQL performance Contact info@mydbops.com for PostgreSQL Managed, Consulting and Remote DBA Services

Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality. Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality. Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality. Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank? ** Episode Overview ** In this first episode of our quality series, Kristen Hansen and the panel discuss: ⦿ What do we mean when we say patent quality? ⦿ Why is patent quality important? ⦿ How to balance quality and budget ⦿ The importance of searching, continuations, and draftsperson domain expertise ⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications https://www.aurorapatents.com/patently-strategic-podcast.html

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.

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.

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!

Invited Remote Lecture to SC21 The International Conference for High Performance Computing, Networking, Storage, and Analysis St. Louis, Missouri November 18, 2021

Everything that I found interesting last month about the irresponsible use of machine intelligence

Password Rotation in 2024 is still Relevant

Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr "A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models" arXiv2023 https://arxiv.org/abs/2307.12980

Solar Storms (Geo Magnetic Storms) are the motion of accelerated charged particles in the solar environment with high velocities due to the coronal mass ejection (CME).

Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.

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.