Support Vector Machines

This Naive Bayes Classifier tutorial presentation will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this presentation, you will also implement Naive Bayes algorithm for text classification in Python. The topics covered in this Naive Bayes presentation are as follows: 1. What is Naive Bayes? 2. Naive Bayes and Machine Learning 3. Why do we need Naive Bayes? 4. Understanding Naive Bayes Classifier 5. Advantages of Naive Bayes Classifier 6. Demo - Text Classification using Naive Bayes - - - - - - - - Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. - - - - - - - -

The document discusses the random forest algorithm. It introduces random forest as a supervised classification algorithm that builds multiple decision trees and merges them to provide a more accurate and stable prediction. It then provides an example pseudocode that randomly selects features to calculate the best split points to build decision trees, repeating the process to create a forest of trees. The document notes key advantages of random forest are that it avoids overfitting and can be used for both classification and regression tasks.

Ensemble methods combine multiple machine learning models to obtain better predictive performance than from any individual model. There are two main types of ensemble methods: sequential (e.g AdaBoost) where models are generated one after the other, and parallel (e.g Random Forest) where models are generated independently. Popular ensemble methods include bagging, boosting, and stacking. Bagging averages predictions from models trained on random samples of the data, while boosting focuses on correcting previous models' errors. Stacking trains a meta-model on predictions from other models to produce a final prediction.

This document provides an overview of support vector machines (SVMs). It discusses how SVMs can be used to perform classification tasks by finding optimal separating hyperplanes that maximize the margin between different classes. The document outlines how SVMs solve an optimization problem to find these optimal hyperplanes using techniques like Lagrange duality, kernels, and soft margins. It also covers model selection methods like cross-validation and discusses extensions of SVMs to multi-class classification problems.

This document discusses various classification algorithms including k-nearest neighbors, decision trees, naive Bayes classifier, and logistic regression. It provides examples of how each algorithm works. For k-nearest neighbors, it shows how an unknown data point would be classified based on its nearest neighbors. For decision trees, it illustrates how a tree is built by splitting the data into subsets at each node until pure subsets are reached. It also provides an example decision tree to predict whether Amit will play cricket. For naive Bayes, it gives an example of calculating the probability of cancer given a patient is a smoker.

In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis

Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning. Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.

Abstract: This PDSG workshop introduces basic concepts of ensemble methods in machine learning. Concepts covered are Condercet Jury Theorem, Weak Learners, Decision Stumps, Bagging and Majority Voting. Level: Fundamental Requirements: No prior programming or statistics knowledge required.

The document provides an overview of the Naive Bayes algorithm for classification problems. It begins by explaining that Naive Bayes is a supervised learning algorithm based on Bayes' theorem. It then explains the key aspects of Naive Bayes: - It assumes independence between features (naive) and uses Bayes' theorem to calculate probabilities (Bayes). - Bayes' theorem is used to calculate the probability of a hypothesis given observed data. - An example demonstrates how Naive Bayes classifies weather data to predict whether to play or not play. The document concludes by discussing the advantages, disadvantages, applications, and types of Naive Bayes models, as well as providing Python code to implement a Naive Bayes classifier.

https://telecombcn-dl.github.io/2018-dlai/ Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.

This document provides an overview of machine learning, including: - Machine learning allows computers to learn from data without being explicitly programmed, through processes like analyzing data, training models on past data, and making predictions. - The main types of machine learning are supervised learning, which uses labeled training data to predict outputs, and unsupervised learning, which finds patterns in unlabeled data. - Common supervised learning tasks include classification (like spam filtering) and regression (like weather prediction). Unsupervised learning includes clustering, like customer segmentation, and association, like market basket analysis. - Supervised and unsupervised learning are used in many areas like risk assessment, image classification, fraud detection, customer analytics, and more

The document provides an introduction to supervised machine learning and pattern classification. It begins with an overview of the speaker's background and research interests. Key concepts covered include definitions of machine learning, examples of machine learning applications, and the differences between supervised, unsupervised, and reinforcement learning. The rest of the document outlines the typical workflow for a supervised learning problem, including data collection and preprocessing, model training and evaluation, and model selection. Common classification algorithms like decision trees, naive Bayes, and support vector machines are briefly explained. The presentation concludes with discussions around choosing the right algorithm and avoiding overfitting.

Support Vector Machine (SVM) is a supervised machine learning algorithm that can be used for both classification and regression analysis. It works by finding a hyperplane in an N-dimensional space that distinctly classifies the data points. SVM selects the hyperplane that has the largest distance to the nearest training data points of any class, since larger the margin lower the generalization error of the classifier. SVM can efficiently perform nonlinear classification by implicitly mapping their inputs into high-dimensional feature spaces.

Support vector machines (SVMs) are a type of supervised machine learning algorithm used for classification and regression. SVMs learn by finding the optimal separating hyperplane that maximizes the margin between two classes of objects. SVMs can efficiently perform nonlinear classification using kernel methods. Key features of SVMs include their ability to handle high dimensional data, use different kernel functions for nonlinear classification, and avoid overfitting.

ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.

Logistic regression is a predictive analysis algorithm that can be used for classification problems. It estimates the probabilities of different classes using the logistic function, which outputs values between 0 and 1. Logistic regression transforms its output using the sigmoid function to return a probability value. It is used for problems like email spam detection, fraud detection, and tumor classification. The independent variables should be independent of each other and the dependent variable must be categorical. Gradient descent is used to minimize the loss function and optimize the model parameters during training.

▸ Machine Learning / Deep Learning models require to set the value of many hyperparameters ▸ Common examples: regularization coefficients, dropout rate, or number of neurons per layer in a Neural Network ▸ Instead of relying on some "expert advice", this presentation shows how to automatically find optimal hyperparameters ▸ Exhaustive Search, Monte Carlo Search, Bayesian Optimization, and Evolutionary Algorithms are explained with concrete examples

The document discusses machine learning classification using the MNIST dataset of handwritten digits. It begins by defining classification and providing examples. It then describes the MNIST dataset and how it is fetched in scikit-learn. The document outlines the steps of classification which include dividing the data into training and test sets, training a classifier on the training set, testing it on the test set, and evaluating performance. It specifically trains a stochastic gradient descent (SGD) classifier on the MNIST data. The performance is evaluated using cross validation accuracy, confusion matrix, and metrics like precision and recall.

This document outlines an introductory machine learning course, covering key concepts, applications, and types of machine learning like supervised and unsupervised learning. It discusses techniques like linear regression, classification, and handling overfitting. The course will include tutorials on sentiment analysis, spam filtering, stock prediction, image recognition and recommendation engines using Python and Scala. Later classes cover machine learning at scale using tools like Spark MLLib.

This second meetup will be about training different models for our recommender system. We will review the simple models we can build as a baseline. After that, we will present the recommender system as an optimization problem and discuss different training losses. We will mention linear models and matrix factorization techniques. We will end the presentation with a simple introduction to non-linear models and deep learning.

This document summarizes a session on classification models. It discusses classifying data by learning classification rules from labeled input data. Specifically, it covers k-nearest neighbors classification, where the target point is assigned the majority class of its k nearest neighbors. It also discusses extending linear regression to classification by using it to generate real-valued outputs, then thresholding those values to classify instances into classes 0 or 1. However, it notes that logistic regression is a better approach, using a sigmoid function to convert real-valued outputs to a probability between 0 and 1 before thresholding for classification. The document highlights key considerations for classification like dealing with multiple classes and class imbalance. It also discusses evaluating classifiers, ensemble methods, and overfitting/under

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.

This document discusses scaling machine learning algorithms to large datasets. It introduces machine learning and linear regression, explaining how to represent data with features and labels. Linear regression finds the line of best fit by minimizing a squared loss function. Computing the closed-form solution is inefficient for large datasets due to expensive matrix operations. Gradient descent is introduced as an iterative alternative that is easily parallelized. It works by taking steps in the direction of steepest descent until converging to a solution.

This document provides an overview of support vector machines (SVM) including: 1) Maximal margin classifiers which find the optimal separating hyperplane with the maximum margin between classes. Support vectors are the data points that determine the hyperplane. 2) Support vector classifiers which allow some misclassified data points by introducing slack variables. This makes the classifier more robust. 3) SVM can handle non-linear decision boundaries using kernel methods to map data into higher dimensional feature spaces where a linear separator can be found. Common kernels include linear, polynomial and radial basis function kernels. 4) Multi-class classification with SVM can be done with one-vs-one or one-vs-all approaches. 5) SVM

Machine Learning: An introduction โดย รศ.ดร.สุรพงค์ เอื้อวัฒนามงคล ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE

With the growing trend of machine learning, it is needless to say how machine learning can help reap benefits in agriculture. It will be boon for the farmer welfare.

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?

Topics covered in the Webinar 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? Presented by Sandeep Giri www.cloudxlab.com

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 machine learning algorithms for classification problems, specifically logistic regression. It explains that logistic regression predicts the probability of a binary outcome using a sigmoid function. Unlike linear regression, which is used for continuous outputs, logistic regression is used for classification problems where the output is discrete/categorical. It describes how logistic regression learns model parameters through gradient descent optimization of a likelihood function to minimize error. Regularization techniques can also be used to address overfitting issues that may arise from limited training data.

This document discusses support vector machines (SVM), a supervised machine learning algorithm used for classification and regression. It explains that SVM finds the optimal boundary, known as a hyperplane, that separates classes with the maximum margin. When data is not linearly separable, kernel functions can transform the data into a higher-dimensional space to make it separable. The document discusses SVM for both linearly separable and non-separable data, kernel functions, hyperparameters, and approaches for multiclass classification like one-vs-one and one-vs-all.

Basic machine learning background with Python scikit-learn This document provides an overview of machine learning and the Python scikit-learn library. It introduces key machine learning concepts like classification, linear models, support vector machines, decision trees, bagging, boosting, and clustering. It also demonstrates how to perform tasks like SVM classification, decision tree modeling, random forest, principal component analysis, and k-means clustering using scikit-learn. The document concludes that scikit-learn can handle large datasets and recommends Keras for deep learning.

This document provides an overview of machine learning, including: - An introduction to machine learning and why it is important. - The main types of machine learning algorithms: supervised learning, unsupervised learning, and deep neural networks. - Examples of how machine learning algorithms work, such as logistic regression, support vector machines, and k-means clustering. - How machine learning is being applied in various industries like healthcare, commerce, and more.

This document discusses online advertising and techniques for fitting large-scale models to advertising data. It outlines batch and online algorithms for logistic regression, including parallelizing existing batch algorithms and stochastic gradient descent. The document also discusses using alternating direction method of multipliers and follow the proximal regularized leader to fit models to large datasets across multiple machines. It provides examples of how major companies like LinkedIn and Facebook implement hybrid online-batch algorithms at large scale.

Machine learning techniques can be applied in formal verification in several ways: 1) To enhance current formal verification tools by automating tasks like debugging, specification mining, and theorem proving. 2) To enable the development of new formal verification tools by applying machine learning to problems like SAT solving, model checking, and property checking. 3) Specific applications include using machine learning for debugging and root cause identification, learning specifications from runtime traces, aiding theorem proving by selecting heuristics, and tuning SAT solver parameters and selection.

Système de recommandations de produits sur un site marchand par Koby KARP, Data Scientist (Equancy) & Hervé MIGNOT, Partner at Equancy La recommandation reste un outil clé pour la personnalisation des sites marchands et le sujet est loin d’être épuisé. La prise en compte de la particularité d’un marché peut nécessité d’adapter le traitement et les algorithmes utilisés. Après une revue des techniques de recommandations, nous présenterons la démarche spécifique que nous avons adopté. Le système a été développé sous Spark pour la préparation des données et le calcul des modèles de recommandations. Une API simple et son service ont été développé pour délivrer les recommandations aux applications clientes.

This document provides an introduction and overview for a course on machine learning. It outlines the course structure, assignments, and expectations. The course will cover topics including linear regression, classification, model selection, and dimensionality reduction. It will teach students how to analyze data, preprocess it, extract features, train models, and evaluate model performance. The goal is for students to understand core machine learning algorithms and concepts. Required materials include an introduction to statistical learning textbook.

This document provides an agenda for an introduction to deep learning presentation. It begins with an introduction to basic AI, machine learning, and deep learning terms. It then briefly discusses use cases of deep learning. The document outlines how to approach a deep learning problem, including which tools and algorithms to use. It concludes with a question and answer section.

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.

( Machine Learning & Deep Learning Specialization Training: https://goo.gl/5u2RiS ) This CloudxLab Reinforcement Learning tutorial helps you to understand Reinforcement Learning in detail. Below are the topics covered in this tutorial: 1) What is Reinforcement? 2) Reinforcement Learning an Introduction 3) Reinforcement Learning Example 4) Learning to Optimize Rewards 5) Policy Search - Brute Force Approach, Genetic Algorithms and Optimization Techniques 6) OpenAI Gym 7) The Credit Assignment Problem 8) Inverse Reinforcement Learning 9) Playing Atari with Deep Reinforcement Learning 10) Policy Gradients 11) Markov Decision Processes

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.

( Machine Learning & Deep Learning Specialization Training: https://goo.gl/goQxnL ) This CloudxLab Deep Learning tutorial helps you to understand Deep Learning in detail. Below are the topics covered in this tutorial: 1) What is Deep Learning 2) Deep Learning Applications 3) Artificial Neural Network 4) Deep Learning Neural Networks 5) Deep Learning Frameworks 6) AI vs Machine Learning

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

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

Kief Morris rethinks the infrastructure code delivery lifecycle, advocating for a shift towards composable infrastructure systems. We should shift to designing around deployable components rather than code modules, use more useful levels of abstraction, and drive design and deployment from applications rather than bottom-up, monolithic architecture and delivery.

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.

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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

Everything that I found interesting about engineering leadership last month

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.

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!

The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2023 and the first deals of 2024.

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.

In the modern digital era, social media platforms have become integral to our daily lives. These platforms, including Facebook, Instagram, WhatsApp, and Snapchat, offer countless ways to connect, share, and communicate.

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.

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

The integration of programming into civil engineering is transforming the industry. We can design complex infrastructure projects and analyse large datasets. Imagine revolutionizing the way we build our cities and infrastructure, all by the power of coding. Programming skills are no longer just a bonus—they’re a game changer in this era. Technology is revolutionizing civil engineering by integrating advanced tools and techniques. Programming allows for the automation of repetitive tasks, enhancing the accuracy of designs, simulations, and analyses. With the advent of artificial intelligence and machine learning, engineers can now predict structural behaviors under various conditions, optimize material usage, and improve project planning.

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Stream processing is a crucial component of modern data infrastructure, but constructing an efficient and scalable stream processing system can be challenging. Decoupling compute and storage architecture has emerged as an effective solution to these challenges, but it can introduce high latency issues, especially when dealing with complex continuous queries that necessitate managing extra-large internal states. In this talk, we focus on addressing the high latency issues associated with S3 storage in stream processing systems that employ a decoupled compute and storage architecture. We delve into the root causes of latency in this context and explore various techniques to minimize the impact of S3 latency on stream processing performance. Our proposed approach is to implement a tiered storage mechanism that leverages a blend of high-performance and low-cost storage tiers to reduce data movement between the compute and storage layers while maintaining efficient processing. Throughout the talk, we will present experimental results that demonstrate the effectiveness of our approach in mitigating the impact of S3 latency on stream processing. By the end of the talk, attendees will have gained insights into how to optimize their stream processing systems for reduced latency and improved cost-efficiency.

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

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An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)