PayPal's Fraud Detection with Deep Learning in H2O World 2014

a simple presentation about different big data stream processing systems such as SPARK, SAMZA and STORM and the difference between their architectures and purpose, in addition we talk about streaming layers tools such as Kafka and rabbitMQ, this presentation refer to this paper https://vsis-www.informatik.uni-hamburg.de/getDoc.php/publications/561/Real-time%20stream%20processing%20for%20Big%20Data.pdf and other useful links.

Forecasting time-series data has applications in many fields, including finance, health, etc. There are potential pitfalls when applying classic statistical and machine learning methods to time-series problems. This talk will give folks the basic toolbox to analyze time-series data and perform forecasting using statistical and machine learning models, as well as interpret and convey the outputs.

Graph analytics has a wide range of applications, from information propagation and network flow optimization to fraud and anomaly detection. The rise of social networks and the Internet of Things has given us complex web-scale graphs with billions of vertices and edges. However, in order to extract the hidden gems of understanding and information within those graphs, you need tools to analyze the graphs easily and efficiently. At Spark Summit 2016, Databricks introduced GraphFrames, which implements graph queries and pattern matching on top of Spark SQL to simplify graph analytics. In this talk, we’ll discuss the work that has made graph algorithms in GraphFrames faster and more scalable. For example, new implementations of connected components have received algorithm improvements based on recent research, as well as performance improvements from Spark DataFrames. Discover lessons learned from scaling the implementation from millions to billions of nodes; see its performance in the context of other popular graph libraries; and hear about real-world applications.

Text mining techniques like sentiment analysis, topic modeling, named entity extraction, and event extraction are used to map unstructured text to conventional data store structures.

The document provides an overview of Long Short Term Memory (LSTM) networks. It discusses: 1) The vanishing gradient problem in traditional RNNs and how LSTMs address it through gated cells that allow information to persist without decay. 2) The key components of LSTMs - forget gates, input gates, output gates and cell states - and how they control the flow of information. 3) Common variations of LSTMs including peephole connections, coupled forget/input gates, and Gated Recurrent Units (GRUs). Applications of LSTMs in areas like speech recognition, machine translation and more are also mentioned.

Text mining refers to extracting knowledge from unstructured text data. It is needed because most biological knowledge exists in unstructured research papers, making it difficult for scientists to manually analyze large amounts of text. Challenges include dealing with noisy, unstructured data and complex relationships between concepts. The text mining process involves preprocessing text through steps like tokenization, feature selection, and parsing to extract meaningful features before analysis can be done through classification, clustering, or other techniques. Potential applications are wide-ranging across domains like customer profiling, trend analysis, and web search.

basics of GAN neural network GAN is a advanced tech in area of neural networks which will help to generate new data . This new data will be developed based over the past experiences and raw data.

The document discusses data recovery, including what it is, common uses, and techniques. Data recovery involves retrieving deleted or inaccessible data from electronic storage media. It is commonly used by average users to recover important files, and by law enforcement to locate illegal data or restore deleted information for criminal investigations. Techniques discussed include software and hardware recovery methods, secure deletion standards, and overwriting schemes to prevent recovery.

Every investigator needs the skills and knowledge to use OSINT competently in investigations. As online information continues to multiply in volume and complexity, the tools required to find, sift through, authenticate and preserve that information become more and more important for investigators. Failure to master these tools to tap into the rich resources of the web can hamper your investigations. Learn the intricacies of online investigating from an expert in the field. Join Sandra Stibbards, owner and president of Camelot Investigations and a financial fraud investigator, speaker and trainer, for a free webinar on How to Use OSINT in Investigations. Webinar attendees will learn: -How to find information on the hidden web -How to find publicly available information in government and private databases -Dos and don’ts for searching social media effectively -Tips for remaining anonymous while researching investigation subjects -Accessing archived information -How criminals hide, and how to find them

Alternative Set Theoretic Models Fuzzy Set Model :a set theoretic model of document retrieval based on fuzzy theory. Extended Boolean Model:a set theoretic model of document retrieval based on an extension of the classic Boolean model. The idea is to interpret partial matches as Euclidean distances represented in a vectorial space of index terms.

This document provides an overview of different techniques for hyperparameter tuning in machine learning models. It begins with introductions to grid search and random search, then discusses sequential model-based optimization techniques like Bayesian optimization and Tree-of-Parzen Estimators. Evolutionary algorithms like CMA-ES and particle-based methods like particle swarm optimization are also covered. Multi-fidelity methods like successive halving and Hyperband are described, along with recommendations on when to use different techniques. The document concludes by listing several popular libraries for hyperparameter tuning.

Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.

Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.

Web scraping involves extracting data from human-readable web pages and converting it into structured data. There are several types of scraping including screen scraping, report mining, and web scraping. The process of web scraping typically involves using techniques like text pattern matching, HTML parsing, and DOM parsing to extract the desired data from web pages in an automated way. Common tools used for web scraping include Selenium, Import.io, Phantom.js, and Scrapy.

Many ecommerce companies have extensive logs of user behavior such as clicks and conversions. However, if supervised learning is naively applied, then systems can suffer from poor performance due to bias and feedback loops. Using techniques from counterfactual learning we can leverage log data in a principled manner in order to model user behaviour and build personalized recommender systems. At Grubhub, a user journey begins with recommendations and the vast majority of conversions are powered by recommendations. Our recommender policies can drive user behavior to increase orders and/or profit. Accordingly, the ability to rapidly iterate and experiment is very important. Because of our powerful GPU workflows, we can iterate 200% more rapidly than with counterpart CPU workflows. Developers iterate ideas with notebooks powered by GPUs. Hyperparameter spaces are explored up to 8x faster with multi-GPUs Ray clusters. Solutions are shipped from notebooks to production in half the time with nbdev. With our accelerated DS workflows and Deep Learning on GPUs, we were able to deliver a +12.6% conversion boost in just a few months. In this talk we hope to present modern techniques for industrial recommender systems powered by GPU workflows. First a small background on counterfactual learning techniques, then followed by practical information and data from our industrial application. By Alex Egg, accepted to Nvidia GTC 2021 Conference

This document provides an introduction to searching and seizing computers for computer forensics. It discusses issues with digital evidence being volatile and massive in size. It explains that searching and seizing computers can be done with or without a warrant, depending on the country's constitution and exceptions like consent. Key aspects of searches include who conducted it, what was searched/seized, if it was a legal search/seizure, and if the search was reasonable with a warrant or under an exception. Probable cause and exceptions to warrants like emergencies, vehicles and borders are also outlined. Proper warrant preparation and seizing equipment on site are important parts of legally searching and seizing computer-related evidence.

This is a presentation in how to use Overleaf in Institutional Settings and how I derived benefits out of it.

Introduction to Zooz - Presentation by Oren Levy, Co-Founder & CEO of Zooz at the NOAH 2013 Conference in London, Old Billingsgate on the 13th of November 2013.