The document discusses reactive approaches to collecting and modeling uncertain data from sensors in a distributed system. It presents reactive techniques like using queues and immutable data structures to handle out-of-order and concurrent updates at scale. Distributed databases allow scaling data collection across nodes while handling incomplete data from failures. The overall approach focuses on reactive systems principles of responding to changes instead of shared mutable state.
Apprentissage statistique et analyse prédictive en Python avec scikit-learn p...La Cuisine du Web
Avec plus de 300 000 utilisateurs réguliers, scikit-learn (http://scikit-learn.org) est la librairie de référence pour le machine learning en Python. scikit-learn couvre l’apprentissage supervisé (régression, classification) et non-supervisé (clustering, détection d’anomalie, réduction de dimension) scikit-learn est construit sur l’écosystème du Python scientifique Numpy, Scipy et Cython.
Recommender Systems with Apache Spark's ALS FunctionWill Johnson
A quick visual guide to recommender systems (user based, item based, and matrix factorization) and the code behind making an apache spark MatrxFactorization Model with the ALS function.
Python is the choice llanguage for data analysis,
The aim of this slide is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of the steps you need to learn to use Python for data analysis.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
The document discusses recent developments in the R programming environment for data analysis, including packages like magrittr, readr, tidyr, and dplyr that enable data wrangling workflows. It provides an overview of the key functions in these packages that allow users to load, reshape, manipulate, model, visualize, and report on data in a pipeline using the %>% operator.
The document discusses the Java Collections Framework, which includes interfaces like Collection, List, Set, and Map. It describes common implementations like ArrayList, LinkedList, HashSet, TreeSet, HashMap, and LinkedHashMap. It covers the core functionality provided by the interfaces and benefits of using the framework.
Collections in Java include arrays, iterators, and interfaces like Collection, Set, List, and Map. Arrays have advantages like type checking and known size but are fixed. Collections generalize arrays, allowing resizable and heterogeneous groups through interfaces implemented by classes like ArrayList, LinkedList, HashSet and HashMap. Common operations include adding, removing, and iterating over elements.
The document provides information about Java's collection framework. It discusses the key interfaces like List, Set, and Map. It describes common implementations of these interfaces like ArrayList, LinkedList, HashSet, TreeSet, HashMap. It explains concepts like iterators, storage of elements in ArrayList and Vector, differences between ArrayList and Vector. It also provides examples of using ArrayList, Vector, HashSet and TreeMap.
The document discusses different strategies for retrieving objects from a database using Hibernate including retrieval by identifier, HQL queries, and criteria queries. It also describes Hibernate fetching strategies like lazy fetching, eager fetching, and batch fetching which can be used to minimize database access and solve the "n+1 selects" problem of loading associated objects. The "n+1 selects" problem occurs when lazy loading associated collections, resulting in n queries to load the parent objects and then n additional queries - one for each collection. This problem can be addressed using batch fetching, eager fetching, or fetching associations within queries.
Data preprocessing for Machine Learning with R and PythonAkhilesh Joshi
The document describes the steps for data preprocessing in Python and R. These include importing and reading the dataset, handling missing data through imputation, encoding categorical variables, splitting the data into training and test sets, and scaling numeric features. Key preprocessing steps are performed similarly in both languages, such as imputing missing values, splitting data, and feature scaling. However, encoding categorical variables differs between one-hot encoding in Python versus factorizing in R.
This document provides an overview of Java collection classes and interfaces. It discusses the Collection framework, commonly used methods for Collection, List, Iterator, ArrayList, LinkedList, Set, Queue, Map, Entry, and sorting. The key classes covered are Collection, List, Iterator, ArrayList, LinkedList, HashSet, Queue, Map, and Entry. It explains the purpose of each interface and differences between data structures like ArrayList vs LinkedList, List vs Set.
This document provides an overview of machine learning in R. It discusses R's capabilities for statistical analysis and visualization. It describes key R concepts like objects, data structures, plots, and packages. It explains how to import and work with data, perform basic statistics and machine learning algorithms like linear models, naive Bayes, and decision trees. The document serves as an introduction for using R for machine learning tasks.
This presentation introduces some concepts about the Java Collection framework. These slides introduce the following concepts:
- Collections and iterators
- Linked list and array list
- Hash set and tree set
- Maps
- The collection framework
The presentation is took from the Java course I run in the bachelor-level informatics curriculum at the University of Padova.
This talk has been given at the 13th International Conference on Principles of Knowledge Representation and Reasoning (KR 2012) to be held in Rome, Italy, June 10-14, 2012 by Ilias Tahmazidis (FORTH).
Abstract:
We are witnessing an explosion of available data from the Web, government authorities, scientific databases, sensors and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling the vast amounts of data for these applications. In this paper, we consider nonmonotonic reasoning, which has traditionally focused on rich knowledge structures. In particular, we consider defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge data sets. Our experimental results demonstrate that defeasible reasoning with billions of data is performant, and has the potential to scale to trillions of facts.
This document discusses the collection framework in Java. It provides an overview of the need for collections due to limitations of arrays. It then describes the key interfaces in the collection framework - Collection, List, Set, SortedSet, NavigableSet, Queue, Map, SortedMap, and NavigableMap. For each interface, it provides a brief description of its purpose and characteristics. It explains that collections allow storing heterogeneous data types with variable sizes, unlike arrays.
Text Classification in Python – using Pandas, scikit-learn, IPython Notebook ...Jimmy Lai
Big data analysis relies on exploiting various handy tools to gain insight from data easily. In this talk, the speaker demonstrates a data mining flow for text classification using many Python tools. The flow consists of feature extraction/selection, model training/tuning and evaluation. Various tools are used in the flow, including: Pandas for feature processing, scikit-learn for classification, IPython, Notebook for fast sketching, matplotlib for visualization.
Data Structures for Statistical Computing in PythonWes McKinney
The document discusses statistical data structures in Python. It summarizes that structured arrays are commonly used to store statistical data sets but have limitations. The R data frame is introduced as a flexible alternative that inspired the pandas library in Python. Pandas aims to create intuitive data structures for statistical analysis with labeled axes and automatic data alignment. Its core data structure, the DataFrame, functions similarly to R's data frame.
To learn important concept of Collection and its handling plus its advantages and different class & child class of Collection and their implementations. Important interview questions of the collection.
Reactive Machine Learning On and Beyond the JVMJeff Smith
The document discusses reactive machine learning on and beyond the JVM. It covers topics like reactive systems, strategies for building reactive systems, machine learning, and how these concepts come together in reactive machine learning systems. Examples are provided of building reactive machine learning models on the JVM for applications like fraud detection. The discussion explores taking these ideas further through technologies like Elixir and new approaches to knowledge representation.
Visual Data Representation Techniques Combining Art and DesignLogo Design Guru
Visually representing data is becoming increasingly popular. Companies are investing thousands of dollars in getting their data created using design elements. From large enterprises to small businesses, everyone is hunting for techniques to help them make dull and monotonous data into something attractive.
Designers thoroughly study data and then invest all these imagination into making it simpler for everyone to understand it. Minimizing information and making it universal is the key to data visualization.
From infographics to presentations and software to tools, there are many techniques one can use to enhance the look of spreadsheets, Big Data and analytics.
Here are visual techniques to help you display your data in an aesthetically pleasing way. You can use some of these or all of them. These tips are not limited to the web or print, but can also be used for television. In fact, weather forecasting channels use visuals like maps, icons and GIFs to represent information.
Want your data to stand out? Use these techniques to uplift your data.
The document discusses six emerging trends in business analytics:
1. Humans and machines will increasingly work together in complementary roles, with machines handling tasks like data processing and humans focusing on creativity, empathy, and oversight of machine performance.
2. Analytics capabilities are expanding across entire organizations, moving from isolated initiatives to enterprise-wide strategies aimed at creating "insight-driven organizations."
3. Cybersecurity is becoming more important and proactive, utilizing predictive analytics to anticipate threats rather than just reacting to attacks.
4. The Internet of Things is expanding to include people and generating new business models by aggregating and analyzing behavioral data.
5. Companies are getting creative in addressing talent shortages, collaborating more closely
Reactive Machine Learning and Functional ProgrammingJeff Smith
The document discusses reactive machine learning and functional programming. It describes how reactive systems are responsive, resilient, elastic and message-driven. It then provides examples of how data collection events and functional transformations can be used in a reactive machine learning pipeline. Models are treated as pure functions and supervised through a model supervisor architecture. The document concludes by recommending several reactive resources and frameworks that can enable large-scale reactive machine learning.
As so many fields have in recent years, entry-level hiring must also make the transition from relying on untested intuition to leveraging the power of data and evidence. Employers now have access to talent analytics tools that can enable them to develop a deep understanding of what attributes drive good performance for their current employees, apply tools to objectively assess these attributes, and access broader talent pools to find individuals with the most-valued attributes. The talent analytics tools that enable this vision for data-driven hiring already exist. The key obstacle to their implementation is institutional will.
This document provides tips to improve Excel skills in order to work faster, look more professional, impress your boss, and grow your career. It includes nine demonstrations on better using Excel covering topics like custom formatting, moving averages, combining annual and monthly data, using trial balances, identifying and removing duplicates, data relationships, flexible budget models, toggling macros, and consistent styles. Videos and other resources from CPA Australia on Excel for finance professionals are also promoted.
Big Data is one of the most prominent disruptive technologies available today. The potential it offers for business is truly astounding.
But what is it? Time for a crashcourse!
DAMA Webinar - Big and Little Data QualityDATAVERSITY
While technological innovation brings constant change to the data landscape, many organizations still struggle with the basics: ensuring they have reliable, high quality data. In health care, the promise of insight to be gained through analytics is dependent on ensuring the interactions between providers and patients are recorded accurately and completely. While traditional health care data is dependent on person-to-person contact, new technologies are emerging that change how health care is delivered and how health care data is captured, stored, accessed and used. Using health care as a lens through which to understand the emergence of big data, this presentation will ask the audience to think about data in old and new ways in order to gain insight about how to improve the quality of data, regardless of size.
This document discusses visualizing data with code and provides information on tools and techniques for data visualization. It lists relevant fields like information design, data science, and cartography. It also lists example visualization tools and techniques like D3, Processing, network graphs, and mapping. Finally, it outlines a process for developing data visualizations that involves looking at the data, creating initial visualizations, asking questions, getting inspiration, refining ideas, and publishing visualizations.
The Net Promoter Score process involves a number of parameters which when worked together can provide the best outcome and can be very tricky to execute. This infographic highlights some pitfalls to avoid when running your next NPS campaign to churn out the best results out of it.
This document outlines Seth Familian's presentation on working with big data. It discusses key concepts like what constitutes big data, popular tools for working with big data like Splunk and Segment, and techniques for building dashboards and inferring customer segments from large datasets. Specific examples are provided of automated data flows that extract, load, transform and analyze big data from various sources to generate insights and populate customized dashboards.
An immersive workshop at General Assembly, SF. I typically teach this workshop at General Assembly, San Francisco. To see a list of my upcoming classes, visit https://generalassemb.ly/instructors/seth-familian/4813
I also teach this workshop as a private lunch-and-learn or half-day immersive session for corporate clients. To learn more about pricing and availability, please contact me at http://familian1.com
Bringing Data Scientists and Engineers TogetherJeff Smith
The document discusses bringing data scientists and engineers together in hiring, onboarding, building, operating, and succeeding as a team. It recommends hiring for real problems rather than titles, shipping work early and often, using ubiquitous language with clear data ownership, having those who build software also run it and own metrics, and charting a course while launching initiatives to succeed as a team.
El documento presenta una introducción al concepto de discurso desde diferentes disciplinas como la antropología y la lingüística. Luego, explica que un discurso puede definirse como una estructura verbal, un evento comunicativo cultural o una forma de interacción. Finalmente, detalla que un discurso consta de tres partes: la introducción, el desarrollo y la conclusión.
SPARK USE CASE- Distributed Reinforcement Learning for Electricity Market Bi...Impetus Technologies
SPARK SUMMIT SESSION -
A majority of the electricity in the U.S. is traded in independent system operator (ISO) based wholesale markets. ISO-based markets typically function in a two-step settlement process with day-ahead (DA) financial settlements followed by physical real-time (spot) market settlements for electricity. In this work, we focus on obtaining equilibrium bidding strategies for electricity generators in DA markets. Electricity prices in DA markets are determined by the ISO, which matches competing supply offers from power generators with demand bids from load serving entities. Since there are multiple generators competing with one another to supply power, this can be modeled as a competitive Markov decision problem, which we solve using a reinforcement learning approach. For power networks of realistic sizes, the state-action space could explode, making the RL procedure computationally intensive. This has motivated us to solve the above problem over Spark. The talk provides the following takeaways:
1. Modeling the day-ahead market as a Markov decision process
2. Code sketches to show the markov decision process solution over Spark and Mahout over Apache Tez
3. Performance results comparing Mahout over Apache Tez and Spark.
Bidding strategies in deregulated power marketGautham Reddy
This document discusses bidding strategies for power suppliers in deregulated electricity markets. It explains that deregulation allows competitive suppliers to enter the market and gives consumers a choice in suppliers. Bidding involves suppliers submitting quantity and cost bids to either buy or sell energy. A market clearing price is determined that balances supply and demand. The goal of strategic bidding is to maximize profits by constructing optimal bids based on costs and expectations of rivals. Mathematical models are presented for profit maximization using linear supply curves. The document also discusses using fuzzy adaptive gravitational search and genetic algorithms to determine optimal bidding coefficients.
The dab:group @AM:DataConsult Business Assurance Series (English)dabGroup
Read Mr. Stefan Wenig's (CEO of dab:Group) presentation at theAM:DataConsult Business Assurance Series.
A look at adhoc versus CCM approach for data analytics.
Este documento explica el significado y el poder de la bendición. Dar o recibir una bendición invoca el apoyo activo de Dios y trae prosperidad y felicidad. Las bendiciones comienzan en el hogar y dan a los niños un buen comienzo emocional. También fortalecen las relaciones de pareja y amistades. Al bendecir a otros, una persona también se bendice a sí misma. Vivir en presencia de Dios trae siempre su bendición divina.
Este documento trata sobre el valor educativo de los cuentos en los programas de intervención infantil. Explica que los cuentos estimulan la imaginación de los niños, desarrollan su inteligencia y les permiten identificar sentimientos. También describe las características de los cuentos, como su brevedad y ficción, y los elementos lingüísticos, imaginativos y psicológicos que potencian su valor educativo. Finalmente, discute la importancia de las técnicas de narración oral para contar cuentos de manera interactiva
This document introduces machine learning algorithms. It discusses supervised and unsupervised learning problems and strategies. It provides examples of machine learning applications including neural networks for handwritten digit recognition, evolutionary algorithms for nozzle design, and Bayesian networks for gene expression analysis.
This document introduces machine learning algorithms. It discusses supervised and unsupervised learning problems and strategies. It provides examples of machine learning applications including neural networks for handwritten digit recognition, evolutionary algorithms for nozzle design, and Bayesian networks for gene expression analysis.
The document discusses different techniques for intrusion detection systems, including misuse detection, anomaly detection, pattern matching, and machine learning methods. It proposes two ideas for improving intrusion detection: 1) using association pattern detecting to match patterns in sequential data, and 2) discovering new patterns from existing rule sets using data mining or machine learning.
This document summarizes a study on using data mining techniques like multiple linear regression and density-based clustering to estimate crop production in East Godavari district of India. Multiple linear regression and density-based clustering were used to model the relationship between crop production and factors like rainfall, area sown, fertilizer use. The estimated values from both techniques were found to have a percentage difference ranging from -14% to 13% when compared to actual production values, indicating the techniques can adequately estimate crop production. Tables of actual versus estimated values using both techniques are provided for comparison.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
SciPy and NumPy are Python packages that provide scientific computing capabilities. NumPy provides multidimensional array objects and fast linear algebra functions. SciPy builds on NumPy and adds modules for optimization, integration, signal and image processing, and more. Together, NumPy and SciPy give Python powerful data analysis and visualization capabilities. The community contributes to both projects to expand their functionality. Memory mapped arrays in NumPy allow working with large datasets that exceed system memory.
Supervised vs unsupervised learning - infographicIntellspot
Supervised learning uses labeled input data to teach a machine using examples to predict future events, while unsupervised learning sorts unlabeled data without predefined labels by finding hidden patterns in the data. Supervised learning is used for applications like credit card fraud detection and text sentiment analysis and has labeled classification and regression algorithms, while unsupervised learning is applied to problems like image segmentation, social network analysis and anomaly detection using clustering and association algorithm types.
Machine Learning : why we should know and how it worksKevin Lee
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 presentation discusses computer vision techniques for human tracking and interaction. It begins with an outline of the topics to be covered, including basic visual tracking, multi-cue particle filtering for tracking, multi-human tracking, multi-camera tracking, and handling re-entering people. It then describes implementations of basic color-based tracking, particle filtering with multiple cues, and using particle filtering for human head tracking. Challenges with overlapping people are addressed through joint candidate evaluation and sorting by depth. The multi-camera system correlates tracks across cameras to identify corresponding people. Overall, the presentation explains a complete visual tracking and surveillance system using computer vision algorithms.
The document discusses using support vector machines (SVMs) for intrusion detection on virtual machines. It provides background on virtual machines, intrusion detection systems, and SVMs. It then describes using a two-class SVM approach on a synthetic dataset containing normal and abnormal workload data from virtual machines to detect intrusions. The SVM model performed well, accurately detecting intrusions in over 80% of cases. The document concludes that machine learning techniques like SVMs show promise for developing accurate intrusion detection systems for virtual machines.
The document discusses different techniques for automatically fusing extracted annotations from multiple data sources. It outlines approaches for handling inconsistencies by applying uncertainty reasoning and overcoming schema heterogeneity. Specific techniques discussed include using a problem-solving method to decompose the fusion task, selecting methods based on their capabilities, propagating beliefs in a valuation network, and refining data using a neighborhood graph.
Introduction to Deep Learning and neon at GalvanizeIntel Nervana
The document provides an introduction to deep learning and the Nervana framework. It discusses the speaker's background and Intel's Artificial Intelligence Products Group. It then covers machine learning concepts, a brief history of deep learning, neural network architectures, training procedures, and examples of computer vision applications for deep learning like image classification. Use cases for recurrent neural networks and long short-term memory networks are also mentioned.
Towards Efficient Privacy-preserving Image Feature Extraction in Cloud ComputingSi Chen
As the image data produced by individuals and enterprises is rapidly increasing, Scalar Invariant Feature Transform (SIFT), as a local feature detection algorithm, has been heavily employed in various areas, including object recognition, robotic mapping, etc. In this context, there is a growing need to outsource such image computation with high complexity to cloud for its economic computing resources and on-demand ubiquitous access. However, how to protect the private image data while enabling image computation becomes a major concern. To address this fundamental challenge, we study the privacy requirements in outsourcing SIFT computation and propose SecSIFT, a high performance privacy-preserving SIFT feature detection system. In previous private image computation works, one common approach is to encrypt the private image in a public key based homomorphic scheme that enables the original processing algorithms designed for plaintext domain to be performed over ciphertext domain. In contrast to these works, our system is not restricted by the efficiency limitations of homomorphic encryption scheme. The proposed system distributes the computation procedures of SIFT to a set of independent, co-operative cloud servers, and keeps the outsourced computation procedures as simple as possible to avoid utilizing homomorphic encryption scheme. Thus, it enables implementation with practical computation and communication complexity. Extensive experimental results demonstrate that SecSIFT performs comparably to original SIFT on image benchmarks while capable of preserving the privacy in an efficient way.
International Journal of Computational Engineering Research(IJCER)ijceronline
This document summarizes a research paper that proposes a novel approach to improve the detection rate and search efficiency of signature-based network intrusion detection systems (NIDS). The approach uses data mining and classification algorithms like C4.5 and ensemble algorithms like MadaBoost to improve detection rates. It also uses a modified signature apriori algorithm to more efficiently search for signatures of related attacks based on known signatures, in order to improve search efficiency. The full paper describes these approaches in more technical detail and evaluates their effectiveness at improving NIDS performance.
This document provides an introduction and overview of data mining and the data mining process. It discusses different types of data like transactional data, temporal data, spatial data, and unstructured data. It also covers common data mining tasks like classification, clustering, association rule mining and frequent pattern mining. Additionally, it discusses related fields like statistics, machine learning, databases and visualization and how they differ from data mining. Finally, it provides examples of different data mining models and tasks.
This document discusses intrusion detection techniques. It defines intrusion detection as the process of identifying intrusions, which are activities that violate a system's security policy. It describes different types of intrusion detection systems including host-based, distributed, and network-based. The main intrusion detection techniques are described as misuse detection, which detects known attacks, and anomaly detection, which detects deviations from normal behavior. Ideas for improving intrusion detection include using association pattern detecting to match patterns in sequential data and discovering new patterns by combining existing rulesets from different intrusion detection systems.
This document discusses using Bayesian networks for predictive analysis and machine learning perspectives on data utilization. It provides an example of using Bayesian networks to accurately predict incident clearance time based on variables like type of incident, number of police/ambulance vehicles, number of injuries, and number of vehicles involved. The document also discusses applying Bayesian networks by collecting current situation data as evidence to perform inference on a constructed inference model.
Machine learning is a branch of artificial intelligence concerned with building systems that can learn from data. The document discusses various machine learning concepts including what machine learning is, related fields, the machine learning workflow, challenges, different types of machine learning algorithms like supervised learning, unsupervised learning and reinforcement learning, and popular Python libraries used for machine learning like Scikit-learn, Pandas and Matplotlib. It also provides examples of commonly used machine learning algorithms and datasets.
What the Brain says about Machine Intelligence Numenta
This document discusses machine intelligence and the cortical theory of intelligence. It begins by comparing approaches to computing in the 1940s-1950s and 2010s-2020s, noting that while many approaches existed, one dominant paradigm eventually emerged in both eras due to flexibility and scalability. It then outlines Numenta's cortical theory, including hierarchical temporal memory (HTM), and how HTM models the neocortex. The document details Numenta's research applying HTM to areas like anomaly detection, language processing, and vision. It argues HTM may be the dominant machine intelligence paradigm due to the neocortex's success and HTM's ability to model the neocortex's common algorithms across modalities.
Similar to Collecting Uncertain Data the Reactive Way (20)
This document poses a series of questions about the capabilities and limitations of conversational AI. It questions whether the AI can remember or forget information, understand names and pronouns, learn and how quickly or how it is taught. It also questions the AI's ability to understand language, become confused, speak, listen, respect privacy, and handle being busy or damaged. The questions cover the AI's knowledge, understanding, learning, certainty, language, voice, security, and infrastructure.
This document discusses using neuroevolution techniques to evolve neural networks represented as ONNX models in Elixir. It describes representing neural network genotypes as ONNX graphs that can be mutated and evolved over generations. Mutation functions are defined that modify parameters in the ONNX models, and an approach is outlined for loading ONNX models into Elixir and mutating their attributes to evolve new models. Opportunities are mentioned for developing additional tools for working with ONNX models in Elixir.
The document discusses the design of reactive learning agents. It describes how agents can utilize sensors and actuators to interact with their environment, as well as knowledge sources and machine learning to guide their functions. Reactive systems are highlighted as they can be responsive, resilient, and elastic by leveraging messaging. The BEAM virtual machine and Elixir language are presented as platforms for building such agents, and various testing and validation techniques are outlined.
This document discusses how machine learning teams can adopt a reactive approach to building ML systems. It describes the typical components of a naive ML architecture, including data collectors, pipelines, model publishers and servers. It then explains how adopting reactive traits like being responsive, elastic and resilient can help ML systems handle large, fast and hairy problems like varying data loads, communication failures and system impacts. The document advocates for strategies like using metrics and containment to build on the success of reactive systems and help machine learning teams scale their work.
The document discusses using Elixir and various tools like Dialyzer and Concuerror for building machine learning systems. It covers topics like ensemble models, feature generation, applying models, and testing concurrency issues. Dialyzer is used to check for type errors and behaviors like functions with no local returns. Concuerror finds concurrency errors by exploring possible process interleavings and checks for issues like processes blocked on receives. The document also briefly mentions building a model registry and reactive machine learning.
Huhdoop?: Uncertain Data Management on Non-Relational Database SystemsJeff Smith
This document discusses approaches for managing uncertain and non-deterministic data in non-relational database systems like Hadoop and HBase. It presents a model for representing sensor data uncertainty through probability density functions and uncertain intervals. It also examines different types of queries for uncertain data, such as value-based queries to retrieve a single record, value sum queries to aggregate values, and entity-based queries involving probabilistic assignments of sensor readings to ranges. The document evaluates strategies for implementing these queries efficiently in systems like Hive and Pig, and also discusses open questions and opportunities for further optimization.
Breadth or Depth: What's in a column-store?Jeff Smith
This presentation discusses the advantages of column-oriented databases and data storage. It summarizes different database technologies like HBase and Cassandra, and how column-oriented storage is better suited for certain tasks like analytics. It also talks about future innovations for column databases, including deeper column hierarchies and more distributed and flexible schemas.
This document discusses various open source monitoring tools that can be used to monitor heterogeneous servers and prevent failures. It introduces tools like Ganglia for monitoring, Nagios for alerts, Munin for simple monitoring, Collectd to collect data from servers, and RRD as a backend database standard. It encourages selecting tools based on architecture and use case, and provides examples of companies like Etsy that use these tools to achieve real-time monitoring and prevent failures. The overall message is that using these free and reliable open source monitoring tools can help system administrators prevent server failures and save the world.
This document provides an overview of NoSQL databases and discusses some of their advantages over traditional relational databases. It introduces some common NoSQL database types and properties like CAP theorem. Functional programming concepts like MapReduce that were influential in NoSQL are described. The document also compares the transactional consistency models used by SQL databases and newer NoSQL databases.
The Rise of Python in Finance,Automating Trading Strategies: _.pdfRiya Sen
In the dynamic realm of finance, where every second counts, the integration of technology has become indispensable. Aspiring traders and seasoned investors alike are turning to coding as a powerful tool to unlock new avenues of financial success. In this blog, we delve into the world of Python live trading strategies, exploring how coding can be the key to navigating the complexities of the market and securing your path to prosperity.
How AI is Revolutionizing Data Collection.pdfPromptCloud
Artificial Intelligence (AI) is transforming the landscape of data collection, making it more efficient, accurate, and insightful than ever before. With AI, businesses can automate the extraction of vast amounts of data from diverse sources, analyze patterns in real-time, and gain deeper insights with minimal human intervention. This revolution in data collection enables companies to make faster, data-driven decisions, enhance their competitive edge, and unlock new opportunities for growth.
AI-powered tools can handle complex and dynamic web content, adapt to changes in website structures, and even understand the context of data through natural language processing. This means that data collection is not only faster but also more precise, reducing the time and effort required for manual data extraction. Furthermore, AI can process unstructured data, such as social media posts and customer reviews, providing valuable insights into customer sentiment and market trends.
Embrace the future of data collection with AI and stay ahead of the curve. Learn more about how PromptCloud’s AI-driven web scraping solutions can transform your data strategy. https://www.promptcloud.com/contact/
Combined supervised and unsupervised neural networks for pulse shape discrimi...Samuel Jackson
Our methodology for pulse shape discrimination is split into two steps. Firstly, we learn a model to discriminate between pulses using "clean" low-rate examples by removing pile-up & saturated events. In addition to traditional tail sum discrimination, we investigate three different choices for discrimination between γ-pulses, fast, thermal neutrons. We consider clustering the pulses directly using Gaussian Mixture Modelling (GMM), using variational autoencoders to learn a representation of the pulses and then clustering the learned representation (VAE+GMM) and using density ratio estimation to discriminate between a mixed (γ + neutron) and pure (γ only) sources using a multi-layer perceptron (MLP) as a supervised learning problem.
Secondly, we aim to classify and recover pile-up events in the < 150 ns regime by training a single unified multi-label MLP. To frame the problem as a multi-label supervised learning method, we first simulate pile-up events with known components. Then, using the simulated data and combining it with single event data, we train a final multi-label MLP to output a binary code indicating both how many and which type of events are present within an event window.
Harnessing Wild and Untamed (Publicly Available) Data for the Cost efficient ...weiwchu
We recently discovered that models trained with large-scale speech datasets sourced from the web could achieve superior accuracy and potentially lower cost than traditionally human-labeled or simulated speech datasets. We developed a customizable AI-driven data labeling system. It infers word-level transcriptions with confidence scores, enabling supervised ASR training. It also robustly generates phone-level timestamps even in the presence of transcription or recognition errors, facilitating the training of TTS models. Moreover, It automatically assigns labels such as scenario, accent, language, and topic tags to the data, enabling the selection of task-specific data for training a model tailored to that particular task. We assessed the effectiveness of the datasets by fine-tuning open-source large speech models such as Whisper and SeamlessM4T and analyzing the resulting metrics. In addition to openly-available data, our data handling system can also be tailored to provide reliable labels for proprietary data from certain vertical domains. This customization enables supervised training of domain-specific models without the need for human labelers, eliminating data breach risks and significantly reducing data labeling cost.
Introduction to Data Science
1.1 What is Data Science, importance of data science,
1.2 Big data and data Science, the current Scenario,
1.3 Industry Perspective Types of Data: Structured vs. Unstructured Data,
1.4 Quantitative vs. Categorical Data,
1.5 Big Data vs. Little Data, Data science process
1.6 Role of Data Scientist
34. Uncertain Data Model
case class PreyReading(sensorId: Int,
locationId: Int,
timestamp: Long,
animalsLowerBound: Double,
animalsUpperBound: Double,
percentZebras: Double)
39. Mutable State
case class Region(id: Int)
import collection.mutable.HashMap
var densities = new HashMap[Region, Double]()
densities.put(Region(4), 52.4)
43. Out of Order Updates
densities.put(Region(6), 73.6)
densities.put(Region(6), 0.5)
densities.get(Region(6)).get
44. Out of Order Updates
densities.put(Region(6), 73.6)
densities.put(Region(6), 0.5)
densities.get(Region(6)).get
densities.put(Region(6), 0.5)
densities.put(Region(6), 73.6)
densities.get(Region(6)).get