Azure ml screen grabs

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Azure Machine Learning is a Microsoft service for building, training, and deploying machine learning models. It provides a graphical interface and various tools to help users without expertise in data science or programming build machine learning solutions. The service aims to make machine learning more accessible and help users apply it to their business.

Azure Machine Learning
Eleven still frame-grabbed screenshots from Azure ML promotional
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Alt-PrtScn’d by Andrew Brust

This document provides an overview of anomaly detection techniques. It discusses using statistical methods like the multivariate Gaussian distribution to model normal behavior and flag anomalies. Features that may indicate anomalies like CPU load, memory usage and temperature are selected. A model is fit to training data and new data is scored to detect outliers. Evaluation on labeled validation data helps choose algorithms and parameters. Overall it introduces concepts like parameter estimation, density estimation, and evaluating anomaly detection systems.

Map reduce prashant

Prashant Gupta

This document discusses various concepts related to Hadoop MapReduce including combiners, speculative execution, custom counters, input formats, multiple inputs/outputs, distributed cache, and joins. It explains that a combiner acts as a mini-reducer between the map and reduce stages to reduce data shuffling. Speculative execution allows redundant tasks to improve performance. Custom counters can track specific metrics. Input formats handle input splitting and reading. Multiple inputs allow different mappers for different files. Distributed cache shares read-only files across nodes. Joins can correlate large datasets on a common key.

Introduction to GraphQL

Amazon Web Services

Testing Hadoop jobs with MRUnit

Eric Wendelin

MRUnit is a testing library that makes it easier to test Hadoop jobs. It allows programmatically specifying test input and output, reducing the need for external test files. Tests can focus on individual map and reduce functions. MRUnit abstracts away much of the boilerplate test setup code, though it has some limitations like a lack of distributed testing. Overall though, the benefits of using MRUnit to test Hadoop jobs outweigh the problems.

Machine Learning in R

Alexandros Karatzoglou

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.

Html

irshadahamed

Introduction to GraphQL

Amazon Web Services

GraphQL is a query language for APIs and a runtime for fulfilling those queries. It gives clients the power to ask for exactly what they need, which makes it a great fit for modern web and mobile apps. In this talk, we explain why GraphQL was created, introduce you to the syntax and behavior, and then show how to use it to build powerful APIs for your data. We will also introduce you to AWS AppSync, a GraphQL-powered serverless backend for apps, which you can use to host GraphQL APIs and also add real-time and offline capabilities to your web and mobile apps. You can follow along if you have an AWS account – no GraphQL experience required! Level: Beginner Speaker: Rohan Deshpande - Sr. Software Dev Engineer, AWS Mobile Applications

All About MATLAB

Multisoft Virtual Academy

LangChain is an advanced framework that allows developers to create language model-powered applications. It provides a set of tools, components, and interfaces that make building LLM-based applications easier. With LangChain, managing interactions with language models, chaining together various components, and integrating resources like APIs and databases is a breeze. The platform includes a set of APIs that can be integrated into applications, allowing developers to add language processing capabilities without having to start from scratch.

HTML Common Tags -1

Vikram Nandini

This document provides an overview of common HTML tags including images, lists, tables, forms, and frames. It begins with an introduction to HTML and basic tags like <html>, <head>, <title>, and <body>. Each section then demonstrates and provides examples of the given tag type. For images, it shows how to add an image with <img> and provide a source, width, and height. Lists are demonstrated using the <ul> and <ol> tags for unordered and ordered lists. Tables are created with <table>, <tr>, and <td> tags. Forms are made using the <form> tag to collect user input. Finally, frames are explained as using the <frameset> tag

Introduction to GraphQL: Mobile Week SF

Amazon Web Services

AWS Mobile Week at the San Francisco Loft Introduction to GraphQL GraphQL is a query language for APIs and a runtime for fulfilling those queries. It gives clients the power to ask for exactly what they need, which makes it a great fit for modern web and mobile apps. In this talk, we explain why GraphQL was created, introduce you to the syntax and behavior, and then show how to use it to build powerful APIs for your data. We will also introduce you to AWS AppSync, a GraphQL-powered serverless backend for apps, which you can use to host GraphQL APIs and also add real-time and offline capabilities to your web and mobile apps. You can follow along if you have an AWS account – no GraphQL experience required! Level: Beginner Speaker: Rohan Deshpande - Sr. Software Developer Engineer, AWS Mobile Applications

Chat GPT.ppsx

HarshaSJois1

This document discusses Chat GPT, an AI assistant created by Anthropic to be helpful, harmless, and honest. It summarizes Chat GPT's capabilities, including answering text-based prompts, using large language models, and employing techniques like NLP. The document also outlines some advantages of Chat GPT like budget-friendly access, mostly correct responses, and optimized answers. Potential disadvantages discussed include limited training data and lack of non-English support or updates. It concludes by noting Chat GPT's rapid adoption and expectations for future, more powerful models.

NoSQL databases, the CAP theorem, and the theory of relativity

Lars Marius Garshol

The document discusses NoSQL databases and the CAP theorem. It begins by providing an overview of NoSQL databases, their key features like being schemaless and supporting eventual consistency over ACID transactions. It then explains the CAP theorem - that a distributed system can only provide two of consistency, availability, and partition tolerance. It also discusses how Google's Spanner database achieves consistency and scalability using ideas from Lamport's Paxos algorithm and a new time service called TrueTime.

Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...

Simplilearn

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

Natural language processing and its application in ai

Ram Kumar

This document provides an overview of natural language processing (NLP). It defines NLP as the technology used by machines to understand, analyze, and generate human languages. The document then discusses the history and development of NLP, its advantages and disadvantages, key components including natural language understanding and generation, common applications such as question answering and machine translation, and the basic steps to build an NLP pipeline including sentence segmentation, tokenization, stemming/lemmatization, stop word removal, and part-of-speech tagging. Code examples using the NLTK library are also provided to demonstrate several of these NLP techniques.

Made to Measure: Ranking Evaluation using Elasticsearch

Daniel Schneiter

The document describes ranking evaluation in Elasticsearch using its rank_eval API. Ranking evaluation allows measuring search quality through repeatable testing across different user needs. It defines typical search queries and rates documents to calculate metrics like precision, reciprocal rank, and discounted cumulative gain. This provides a way to quickly iterate on search and optimize for more user needs compared to methods like A/B testing. The demo uses Wikipedia data to illustrate the ranking evaluation process and API.

Unsupervised learning (clustering)

Pravinkumar Landge

This document discusses various unsupervised machine learning clustering algorithms. It begins with an introduction to unsupervised learning and clustering. It then explains k-means clustering, hierarchical clustering, and DBSCAN clustering. For k-means and hierarchical clustering, it covers how they work, their advantages and disadvantages, and compares the two. For DBSCAN, it defines what it is, how it identifies core points, border points, and outliers to form clusters based on density.

NumPy.pptx

DrJasmineBeulahG

Graphql Intro (Tutorial and Example)

Rafael Wilber Kerr

Clustering in data Mining (Data Mining)

Mustafa Sherazi

Natural Language Processing (NLP)

Yuriy Guts

Introduction to NumPy

Huy Nguyen

NumPy is a Python library used for working with multi-dimensional arrays and matrices for scientific computing. It allows fast operations on large data sets and arrays. NumPy arrays can be created from lists or ranges of values and support element-wise operations via universal functions. NumPy is the foundation of the Python scientific computing stack and provides key features like broadcasting for efficient computations.

Presentation - Rational Unified Process

Sharad Srivastava

This document provides an overview of the Rational Unified Process (RUP). It discusses the phases of RUP which include inception, elaboration, construction, and transition. It also discusses the core workflows, best practices, and tools used in RUP. The document outlines the dynamic and static structures of RUP, describing the phases, milestones, roles, activities, artifacts, and workflows. It provides details on the objectives and goals of each phase in the RUP lifecycle.

Regular Expression

Mahzad Zahedi

This document provides an introduction and overview of regular expressions (regex). It begins with an introduction to regex and how they are used. It then outlines the table of contents which covers literal characters, character classes, anchors, boundaries, alternation, optional and repetitive items, and grouping. The document discusses topics like literal characters, special characters, character classes, shorthand classes, negated classes, metacharacters inside classes, dot matching, and start/end of string anchors. It provides examples and explanations of how regex engines work to match patterns.

Machine learning

Dr Geetha Mohan

1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed. 2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data. 3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.

NUMPY

SharmilaChidaravalli

NumPy is a Python library that provides multidimensional array and matrix objects to perform scientific computing. It contains efficient functions for operations on arrays like arithmetic, aggregation, copying, indexing, slicing, and reshaping. NumPy arrays have advantages over native Python sequences like fixed size and efficient mathematical operations. Common NumPy operations include elementwise arithmetic, aggregation functions, copying and transposing arrays, changing array shapes, and indexing/slicing arrays.

Introduction to Machine Learning with SciKit-Learn

Benjamin Bengfort

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.

GraphQL as an alternative approach to REST (as presented at Java2Days/CodeMon...

luisw19

Originally designed by Facebook to allow its mobile clients to define exactly what data should be send back by an API and therefore avoid unnecessary roundtrips and data usage, GraphQL is a JSON based query language for Web APIs. Since it was open sourced by Facebook in 2015, it has undergone very rapid adoption and many companies have already switch to the GraphQL way of building APIs – see http://GraphQL.org/users. However, with some many hundreds of thousands of REST APIs publicly available today (and many thousands others available internally), what are the implications of moving to GraphQL? Is it really worth the effort of replacing REST APIs specially if they’re successful and performing well in production? What are the pros/cons of using GraphQL? What tools / languages can be used for GraphQL? What about API Gateways? What about API design? With a combination of rich content and hands-on demonstrations, attend this session for a point of view on how address these and many other questions, and most importantly get a better understanding and when/where/why/if GraphQL applies for your organisation or specific use case.

Big Data Strategy for the Relational World

Azure ml screen grabs

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