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This is a presentation in how to use Overleaf in Institutional Settings and how I derived benefits out of it.
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Collaborative writing technologies: Overleaf for institutions
"Collaborative writing technologies: Overleaf for institutions" - John Hammersley, Overleaf co-founder, and Helen Josephine, Head of the Terman Engineering Library at Stanford
Introduction to Overleaf Workshop
Video of Workshop - https://media.dlib.indiana.edu/media_objects/rj430941s
This is workshop offered via Social Science Research Center to students and faculty to become familiar with an online collaborative writing using Latex and Overleaf.
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The document provides a timeline of major events in the evolution of analytics from pre-2005 to 2015. Some of the key developments include the coining of terms like "data science" and "big data" in the late 1990s, the creation of Hadoop in 2005, major companies and technologies being founded or released between 2005-2009 laying the groundwork for analytics, rapid growth and adoption from 2010-2014 including predictive analytics uses in politics, widespread cloud and mobile analytics, and the rise of machine learning.
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This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
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2. What is Spark
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What are the course objectives?
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1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
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Introduction to Overleaf Workshop
Video of Workshop - https://media.dlib.indiana.edu/media_objects/rj430941s
This is workshop offered via Social Science Research Center to students and faculty to become familiar with an online collaborative writing using Latex and Overleaf.
A timeline view of Evolution of Analytics
The document provides a timeline of major events in the evolution of analytics from pre-2005 to 2015. Some of the key developments include the coining of terms like "data science" and "big data" in the late 1990s, the creation of Hadoop in 2005, major companies and technologies being founded or released between 2005-2009 laying the groundwork for analytics, rapid growth and adoption from 2010-2014 including predictive analytics uses in politics, widespread cloud and mobile analytics, and the rise of machine learning.
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Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Literature review and the PhD
Presentation by Professor Simon Haslett at the Institute of Life Science, Swansea University, Monday 4th July 2011.
Transformer based approaches for visual representation learning
1) Transformer-based approaches for visual representation learning such as Vision Transformers (ViTs) have shown promising performance compared to CNNs on image classification tasks.
2) A pure Transformer architecture pre-trained on a very large dataset like JFT-300M can outperform modern CNNs without any convolutions.
3) Self-supervised pre-training methods like DINO that leverage knowledge distillation have been shown to obtain comparable performance to supervised pre-training of ViTs using only unlabeled ImageNet data.
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The document discusses bagging, an ensemble machine learning method. Bagging (bootstrap aggregating) uses multiple models fitted on random subsets of a dataset to improve stability and accuracy compared to a single model. It works by training base models in parallel on random samples with replacement of the original dataset and aggregating their predictions. Key benefits are reduced variance, easier implementation through libraries like scikit-learn, and improved performance over single models. However, bagging results in less interpretable models compared to a single model.
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Google BigQuery for Everyday Developer
IV. IT&C Innovation Conference - October 2016 - Sovata, Romania
A. Every scientist who needs big data analytics to save millions of lives should have that power
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B. The simple fact is that you are brilliant but your brilliant ideas require complex analytics.
Traditional solutions are not applicable.
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Goal: Store everything accessible by SQL immediately.
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Web Scraping
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2. Participants acknowledge the risks of paddling activities including serious injury, assume responsibility for their own actions, and agree their participation serves as consent for their image to be used in event materials.
3. The waiver releases the event organizers and others from all liability for claims related to the participant's involvement in the event, even if due to the organizers' negligence.
Ottawa Trip Part One
Miss Mabel takes a bus trip to Ottawa to visit various landmarks. She tours Rideau Hall and sees the grounds and memorial trees planted for various leaders. She walks around Parliament and sees the Peace Tower. It is a very hot day over 30 degrees Celsius. In the evening she visits the Museum of Civilization and crosses the bridge before returning home exhausted after a long 12-hour outing.
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NoSQL: It’s about making intelligent choices.
Making Sense of NoSQL.
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Apache Spark MLlib
This document provides an overview and agenda for a course on Spark MLlib. The course covers Spark fundamentals, SQL, streaming and MLlib. The MLlib section includes an overview of MLlib, a quick review of machine learning concepts, and why MLlib is useful. It describes the main concepts in MLlib like DataFrames, transformers, estimators and pipelines. It provides examples of classification using logistic regression on text data, regression to predict tweet impressions, and topic modeling on tweets. Finally, it lists some of the algorithms in MLlib, including classification, regression, clustering and tree ensemble methods.
Text categorization
The document discusses text categorization, which involves assigning categories or topics to documents. It covers key aspects of text categorization including definitions, applications, document representation, feature selection, dimensionality reduction, knowledge engineering and machine learning approaches. Specific classification algorithms discussed include naïve Bayes, Bayesian logistic regression, decision trees, decision rules, and more. The document provides details on how these algorithms work and their advantages/disadvantages for text categorization tasks.
Google BigQuery for Everyday Developer
IV. IT&C Innovation Conference - October 2016 - Sovata, Romania
A. Every scientist who needs big data analytics to save millions of lives should have that power
Legacy systems don’t provide the power.
B. The simple fact is that you are brilliant but your brilliant ideas require complex analytics.
Traditional solutions are not applicable.
The Plan: have oversight over developments as they happen.
Goal: Store everything accessible by SQL immediately.
What is BigQuery?
Analytics-as-a-Service - Data Warehouse in the Cloud
Fully-Managed by Google (US or EU zone)
Scales into Petabytes
Ridiculously fast
Decent pricing (queries $5/TB, storage: $20/TB) *October 2016 pricing
100.000 rows / sec Streaming API
Open Interfaces (Web UI, BQ command line tool, REST, ODBC)
Familiar DB Structure (table, views, record, nested, JSON)
Convenience of SQL + Javascript UDF (User Defined Functions)
Integrates with Google Sheets + Google Cloud Storage + Pub/Sub connectors
Client libraries available in YFL (your favorite languages)
Our benefits
no provisioning/deploy
no running out of resources
no more focus on large scale execution plan
no need to re-implement tricky concepts
(time windows / join streams)
pay only the columns we have in your queries
run raw ad-hoc queries (either by analysts/sales or Devs)
no more throwing away-, expiring-, aggregating old data.
2022.03.23 Conda and Conda environments.pptx
A presentation for the African Pathogen Genomics initiative at KEMRI-Wellcome in Kilifi Kenya on Conda and Conda environments. Includes a practical exercise.
Latent Dirichlet Allocation
This document provides an overview of Latent Dirichlet Allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. It defines key terminology for LDA including documents, words, topics, and distributions. The document then explains LDA's graphical model and generative process, which represents documents as mixtures over latent topics and generates words probabilistically from topics. Variational inference is introduced as an approach for approximating the intractable posterior distribution over topics and learning model parameters.
What is a survey paper
A survey paper comprises an author's interpretation and conclusions drawn from analyzing several already published research papers on a specific topic. The goals of a survey paper are to provide a well-organized and comprehensive view of existing work, cover all relevant material completely, and have a logical organizational structure. To write a survey paper, authors collect relevant papers, read them and take notes, then structure the paper following an Introduction, Methodologies, Discussion format and summarize 5-8 papers on a particular topic, including their own commentary on the significance of each paper's approach and solutions. Authors should search digital libraries and research groups to find papers on their topic and favor more recent papers from well-known sources.
Information retrieval-systems notes
This document provides an overview of an information retrieval system. It defines an information retrieval system as a system capable of storing, retrieving, and maintaining information such as text, images, audio, and video. The objectives of an information retrieval system are to minimize the overhead for a user to locate needed information. The document discusses functions like search, browse, indexing, cataloging, and various capabilities to facilitate querying and retrieving relevant information from the system.
Spark
Spark and Resilient Distributed Datasets addresses the need for efficient data sharing across iterative and interactive queries in large clusters. It proposes an in-memory data processing framework called Spark, using a distributed data structure called Resilient Distributed Datasets (RDDs) that allow data to be cached in memory across jobs. RDDs act as a fault-tolerant distributed shared memory, avoiding the need to write to stable storage between jobs and enabling more efficient data sharing compared to MapReduce.
Topic Modeling
This document provides an overview of Bayes law, Bayesian networks, and latent Dirichlet allocation (LDA). It begins with an explanation of Bayes law and examples of how it can be used. Next, it defines Bayesian networks as probabilistic graphical models and provides examples. Finally, it introduces LDA as a statistical model for collections of discrete data like text corpora and explains how it can be used for topic modeling. The document includes mathematical notation and diagrams to illustrate key concepts.
Building Streaming Data Pipelines with Google Cloud Dataflow and Confluent Cl...
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Tuning ML Models: Scaling, Workflows, and Architecture
This document discusses best practices for tuning machine learning models. It covers architectural patterns like single-machine versus distributed training and training one model per group. It also discusses workflows for hyperparameter tuning including setting up full pipelines before tuning, evaluating metrics on validation data, and tracking results for reproducibility. Finally it provides tips for handling code, data, and cluster configurations for distributed hyperparameter tuning and recommends tools to use.
Wrapper feature selection method
Methods wrapper evaluate subsets of variables which allows, unlike filter approaches, to detect the possible interactions between variables.
Big Data Processing with Spark and Scala
In this era of ever growing data, the need for analyzing it for meaningful business insights becomes more and more significant. There are different Big Data processing alternatives like Hadoop, Spark, Storm etc. Spark, however is unique in providing batch as well as streaming capabilities, thus making it a preferred choice for lightening fast Big Data Analysis platforms.
Web Scraping
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Miss Mabel takes a bus trip to Ottawa to visit various landmarks. She tours Rideau Hall and sees the grounds and memorial trees planted for various leaders. She walks around Parliament and sees the Peace Tower. It is a very hot day over 30 degrees Celsius. In the evening she visits the Museum of Civilization and crosses the bridge before returning home exhausted after a long 12-hour outing.
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- 1. Using Overleaf for Collaboration, Dissertations, Grants, & Teaching Arindam Basu School of Health Sciences University of Canterbury, Christchurch, New Zealand October 4, 2016
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