Monary
•Download as PPT, PDF•
2 likes•589 views
O
odscThis document discusses scientific analysis using MongoDB and NumPy. The document is from a presentation given by Anna Herlihy at the 2015 Open Data Science Conference in Boston. It focuses on performing scientific analysis with MongoDB to store data and NumPy for computations.
Report
Share
Report
Share
Recommended
Numpy tutorial(final) 20160303
https://docs.scipy.org/doc/numpy-dev/user/quickstart.html
에 내용 중 Shape Manipulation과 Copies And Views 부분에 대한 발표자료.
Introduction to NumPy (PyData SV 2013)
NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
Understanding the Chief Data Officer
As the importance of having a data strategy in place is sinking in, many organizations have added a chief data officer (CDO) to their executive team to help create and implement that strategy. But every organization is doing this a little bit differently. This talk will describe how a variety of industries and organizations are using CDOs and will make recommendations for best practices.
Machine-In-The-Loop for Knowledge Discovery
I’ll present the new knowledge discovery tools we are building at Diffeo. Unlike traditional search engines that use keywords, Diffeo provides an in-browser knowledge base that accelerates information gathering about people, companies, chemical compounds, cyber events, or other real world entities. I’ll describe how Diffeo uses active learning to encourage long and deep user interactions in order to recommend new content for in-progress articles. As you write, the search results get better and more interesting, because the system can see more precisely which entity you mean and which you don’t (disambiguation) and also what you don’t know yet about the entity (discovery).
Finally in this presentation I’ll describe our experience organizing the Text REtrieval Conference (TREC) on Knowledge Base Acceleration (KBA) and Dynamic Domain (DD) which are pushing the state of the art in knowledge discovery on large streams. I’ll show you how to access the largest corpus of streaming text data ever released for public evaluations.
API Driven Development
An exposé on human-centered design, as related to data science and “medium data”. Examples of great API design will be showcased, as well as other end-user facing tools that can enable data scientists to share their observations with the world.
Mobile technology Usage by Humanitarian Programs: A Metadata Analysis
This document summarizes an analysis of metadata from the CommCare mobile data platform, which is used by hundreds of humanitarian programs worldwide. The analysis examined CommCare usage data to better understand how frontline workers and programs utilize the technology. Key findings included: 1) Frontline workers develop proficiency quickly in their first year but then level off; 2) Worker activity levels remain relatively stable month-to-month; 3) Worker productivity does not follow a normal distribution. The metadata analysis can help programs monitor performance and identify factors that lead to improvement over time.
Productionizing Deep Learning From the Ground Up
This document discusses productionizing deep learning from the ground up. It begins with an overview of deep learning and neural networks, explaining that deep learning performs pattern recognition on unlabeled and unstructured data using deep neural networks with three or more layers. It then discusses challenges like the computational intensity of deep learning models and the need for special hardware like GPUs. It also covers software engineering concerns in scaling deep learning to production, such as data pipelines, maintenance of GPU clusters, and different types of parallelism in deep learning models and data.
Big Data Infrastructure: Introduction to Hadoop with MapReduce, Pig, and Hive
The main objective of this workshop is to give the audience hands on experience with several Hadoop technologies and jump start their hadoop journey. In this workshop, you will load data and submit queries using Hadoop! Before jumping in to the technology, the Founders of DataKitchen review Hadoop and some of its technologies (MapReduce, Hive, Pig, Impala and Spark), look at performance, and present a rubric for choosing which technology to use when.
Recommended
Numpy tutorial(final) 20160303
https://docs.scipy.org/doc/numpy-dev/user/quickstart.html
에 내용 중 Shape Manipulation과 Copies And Views 부분에 대한 발표자료.
Introduction to NumPy (PyData SV 2013)
NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
Understanding the Chief Data Officer
As the importance of having a data strategy in place is sinking in, many organizations have added a chief data officer (CDO) to their executive team to help create and implement that strategy. But every organization is doing this a little bit differently. This talk will describe how a variety of industries and organizations are using CDOs and will make recommendations for best practices.
Machine-In-The-Loop for Knowledge Discovery
I’ll present the new knowledge discovery tools we are building at Diffeo. Unlike traditional search engines that use keywords, Diffeo provides an in-browser knowledge base that accelerates information gathering about people, companies, chemical compounds, cyber events, or other real world entities. I’ll describe how Diffeo uses active learning to encourage long and deep user interactions in order to recommend new content for in-progress articles. As you write, the search results get better and more interesting, because the system can see more precisely which entity you mean and which you don’t (disambiguation) and also what you don’t know yet about the entity (discovery).
Finally in this presentation I’ll describe our experience organizing the Text REtrieval Conference (TREC) on Knowledge Base Acceleration (KBA) and Dynamic Domain (DD) which are pushing the state of the art in knowledge discovery on large streams. I’ll show you how to access the largest corpus of streaming text data ever released for public evaluations.
API Driven Development
An exposé on human-centered design, as related to data science and “medium data”. Examples of great API design will be showcased, as well as other end-user facing tools that can enable data scientists to share their observations with the world.
Mobile technology Usage by Humanitarian Programs: A Metadata Analysis
This document summarizes an analysis of metadata from the CommCare mobile data platform, which is used by hundreds of humanitarian programs worldwide. The analysis examined CommCare usage data to better understand how frontline workers and programs utilize the technology. Key findings included: 1) Frontline workers develop proficiency quickly in their first year but then level off; 2) Worker activity levels remain relatively stable month-to-month; 3) Worker productivity does not follow a normal distribution. The metadata analysis can help programs monitor performance and identify factors that lead to improvement over time.
Productionizing Deep Learning From the Ground Up
This document discusses productionizing deep learning from the ground up. It begins with an overview of deep learning and neural networks, explaining that deep learning performs pattern recognition on unlabeled and unstructured data using deep neural networks with three or more layers. It then discusses challenges like the computational intensity of deep learning models and the need for special hardware like GPUs. It also covers software engineering concerns in scaling deep learning to production, such as data pipelines, maintenance of GPU clusters, and different types of parallelism in deep learning models and data.
Big Data Infrastructure: Introduction to Hadoop with MapReduce, Pig, and Hive
The main objective of this workshop is to give the audience hands on experience with several Hadoop technologies and jump start their hadoop journey. In this workshop, you will load data and submit queries using Hadoop! Before jumping in to the technology, the Founders of DataKitchen review Hadoop and some of its technologies (MapReduce, Hive, Pig, Impala and Spark), look at performance, and present a rubric for choosing which technology to use when.
Think Breadth, Not Depth
We’ve all been told to “work smarter, not harder.” But what does working smarter really mean? In the world of finance and trading, working smarter means working differently. None of us can compete against computers stacked inches away from the stock exchange or blue chip companies with multi-million dollar marketing campaigns. The key to winning is to go where the big guys haven’t and the way to do that is through diverse datasets. In this talk, you will discover the theory and tools to discover new datasets from unexpected sources in order to gain an upper-hand in both finance and business. So whether you’re a quant that trades in his bedroom or a restaurateur looking to grow his business, you’ll learn how the diversity of data can be the sharpest knife if your set.
Data Science at Dow Jones: Monetizing Data, News and Information
In this presentation I will describe the way Data Science supports the business of information and news at Dow Jones. Specifically, I will describe how we are introducing innovative and advanced large-scale information mining and analytic approaches not only into Dow Jones’ products but also into our strategy and decision making processes.Our goal is to impact every aspect of Dow Jones: from the way journalism is produced in the newsroom, to the way we create and deliver institutional products, to the way we improve retention and acquisition of subscribers. While the task seems broad and daunting, we have already achieved various successes through the application of machine learning, data mining, advanced analytics and big data approaches.In this presentation I will describe how we have achieved this, including our tools, data, approaches and mechanisms as well as describe what our plans are going forward.
Spark, Python and Parquet
Have you been in the situation where you’re about to start a new project and ask yourself, what’s the right tool for the job here? I’ve been in that situation many times and thought it might be useful to share with you a recent project we did and why we selected Spark, Python, and Parquet. My plan is take you through a use case that involves loading, transforming, aggregating, and persisting the dataset. We’ll use an open dataset consisting of full fund holdings graciously provided by Morningstar. My goal in presenting this use case are to have the audience learn about how these technologies can be applied to a real world problem and to inspire members of the audience to start learning these technologies and applying them to their own projects.
Building a Predictive Analytics Solution with Azure ML
Create and operationalize a predictive model using Microsoft Azure Machine Learning.
– Perform the typical steps involved in building a predictive analytics solution such as data ingestion, data cleansing, data exploration, feature engineering, model selection and evaluation of model results
–learn how to use machine learning with big data scenarios using tools like Hadoop and SQL Server to process and work with such data.
Beyond Names
Finding and classifying the mentions of the things named in text, often called Named Entity Recognition or NER, is a fundamental task in many search and analysis applications. Mature, robust NER technology is available for many languages and domains, from people, places, and products, to diseases, genes, and molecules. However, for emerging tasks like knowledge-base construction, mentions alone are insufficient.
In this presentation we’ll explore techniques that go beyond names to:
link mentions to one another and to rich knowledge sources like Wikidata
discover and characterise the relationships between entities that are explicit in the text
And we’ll discuss some of the most important practical implications of these advancements for open data science.
How Woman are Conquering the S&P 500
According to Credit Suisse’s Gender 3000 report, at the end of 2013, women accounted for 12.9% of top management in 3000 companies across 40 countries. However, since 2009, companies with women as 25-50% of their management team
returned 22-29%. If companies with women in management outperform so dramatically, what would happen if you invested in women-led companies? Karen Rubin will explore this question and share her findings after running a 12 year investment simulation.
Domain Expertise and Unstructured Data
Data science allows us to turn a dark forest into a world of
perpetual twilight by giving us the tools to better understand the data that surrounds us. Unfortunately, in this world of twilight we still need a flashlight to get a clean crisp image of our immediate surroundings. We will talk about how to use deep domain expertise as that flashlight shedding light on our understanding of data. Our focus will be on using text analysis as a means to examine qualitative information in a structured, quantitative way. We will draw heavily from examples in complex central bank policy and financial regulation.
Kaggle The Home of Data Science
Kaggle is a platform for data science competitions that has hosted hundreds of challenges on topics ranging from flight routing to predicting molecular activity. It has a community of over 320,000 data scientists who submit over 100,000 machine learning models per month. Through competitions, Kaggle has helped companies like Allstate, Merck, and Mayo Clinic solve real-world problems by crowdsourcing data science solutions.
Open Source Tools & Data Science Competitions
This talk shares the presenter’s experience with open source tools in data science competitions. In the past several years Kaggle and other competitions have created a large online community of data scientists. In addition to competing with each other for fame and glory, members of this community also generously share knowledge, insights using forum and open source code. The open competition and sharing have resulted in rapid progress in the sophistication of the entire community. This presentation will briefly cover this journey from a competitor’s perspective, and share hands on tips on some open source tools proven popular and useful in recent competitions.
Machine Learning with scikit-learn
scikit-learn has emerged as one of the most popular open source machine learning toolkits, now widely used in academia and industry.
scikit-learn provides easy-to-use interfaces to perform advanced analysis and build powerful predictive models.
The tutorial will cover basic concepts of machine learning, such as supervised and unsupervised learning, cross validation, and model selection. We will see how to prepare data for machine learning, and go from applying a single algorithm to building a machine learning pipeline.
We will also cover how to build machine learning models on text data, and how to handle very large datasets.
Bridging the Gap Between Data and Insight using Open-Source Tools
Despite the proliferation of open-source tools for analysis (such as Python and R) and those used for visualization
(such as Javascript / D3), there often exist significant gaps between these areas, and those of us trying to navigate the complete arc from data to insight can encounter many obstacles along the way. Fortunately, in recent years there have been many efforts to fill these needs, and today distilling a meaningful visualization from raw data is faster and easier than ever before.
In this talk we will use will use examples in geospatial analysis and visualization to illustrate how to open-source tools like Python, geopandas, and TileMill work together. Using examples from the RunKeeper mobile app we will show how we currently use these tools to understand better our customers and their data, and to communicate
with our colleagues, external partners, and the data community at large.
Top 10 Signs of the Textpocalypse
Human-generated text may be the next frontier for big data analysis, but we humans are complicated beasts and the text we generate is messy and complicated in ways that can confound analysis. We’ll describe the top ten mistakes people make when they start doing text analysis, and hopefully save you from making a few of these mistakes yourself.
The Art of Data Science
The document is a presentation by Josh Wills from Cloudera on data science. It discusses defining data science and the roles of data scientists. It also covers challenges working with big data, including data modeling and addressing the impedance mismatch between operational and analytical systems. The presentation promotes open data science and sharing examples on GitHub to push beyond the limits of current tools.
Frontiers of Open Data Science Research
The document discusses open data science research topics presented at a conference, including opportunities and challenges with learning analytics and adaptive learning using open data. It describes how learning analytics can help achieve large improvements in student outcomes through targeted feedback and personalized learning paths. An open analytics architecture is proposed to integrate different data sources and applications using common data standards.
Feature Engineering
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
Agile Data
To rephrase an old saying: ‘It takes a village to raise an Analyst.’ Data Analysts and Scientists are working in teams delivering insight and analysis on an ongoing basis. So how do you get the team to support experimentation and insight delivery without ending up in an IT Engineer vs Analyst vs Data Governance war? We present 5 shocking steps to get these teams of people working together with practical, doable steps that can help you achieve data agility. The speaker has decades of hands on and executive management experience in data, analytics, and software development.
Using your powers for good: Data science in the social sector
Just like every major corporation today, nonprofits and governments have more data than ever before. And just like those corporations, they are eager to tap into the power of their data. But the social sector doesn’t have the same resources to attract talent. Jeff Hammerbacher, Chief Scientist at Cloudera, put it best: “The best minds of my generation are thinking about how to make people click ads. That sucks.” At DrivenData our goal is to make the world suck a little less by empowering impact organizations to get the most from their data.
Peter Bull, co-founder at DrivenData, will speak on the ways in which statistics, computer science, and machine learning can be applied to the challenges in the social sector. The talk will address both the big-picture context of the data for good movement, and an in-depth case study of the methods which won DrivenData’s recent machine learning competitions.
It’s an exciting time for people who love data: methods are improving, computational costs are decreasing, storage and transport are cheaper, and the talent pool is growing. It’s up to the data geeks to use these powers for good.
Machine Learning for Suits
You will learn the basic concepts of machine learning – such as Modeling, Model Selection, Loss or Profit, overfitting, and validation – in a non-mathematical way, so that you can ask for data analysis and interpret the results of a model in the context of making business decisions. The concepts behind machine learning are actually quite simple, so expect to take away not just words and acronyms, but rather, a deep understanding. We will work in the context of concrete examples from different domains, including finance and medicine.
What is probability? What is a model? Supervised vs unsupervised learning. Regression and Classification.
Models and Data: Bias, Variance, Noise, Overfitting, and how to solve Overfitting with Regularization and Validation
Risk and Cost, Data snooping, asymmetry in data, experiments.
How good is a model? Profit Curves, ROC curves, and the expected value formalism.
Recurrent Neural Networks for Text Analysis
Recurrent neural networks (RNNs) are well-suited for analyzing text data because they can model sequential and structural relationships in text. RNNs use gating mechanisms like LSTMs and GRUs to address the problem of exploding or vanishing gradients when training on long sequences. Modern RNNs trained with techniques like gradient clipping, improved initialization, and optimized training algorithms like Adam can learn meaningful representations from text even with millions of training examples. RNNs may outperform conventional bag-of-words models on large datasets but require significant computational resources. The author describes an RNN library called Passage and provides an example of sentiment analysis on movie reviews to demonstrate RNNs for text analysis.
Predictive Modeling Workshop
The workshop is an overview of creating predictive models using R. An example data set will be used to demonstrate a typical workflow: data splitting, pre-processing, model tuning and evaluation. Several R packages will be shown along with the caret package which provides a unified interface to a large number of R’s modeling functions and enables parallel processing. Participants should have a basic understanding of R data structures and basic language elements (i.e. functions, classes, etc).
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
More Related Content
More from odsc
Think Breadth, Not Depth
We’ve all been told to “work smarter, not harder.” But what does working smarter really mean? In the world of finance and trading, working smarter means working differently. None of us can compete against computers stacked inches away from the stock exchange or blue chip companies with multi-million dollar marketing campaigns. The key to winning is to go where the big guys haven’t and the way to do that is through diverse datasets. In this talk, you will discover the theory and tools to discover new datasets from unexpected sources in order to gain an upper-hand in both finance and business. So whether you’re a quant that trades in his bedroom or a restaurateur looking to grow his business, you’ll learn how the diversity of data can be the sharpest knife if your set.
Data Science at Dow Jones: Monetizing Data, News and Information
In this presentation I will describe the way Data Science supports the business of information and news at Dow Jones. Specifically, I will describe how we are introducing innovative and advanced large-scale information mining and analytic approaches not only into Dow Jones’ products but also into our strategy and decision making processes.Our goal is to impact every aspect of Dow Jones: from the way journalism is produced in the newsroom, to the way we create and deliver institutional products, to the way we improve retention and acquisition of subscribers. While the task seems broad and daunting, we have already achieved various successes through the application of machine learning, data mining, advanced analytics and big data approaches.In this presentation I will describe how we have achieved this, including our tools, data, approaches and mechanisms as well as describe what our plans are going forward.
Spark, Python and Parquet
Have you been in the situation where you’re about to start a new project and ask yourself, what’s the right tool for the job here? I’ve been in that situation many times and thought it might be useful to share with you a recent project we did and why we selected Spark, Python, and Parquet. My plan is take you through a use case that involves loading, transforming, aggregating, and persisting the dataset. We’ll use an open dataset consisting of full fund holdings graciously provided by Morningstar. My goal in presenting this use case are to have the audience learn about how these technologies can be applied to a real world problem and to inspire members of the audience to start learning these technologies and applying them to their own projects.
Building a Predictive Analytics Solution with Azure ML
Create and operationalize a predictive model using Microsoft Azure Machine Learning.
– Perform the typical steps involved in building a predictive analytics solution such as data ingestion, data cleansing, data exploration, feature engineering, model selection and evaluation of model results
–learn how to use machine learning with big data scenarios using tools like Hadoop and SQL Server to process and work with such data.
Beyond Names
Finding and classifying the mentions of the things named in text, often called Named Entity Recognition or NER, is a fundamental task in many search and analysis applications. Mature, robust NER technology is available for many languages and domains, from people, places, and products, to diseases, genes, and molecules. However, for emerging tasks like knowledge-base construction, mentions alone are insufficient.
In this presentation we’ll explore techniques that go beyond names to:
link mentions to one another and to rich knowledge sources like Wikidata
discover and characterise the relationships between entities that are explicit in the text
And we’ll discuss some of the most important practical implications of these advancements for open data science.
How Woman are Conquering the S&P 500
According to Credit Suisse’s Gender 3000 report, at the end of 2013, women accounted for 12.9% of top management in 3000 companies across 40 countries. However, since 2009, companies with women as 25-50% of their management team
returned 22-29%. If companies with women in management outperform so dramatically, what would happen if you invested in women-led companies? Karen Rubin will explore this question and share her findings after running a 12 year investment simulation.
Domain Expertise and Unstructured Data
Data science allows us to turn a dark forest into a world of
perpetual twilight by giving us the tools to better understand the data that surrounds us. Unfortunately, in this world of twilight we still need a flashlight to get a clean crisp image of our immediate surroundings. We will talk about how to use deep domain expertise as that flashlight shedding light on our understanding of data. Our focus will be on using text analysis as a means to examine qualitative information in a structured, quantitative way. We will draw heavily from examples in complex central bank policy and financial regulation.
Kaggle The Home of Data Science
Kaggle is a platform for data science competitions that has hosted hundreds of challenges on topics ranging from flight routing to predicting molecular activity. It has a community of over 320,000 data scientists who submit over 100,000 machine learning models per month. Through competitions, Kaggle has helped companies like Allstate, Merck, and Mayo Clinic solve real-world problems by crowdsourcing data science solutions.
Open Source Tools & Data Science Competitions
This talk shares the presenter’s experience with open source tools in data science competitions. In the past several years Kaggle and other competitions have created a large online community of data scientists. In addition to competing with each other for fame and glory, members of this community also generously share knowledge, insights using forum and open source code. The open competition and sharing have resulted in rapid progress in the sophistication of the entire community. This presentation will briefly cover this journey from a competitor’s perspective, and share hands on tips on some open source tools proven popular and useful in recent competitions.
Machine Learning with scikit-learn
scikit-learn has emerged as one of the most popular open source machine learning toolkits, now widely used in academia and industry.
scikit-learn provides easy-to-use interfaces to perform advanced analysis and build powerful predictive models.
The tutorial will cover basic concepts of machine learning, such as supervised and unsupervised learning, cross validation, and model selection. We will see how to prepare data for machine learning, and go from applying a single algorithm to building a machine learning pipeline.
We will also cover how to build machine learning models on text data, and how to handle very large datasets.
Bridging the Gap Between Data and Insight using Open-Source Tools
Despite the proliferation of open-source tools for analysis (such as Python and R) and those used for visualization
(such as Javascript / D3), there often exist significant gaps between these areas, and those of us trying to navigate the complete arc from data to insight can encounter many obstacles along the way. Fortunately, in recent years there have been many efforts to fill these needs, and today distilling a meaningful visualization from raw data is faster and easier than ever before.
In this talk we will use will use examples in geospatial analysis and visualization to illustrate how to open-source tools like Python, geopandas, and TileMill work together. Using examples from the RunKeeper mobile app we will show how we currently use these tools to understand better our customers and their data, and to communicate
with our colleagues, external partners, and the data community at large.
Top 10 Signs of the Textpocalypse
Human-generated text may be the next frontier for big data analysis, but we humans are complicated beasts and the text we generate is messy and complicated in ways that can confound analysis. We’ll describe the top ten mistakes people make when they start doing text analysis, and hopefully save you from making a few of these mistakes yourself.
The Art of Data Science
The document is a presentation by Josh Wills from Cloudera on data science. It discusses defining data science and the roles of data scientists. It also covers challenges working with big data, including data modeling and addressing the impedance mismatch between operational and analytical systems. The presentation promotes open data science and sharing examples on GitHub to push beyond the limits of current tools.
Frontiers of Open Data Science Research
The document discusses open data science research topics presented at a conference, including opportunities and challenges with learning analytics and adaptive learning using open data. It describes how learning analytics can help achieve large improvements in student outcomes through targeted feedback and personalized learning paths. An open analytics architecture is proposed to integrate different data sources and applications using common data standards.
Feature Engineering
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
Agile Data
To rephrase an old saying: ‘It takes a village to raise an Analyst.’ Data Analysts and Scientists are working in teams delivering insight and analysis on an ongoing basis. So how do you get the team to support experimentation and insight delivery without ending up in an IT Engineer vs Analyst vs Data Governance war? We present 5 shocking steps to get these teams of people working together with practical, doable steps that can help you achieve data agility. The speaker has decades of hands on and executive management experience in data, analytics, and software development.
Using your powers for good: Data science in the social sector
Just like every major corporation today, nonprofits and governments have more data than ever before. And just like those corporations, they are eager to tap into the power of their data. But the social sector doesn’t have the same resources to attract talent. Jeff Hammerbacher, Chief Scientist at Cloudera, put it best: “The best minds of my generation are thinking about how to make people click ads. That sucks.” At DrivenData our goal is to make the world suck a little less by empowering impact organizations to get the most from their data.
Peter Bull, co-founder at DrivenData, will speak on the ways in which statistics, computer science, and machine learning can be applied to the challenges in the social sector. The talk will address both the big-picture context of the data for good movement, and an in-depth case study of the methods which won DrivenData’s recent machine learning competitions.
It’s an exciting time for people who love data: methods are improving, computational costs are decreasing, storage and transport are cheaper, and the talent pool is growing. It’s up to the data geeks to use these powers for good.
Machine Learning for Suits
You will learn the basic concepts of machine learning – such as Modeling, Model Selection, Loss or Profit, overfitting, and validation – in a non-mathematical way, so that you can ask for data analysis and interpret the results of a model in the context of making business decisions. The concepts behind machine learning are actually quite simple, so expect to take away not just words and acronyms, but rather, a deep understanding. We will work in the context of concrete examples from different domains, including finance and medicine.
What is probability? What is a model? Supervised vs unsupervised learning. Regression and Classification.
Models and Data: Bias, Variance, Noise, Overfitting, and how to solve Overfitting with Regularization and Validation
Risk and Cost, Data snooping, asymmetry in data, experiments.
How good is a model? Profit Curves, ROC curves, and the expected value formalism.
Recurrent Neural Networks for Text Analysis
Recurrent neural networks (RNNs) are well-suited for analyzing text data because they can model sequential and structural relationships in text. RNNs use gating mechanisms like LSTMs and GRUs to address the problem of exploding or vanishing gradients when training on long sequences. Modern RNNs trained with techniques like gradient clipping, improved initialization, and optimized training algorithms like Adam can learn meaningful representations from text even with millions of training examples. RNNs may outperform conventional bag-of-words models on large datasets but require significant computational resources. The author describes an RNN library called Passage and provides an example of sentiment analysis on movie reviews to demonstrate RNNs for text analysis.
Predictive Modeling Workshop
The workshop is an overview of creating predictive models using R. An example data set will be used to demonstrate a typical workflow: data splitting, pre-processing, model tuning and evaluation. Several R packages will be shown along with the caret package which provides a unified interface to a large number of R’s modeling functions and enables parallel processing. Participants should have a basic understanding of R data structures and basic language elements (i.e. functions, classes, etc).
More from odsc (20)
Data Science at Dow Jones: Monetizing Data, News and Information
Data Science at Dow Jones: Monetizing Data, News and Information
Building a Predictive Analytics Solution with Azure ML
Building a Predictive Analytics Solution with Azure ML
Bridging the Gap Between Data and Insight using Open-Source Tools
Bridging the Gap Between Data and Insight using Open-Source Tools
Using your powers for good: Data science in the social sector
Using your powers for good: Data science in the social sector
Recently uploaded
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Digital Marketing Trends in 2024 | Guide for Staying Ahead
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Recently uploaded (20)
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Monary
- 1. MONARY: SCIENTIFIC ANALYSIS WITH MONGODB AND NUMPY Anna Herlihy O P E N D A T A S C I E N C E C O N F E R E N C E_ BOSTON 2015 @opendat asci