This document is a certificate for Anshul Agarwal for completing the Python Data Science Toolbox (Part 1) course. It provides his name, a course identification number, and specifies that the course completed was Python Data Science Toolbox (Part 1).
Going Smart and Deep on Materials at ALCFIan Foster
As we acquire large quantities of science data from experiment and simulation, it becomes possible to apply machine learning (ML) to those data to build predictive models and to guide future simulations and experiments. Leadership Computing Facilities need to make it easy to assemble such data collections and to develop, deploy, and run associated ML models.
We describe and demonstrate here how we are realizing such capabilities at the Argonne Leadership Computing Facility. In our demonstration, we use large quantities of time-dependent density functional theory (TDDFT) data on proton stopping power in various materials maintained in the Materials Data Facility (MDF) to build machine learning models, ranging from simple linear models to complex artificial neural networks, that are then employed to manage computations, improving their accuracy and reducing their cost. We highlight the use of new services being prototyped at Argonne to organize and assemble large data collections (MDF in this case), associate ML models with data collections, discover available data and models, work with these data and models in an interactive Jupyter environment, and launch new computations on ALCF resources.
Going Smart and Deep on Materials at ALCFIan Foster
As we acquire large quantities of science data from experiment and simulation, it becomes possible to apply machine learning (ML) to those data to build predictive models and to guide future simulations and experiments. Leadership Computing Facilities need to make it easy to assemble such data collections and to develop, deploy, and run associated ML models.
We describe and demonstrate here how we are realizing such capabilities at the Argonne Leadership Computing Facility. In our demonstration, we use large quantities of time-dependent density functional theory (TDDFT) data on proton stopping power in various materials maintained in the Materials Data Facility (MDF) to build machine learning models, ranging from simple linear models to complex artificial neural networks, that are then employed to manage computations, improving their accuracy and reducing their cost. We highlight the use of new services being prototyped at Argonne to organize and assemble large data collections (MDF in this case), associate ML models with data collections, discover available data and models, work with these data and models in an interactive Jupyter environment, and launch new computations on ALCF resources.
Rajat Monga, Engineering Director, TensorFlow, Google at MLconf 2016MLconf
Machine Learning with TensorFlow: TensorFlow has enabled cutting-edge machine learning research at the top AI labs in the world. At the same time it has made the technology accessible to a large audience leading to some amazing uses. TensorFlow is used for classification, recommendation, text parsing, sentiment analysis and more. This talk will go over the design that makes it fast, flexible, and easy to use, and describe how we continue to make it better.
Daniel Shank, Data Scientist, Talla at MLconf SF 2016MLconf
Neural Turing Machines: Perils and Promise: Daniel Shank is a Senior Data Scientist at Talla, a company developing a platform for intelligent information discovery and delivery. His focus is on developing machine learning techniques to handle various business automation tasks, such as scheduling, polls, expert identification, as well as doing work on NLP. Before joining Talla as the company’s first employee in 2015, Daniel worked with TechStars Boston and did consulting work for ThriveHive, a small business focused marketing company in Boston. He studied economics at the University of Chicago.
The Face of Nanomaterials: Insightful Classification Using Deep Learning - An...PyData
Artificial intelligence is emerging as a new paradigm in materials science. This talk describes how physical intuition and (insightful) machine learning can solve the complicated task of structure recognition in materials at the nanoscale.
Dr. Erin LeDell, Machine Learning Scientist, H2O.ai at MLconf SEA - 5/20/16MLconf
Multi-algorithm Ensemble Learning at Scale: Software, Hardware and Algorithmic Approaches: Multi-algorithm ensemble machine learning methods are often used when the true prediction function is not easily approximated by a single algorithm. The Super Learner algorithm, also known as stacking, combines multiple, typically diverse, base learning algorithms into a single, powerful prediction function through a secondary learning process called metalearning. Although ensemble methods offer superior performance over their singleton counterparts, there is an implicit computational cost to ensembles, as it requires training and cross-validating multiple base learning algorithms.
We will demonstrate a variety of software- and hardware-based approaches that lead to more scalable ensemble learning software, including a highly scalable implementation of stacking called “H2O Ensemble”, built on top of the open source, distributed machine learning platform, H2O. H2O Ensemble scales across multi-node clusters and allows the user to create ensembles of deep neural networks, Gradient Boosting Machines, Random Forest, and others. As for algorithm-based approaches, we will present two algorithmic modifications to the original stacking algorithm that further reduce computation time — Subsemble algorithm and the Online Super Learner algorithm. This talk will also include benchmarks of the implementations of these new stacking variants.
(Paper Review)A versatile learning based 3D temporal tracker - scalable, robu...MYEONGGYU LEE
review date: 2018/04/09 (by Meyong-Gyu.LEE @Soongsil Univ.)
Eng review of 'A versatile learning based 3D temporal tracker - scalable, robust, online'(ICCV 2015)
Brief presentation about keras framework. The propose of this presentation is to give some ideas about how it works and its main functionalities. In addition, is also shown a function to create different models from a config file.
Deep Learning with Apache Spark: an IntroductionEmanuele Bezzi
Presented at Scala Italy 2016 with Andrea Bessi
Neural networks and deep learning have seen a spectacular advance during the last few years and represent now the state of the art in tasks such as image recognition, automated translations and natural language processing.
Unfortunately, most of the high performance deep learning implementations are single-node only, not being therefore particularly scalable.
During this talk, we will demonstrate how Apache Spark, the fast and general engine for large-scale data processing, can be used to train artificial neural networks, thus allowing to achieve high performance and parallel computing at the same time.
Presentation given at the Stockholm R useR Group (SRUG) meetup on Dec 6, 2016. Contains a general overview of deep learning, material on using Tensorflow in R etc.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Rajat Monga, Engineering Director, TensorFlow, Google at MLconf 2016MLconf
Machine Learning with TensorFlow: TensorFlow has enabled cutting-edge machine learning research at the top AI labs in the world. At the same time it has made the technology accessible to a large audience leading to some amazing uses. TensorFlow is used for classification, recommendation, text parsing, sentiment analysis and more. This talk will go over the design that makes it fast, flexible, and easy to use, and describe how we continue to make it better.
Daniel Shank, Data Scientist, Talla at MLconf SF 2016MLconf
Neural Turing Machines: Perils and Promise: Daniel Shank is a Senior Data Scientist at Talla, a company developing a platform for intelligent information discovery and delivery. His focus is on developing machine learning techniques to handle various business automation tasks, such as scheduling, polls, expert identification, as well as doing work on NLP. Before joining Talla as the company’s first employee in 2015, Daniel worked with TechStars Boston and did consulting work for ThriveHive, a small business focused marketing company in Boston. He studied economics at the University of Chicago.
The Face of Nanomaterials: Insightful Classification Using Deep Learning - An...PyData
Artificial intelligence is emerging as a new paradigm in materials science. This talk describes how physical intuition and (insightful) machine learning can solve the complicated task of structure recognition in materials at the nanoscale.
Dr. Erin LeDell, Machine Learning Scientist, H2O.ai at MLconf SEA - 5/20/16MLconf
Multi-algorithm Ensemble Learning at Scale: Software, Hardware and Algorithmic Approaches: Multi-algorithm ensemble machine learning methods are often used when the true prediction function is not easily approximated by a single algorithm. The Super Learner algorithm, also known as stacking, combines multiple, typically diverse, base learning algorithms into a single, powerful prediction function through a secondary learning process called metalearning. Although ensemble methods offer superior performance over their singleton counterparts, there is an implicit computational cost to ensembles, as it requires training and cross-validating multiple base learning algorithms.
We will demonstrate a variety of software- and hardware-based approaches that lead to more scalable ensemble learning software, including a highly scalable implementation of stacking called “H2O Ensemble”, built on top of the open source, distributed machine learning platform, H2O. H2O Ensemble scales across multi-node clusters and allows the user to create ensembles of deep neural networks, Gradient Boosting Machines, Random Forest, and others. As for algorithm-based approaches, we will present two algorithmic modifications to the original stacking algorithm that further reduce computation time — Subsemble algorithm and the Online Super Learner algorithm. This talk will also include benchmarks of the implementations of these new stacking variants.
(Paper Review)A versatile learning based 3D temporal tracker - scalable, robu...MYEONGGYU LEE
review date: 2018/04/09 (by Meyong-Gyu.LEE @Soongsil Univ.)
Eng review of 'A versatile learning based 3D temporal tracker - scalable, robust, online'(ICCV 2015)
Brief presentation about keras framework. The propose of this presentation is to give some ideas about how it works and its main functionalities. In addition, is also shown a function to create different models from a config file.
Deep Learning with Apache Spark: an IntroductionEmanuele Bezzi
Presented at Scala Italy 2016 with Andrea Bessi
Neural networks and deep learning have seen a spectacular advance during the last few years and represent now the state of the art in tasks such as image recognition, automated translations and natural language processing.
Unfortunately, most of the high performance deep learning implementations are single-node only, not being therefore particularly scalable.
During this talk, we will demonstrate how Apache Spark, the fast and general engine for large-scale data processing, can be used to train artificial neural networks, thus allowing to achieve high performance and parallel computing at the same time.
Presentation given at the Stockholm R useR Group (SRUG) meetup on Dec 6, 2016. Contains a general overview of deep learning, material on using Tensorflow in R etc.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
