In this Graph Gurus episode, we:
-Review the basics of deep learning algorithm,
-Introduce a classical classification problem: recognize a hand-written digit,
-Present a graph solution to build and train an artificial neural network for digit recognition using TigerGraph GraphStudio and GSQL,
-Review a test dataset and GSQL queries for the solution.
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Full Webinar: https://info.tigergraph.com/graph-gurus-28
In this webinar, we will use the recommendation system problem, which can be efficiently solved as a graph problem, to demonstrate the in-database training capability of TigerGraph, a native graph database. A hybrid (memory-based + model-based) recommendation system will be implemented in TigerGraph. Specifically, the latent factor model used for recommendation will be trained within the database.
In this Graph Gurus episode, we will:
-Review multiple widely-used recommendation methods
-Introduce the concept of in-database machine learning
-Present an in-database machine learning solution for a real time recommendation system
Graph Gurus 23: Best Practices To Model Your Data Using A Graph DatabaseTigerGraph
Watch the webinar at info.tigergraph.com/graph-gurus-23
Learn:
-What can be vertices and edges
-How to choose an edge type (undirected, directed, reversed)
-How to decide between attributes or vertices
-How to model temporal data
-How to model multiple events and/or /transactions between two entities
-How to use derived edges to speed up queries
Using Graph Algorithms for Advanced Analytics - Part 5 ClassificationTigerGraph
What atmospheric data will help you predict if it's going to rain, snow, or be windy? What position should that new athlete play? How well can you guess a person's demographic background, based on their chat activity? These are all classification problems -- trying to pick the right category or label for an entity, based on observable features. They can also be solved with machine learning.
Full Webinar: https://info.tigergraph.com/graph-gurus-21
In this Graph Gurus episode, we:
Explain the architecture and technical implementation for a TigerGraph + Spark graph-enhanced Machine Learning pipeline
Use TigerGraph both before training to extract (graph and non-graph) features and after training to apply the model on streaming data
Use Spark to train and tune machine learning models at scale
Present a solution in production at China Mobile that detects and prevents phone-based scams using machine learning with TigerGraph
Demo the data flow between Spark and TigerGraph via TigerGraph’s JDBC driver
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Full Webinar: https://info.tigergraph.com/graph-gurus-28
In this webinar, we will use the recommendation system problem, which can be efficiently solved as a graph problem, to demonstrate the in-database training capability of TigerGraph, a native graph database. A hybrid (memory-based + model-based) recommendation system will be implemented in TigerGraph. Specifically, the latent factor model used for recommendation will be trained within the database.
In this Graph Gurus episode, we will:
-Review multiple widely-used recommendation methods
-Introduce the concept of in-database machine learning
-Present an in-database machine learning solution for a real time recommendation system
Graph Gurus 23: Best Practices To Model Your Data Using A Graph DatabaseTigerGraph
Watch the webinar at info.tigergraph.com/graph-gurus-23
Learn:
-What can be vertices and edges
-How to choose an edge type (undirected, directed, reversed)
-How to decide between attributes or vertices
-How to model temporal data
-How to model multiple events and/or /transactions between two entities
-How to use derived edges to speed up queries
Using Graph Algorithms for Advanced Analytics - Part 5 ClassificationTigerGraph
What atmospheric data will help you predict if it's going to rain, snow, or be windy? What position should that new athlete play? How well can you guess a person's demographic background, based on their chat activity? These are all classification problems -- trying to pick the right category or label for an entity, based on observable features. They can also be solved with machine learning.
Full Webinar: https://info.tigergraph.com/graph-gurus-21
In this Graph Gurus episode, we:
Explain the architecture and technical implementation for a TigerGraph + Spark graph-enhanced Machine Learning pipeline
Use TigerGraph both before training to extract (graph and non-graph) features and after training to apply the model on streaming data
Use Spark to train and tune machine learning models at scale
Present a solution in production at China Mobile that detects and prevents phone-based scams using machine learning with TigerGraph
Demo the data flow between Spark and TigerGraph via TigerGraph’s JDBC driver
Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-26
Have you ever wondered how routing apps like Google Maps find the best route from one place to another? Finding that route is solved by the Shortest Path graph algorithm. Today, graph algorithms are moving from the classroom to a host of important and valuable operational and analytical applications. This webinar will give you an overview of graph algorithms, how to use them, and the categories of problems they can solve, and then take a closer look at path algorithms. This webinar is the first part in a five-part series, each part examining a different type of problem to be solved.
Graph Databases and Machine Learning | November 2018TigerGraph
Graph Database and Machine Learning: Finding a Happy Marriage. Graph Databases and Machine Learning
both represent powerful tools for getting more value from data, learn how they can form a harmonious marriage to up-level machine learning.
Graph Gurus Episode 17: Seven Key Data Science Capabilities Powered by a Nati...TigerGraph
This webinar will demonstrate seven key data science capabilities using TigerGraph’s intuitive GUI, GraphStudio and GSQL queries. In this episode, we:
-Share the capabilities and tie those to specific use cases across healthcare, pharmaceutical, financial services, Telecom, Internet and government industries.
-Walk you through a sample dataset, GraphStudio UI flow, and GSQL queries demonstrating the capabilities.
-Cover client case studies for Amgen, Intuit, China Mobile, Santa Clara County, and other enterprise customers
Graph Gurus Episode 27: Using Graph Algorithms for Advanced Analytics Part 2TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-27
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms. Join us for Part 2 of our five-part webinar series on using graph algorithms for advanced analytics.
By attending this webinar you will:
- Hear about use cases for centrality graph algorithms
- Learn how to select the right algorithm for your use case
- Be able to run and tailor GSQL graph algorithms
Graph Gurus Episode 31: GSQL Writing Best Practices Part 1TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-31
By watching this webinar you will:
-Become more confident in GSQL query writing
-Know more about GSQL mechanism and accumulators
-Be able to write medium level difficulty GSQL queries.
During the presentation we cover the the following:
Review GSQL basics
Explain how to design a graph traversal plan
-Describe how to choose the best accumulator
-Explore how accumulators are populated
-Show how to produce results.
Using Graph Algorithms For Advanced Analytics - Part 4 Similarity 30 graph al...TigerGraph
Graph-based investigation often enables us to identify individuals who are of special interest, and their uniqueness is due in part to their pattern of interactions. For example:
-A patient whose carepath journey leverages best-practices gained from using pattern matching algorithms that find similar issues among the data of 50 million patients
-An individual who builds a successful portfolio by implementing actions recommended by similarity algorithms that find equivalent actions by successful investors
-A participant in a criminal ring whose attempts at swindling are blocked by matching them to patterns of known fraudulent activity
Once you have identified such a pattern and a key individual, you want to search your data for similar occurrences. Similarity algorithms are the answer.
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Misha Bilenko, Principal Researcher, Microsoft at MLconf SEA - 5/01/15MLconf
Many Shades of Scale: Big Learning Beyond Big Data: In the machine learning research community, much of the attention devoted to ‘big data’ in recent years has been manifested as development of new algorithms and systems for distributed training on many examples. This focus has led to significant advances in the field, from basic but operational implementations on popular platforms to highly sophisticated prototypes in the literature. In the meantime, other aspects of scaling up learning have received relatively little attention, although they are often more pressing in practice. The talk will survey these less-studied facets of big learning: scaling to an extremely large number of features, to many components in predictive pipelines, and to multiple data scientists collaborating on shared experiments.
Training at AI Frontiers 2018 - LaiOffer Data Session: How Spark Speedup AI AI Frontiers
Topic: How to use big data to enhance AI
Outline:
1. Spark ETL
Spark SQL
Spark Streaming
2. Spark ML
Spark ML pipeline
Distributed model tuning
Spark ML model and data lineage management
3. Spark XGboost
XGboost introduction
XGboost with Spark
XGboost with GPU
4. Spark Deep Learning pipeline
Transfer learning
Build Spark ML pipeline with TensorFlow
Model selection on distributed TF model
Jay Yagnik at AI Frontiers : A History Lesson on AIAI Frontiers
We have reached a remarkable point in history with the evolution of AI, from applying this technology to incredible use cases in healthcare, to addressing the world's biggest humanitarian and environmental issues. Our ability to learn task-specific functions for vision, language, sequence and control tasks is getting better at a rapid pace. This talk will survey some of the current advances in AI, compare AI to other fields that have historically developed over time, and calibrate where we are in the relative advancement timeline. We will also speculate about the next inflection points and capabilities that AI can offer down the road, and look at how those might intersect with other emergent fields, e.g. Quantum computing.
Predictive Model and Record Description with Segmented Sensitivity Analysis (...Greg Makowski
Describing a predictive data mining model can provide a competitive advantage for solving business problems with a model. The SSA approach can also provide reasons for the forecast for each record. This can help drive investigations into fields and interactions during a data mining project, as well as identifying "data drift" between the original training data, and the current scoring data. I am working on open source version of SSA, first in R.
This deck quickly walks through fundamentals of Deep Learning and describes how symbolic engine of MXNet implements such networks. It then introduces gluon and provides code examples. The last section of the presentation introduces latest developments in gluon family of tools to include GluonNLP, an NLP toolkit with SOTA implementation of NLP algorithms, GluonCV, a Computer Vision toolkit with SOTA implementation of Vision algorithms, and MXNet backend for Keras.
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017MLconf
Corinna Cortes is a Danish computer scientist known for her contributions to machine learning. She is currently the Head of Google Research, New York. Cortes is a recipient of the Paris Kanellakis Theory and Practice Award for her work on theoretical foundations of support vector machines.
Cortes received her M.S. degree in physics from Copenhagen University in 1989. In the same year she joined AT&T Bell Labs as a researcher and remained there for about ten years. She received her Ph.D. in computer science from the University of Rochester in 1993. Cortes currently serves as the Head of Google Research, New York. She is an Editorial Board member of the journal Machine Learning.
Cortes’ research covers a wide range of topics in machine learning, including support vector machines and data mining. In 2008, she jointly with Vladimir Vapnik received the Paris Kanellakis Theory and Practice Award for the development of a highly effective algorithm for supervised learning known as support vector machines (SVM). Today, SVM is one of the most frequently used algorithms in machine learning, which is used in many practical applications, including medical diagnosis and weather forecasting.
Abstract Summary:
Harnessing Neural Networks:
Deep learning has demonstrated impressive performance gain in many machine learning applications. However, unveiling and realizing these performance gains is not always straightforward. Discovering the right network architecture is critical for accuracy and often requires a human in the loop. Some network architectures occasionally produce spurious outputs, and the outputs have to be restricted to meet the needs of an application. Finally, realizing the performance gain in a production system can be difficult because of extensive inference times.
In this talk we discuss methods for making neural networks efficient in production systems. We also discuss an efficient method for automatically learning the network architecture, called AdaNet. We provide theoretical arguments for the algorithm and present experimental evidence for its effectiveness.
This presentation describes two major papers in multi-variate time-series using deep neural networks. The first paper, DeepAR was developed at Amazon to deal with forecasting of millions of items where the same model can be applied to millions of products. DeepAR is implemented as a built-in algorithm of Amazon SageMaker. Code example is provided.
The second paper, Long- and Short-Term Temporal Patterns with Deep Neural Networks is developed at CMU and introduces a novel way to detect both short term and long term seasonality in data through introduction of skip-rnn.
A Gluon implementation of the paper is provided in the presentation.
Covers basics Artificial neural networks and motivation for deep learning and explains certain deep learning networks, including deep belief networks and autoencoders. It also details challenges of implementing a deep learning network at scale and explains how we have implemented a distributed deep learning network over Spark.
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.
GPT and Graph Data Science to power your Knowledge GraphNeo4j
In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
Graph Gurus Episode 32: Using Graph Algorithms for Advanced Analytics Part 5TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-32
By watching this webinar you will:
-See how similarity algorithms are used to calculate "distances" between entities.
-Learn what data scientists mean when they say Labels and Training.
-Understand the full workflow for the k-Nearest Neighbor machine learning technique, from computing distances to predicting labels for a given value of k, to learning the best value of k.
Join us for this 30-minute webinar to hear from Zach Blumenfeld, Neo4j’s Data Science Specialist, to learn the basics of Graph Neural Networks (GNNs) and how they can help you to improve predictions in your data.
Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-26
Have you ever wondered how routing apps like Google Maps find the best route from one place to another? Finding that route is solved by the Shortest Path graph algorithm. Today, graph algorithms are moving from the classroom to a host of important and valuable operational and analytical applications. This webinar will give you an overview of graph algorithms, how to use them, and the categories of problems they can solve, and then take a closer look at path algorithms. This webinar is the first part in a five-part series, each part examining a different type of problem to be solved.
Graph Databases and Machine Learning | November 2018TigerGraph
Graph Database and Machine Learning: Finding a Happy Marriage. Graph Databases and Machine Learning
both represent powerful tools for getting more value from data, learn how they can form a harmonious marriage to up-level machine learning.
Graph Gurus Episode 17: Seven Key Data Science Capabilities Powered by a Nati...TigerGraph
This webinar will demonstrate seven key data science capabilities using TigerGraph’s intuitive GUI, GraphStudio and GSQL queries. In this episode, we:
-Share the capabilities and tie those to specific use cases across healthcare, pharmaceutical, financial services, Telecom, Internet and government industries.
-Walk you through a sample dataset, GraphStudio UI flow, and GSQL queries demonstrating the capabilities.
-Cover client case studies for Amgen, Intuit, China Mobile, Santa Clara County, and other enterprise customers
Graph Gurus Episode 27: Using Graph Algorithms for Advanced Analytics Part 2TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-27
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms. Join us for Part 2 of our five-part webinar series on using graph algorithms for advanced analytics.
By attending this webinar you will:
- Hear about use cases for centrality graph algorithms
- Learn how to select the right algorithm for your use case
- Be able to run and tailor GSQL graph algorithms
Graph Gurus Episode 31: GSQL Writing Best Practices Part 1TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-31
By watching this webinar you will:
-Become more confident in GSQL query writing
-Know more about GSQL mechanism and accumulators
-Be able to write medium level difficulty GSQL queries.
During the presentation we cover the the following:
Review GSQL basics
Explain how to design a graph traversal plan
-Describe how to choose the best accumulator
-Explore how accumulators are populated
-Show how to produce results.
Using Graph Algorithms For Advanced Analytics - Part 4 Similarity 30 graph al...TigerGraph
Graph-based investigation often enables us to identify individuals who are of special interest, and their uniqueness is due in part to their pattern of interactions. For example:
-A patient whose carepath journey leverages best-practices gained from using pattern matching algorithms that find similar issues among the data of 50 million patients
-An individual who builds a successful portfolio by implementing actions recommended by similarity algorithms that find equivalent actions by successful investors
-A participant in a criminal ring whose attempts at swindling are blocked by matching them to patterns of known fraudulent activity
Once you have identified such a pattern and a key individual, you want to search your data for similar occurrences. Similarity algorithms are the answer.
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Misha Bilenko, Principal Researcher, Microsoft at MLconf SEA - 5/01/15MLconf
Many Shades of Scale: Big Learning Beyond Big Data: In the machine learning research community, much of the attention devoted to ‘big data’ in recent years has been manifested as development of new algorithms and systems for distributed training on many examples. This focus has led to significant advances in the field, from basic but operational implementations on popular platforms to highly sophisticated prototypes in the literature. In the meantime, other aspects of scaling up learning have received relatively little attention, although they are often more pressing in practice. The talk will survey these less-studied facets of big learning: scaling to an extremely large number of features, to many components in predictive pipelines, and to multiple data scientists collaborating on shared experiments.
Training at AI Frontiers 2018 - LaiOffer Data Session: How Spark Speedup AI AI Frontiers
Topic: How to use big data to enhance AI
Outline:
1. Spark ETL
Spark SQL
Spark Streaming
2. Spark ML
Spark ML pipeline
Distributed model tuning
Spark ML model and data lineage management
3. Spark XGboost
XGboost introduction
XGboost with Spark
XGboost with GPU
4. Spark Deep Learning pipeline
Transfer learning
Build Spark ML pipeline with TensorFlow
Model selection on distributed TF model
Jay Yagnik at AI Frontiers : A History Lesson on AIAI Frontiers
We have reached a remarkable point in history with the evolution of AI, from applying this technology to incredible use cases in healthcare, to addressing the world's biggest humanitarian and environmental issues. Our ability to learn task-specific functions for vision, language, sequence and control tasks is getting better at a rapid pace. This talk will survey some of the current advances in AI, compare AI to other fields that have historically developed over time, and calibrate where we are in the relative advancement timeline. We will also speculate about the next inflection points and capabilities that AI can offer down the road, and look at how those might intersect with other emergent fields, e.g. Quantum computing.
Predictive Model and Record Description with Segmented Sensitivity Analysis (...Greg Makowski
Describing a predictive data mining model can provide a competitive advantage for solving business problems with a model. The SSA approach can also provide reasons for the forecast for each record. This can help drive investigations into fields and interactions during a data mining project, as well as identifying "data drift" between the original training data, and the current scoring data. I am working on open source version of SSA, first in R.
This deck quickly walks through fundamentals of Deep Learning and describes how symbolic engine of MXNet implements such networks. It then introduces gluon and provides code examples. The last section of the presentation introduces latest developments in gluon family of tools to include GluonNLP, an NLP toolkit with SOTA implementation of NLP algorithms, GluonCV, a Computer Vision toolkit with SOTA implementation of Vision algorithms, and MXNet backend for Keras.
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017MLconf
Corinna Cortes is a Danish computer scientist known for her contributions to machine learning. She is currently the Head of Google Research, New York. Cortes is a recipient of the Paris Kanellakis Theory and Practice Award for her work on theoretical foundations of support vector machines.
Cortes received her M.S. degree in physics from Copenhagen University in 1989. In the same year she joined AT&T Bell Labs as a researcher and remained there for about ten years. She received her Ph.D. in computer science from the University of Rochester in 1993. Cortes currently serves as the Head of Google Research, New York. She is an Editorial Board member of the journal Machine Learning.
Cortes’ research covers a wide range of topics in machine learning, including support vector machines and data mining. In 2008, she jointly with Vladimir Vapnik received the Paris Kanellakis Theory and Practice Award for the development of a highly effective algorithm for supervised learning known as support vector machines (SVM). Today, SVM is one of the most frequently used algorithms in machine learning, which is used in many practical applications, including medical diagnosis and weather forecasting.
Abstract Summary:
Harnessing Neural Networks:
Deep learning has demonstrated impressive performance gain in many machine learning applications. However, unveiling and realizing these performance gains is not always straightforward. Discovering the right network architecture is critical for accuracy and often requires a human in the loop. Some network architectures occasionally produce spurious outputs, and the outputs have to be restricted to meet the needs of an application. Finally, realizing the performance gain in a production system can be difficult because of extensive inference times.
In this talk we discuss methods for making neural networks efficient in production systems. We also discuss an efficient method for automatically learning the network architecture, called AdaNet. We provide theoretical arguments for the algorithm and present experimental evidence for its effectiveness.
This presentation describes two major papers in multi-variate time-series using deep neural networks. The first paper, DeepAR was developed at Amazon to deal with forecasting of millions of items where the same model can be applied to millions of products. DeepAR is implemented as a built-in algorithm of Amazon SageMaker. Code example is provided.
The second paper, Long- and Short-Term Temporal Patterns with Deep Neural Networks is developed at CMU and introduces a novel way to detect both short term and long term seasonality in data through introduction of skip-rnn.
A Gluon implementation of the paper is provided in the presentation.
Covers basics Artificial neural networks and motivation for deep learning and explains certain deep learning networks, including deep belief networks and autoencoders. It also details challenges of implementing a deep learning network at scale and explains how we have implemented a distributed deep learning network over Spark.
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.
GPT and Graph Data Science to power your Knowledge GraphNeo4j
In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
Graph Gurus Episode 32: Using Graph Algorithms for Advanced Analytics Part 5TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-32
By watching this webinar you will:
-See how similarity algorithms are used to calculate "distances" between entities.
-Learn what data scientists mean when they say Labels and Training.
-Understand the full workflow for the k-Nearest Neighbor machine learning technique, from computing distances to predicting labels for a given value of k, to learning the best value of k.
Join us for this 30-minute webinar to hear from Zach Blumenfeld, Neo4j’s Data Science Specialist, to learn the basics of Graph Neural Networks (GNNs) and how they can help you to improve predictions in your data.
Shift Remote: AI: Smarter AI with analytical graph databases - Victor Lee (Ti...Shift Conference
Today's analytical graph databases are taking organizations to another level by connecting all their data, representing knowledge better, and obtaining answers to deeper questions in real time. These benefits extend to the world of machine learning and AI. This talk will illustrate several ways in which graph databases and graph analytics can deliver smarter AI:
1. Unsupervised learning with graph algorithms.
2. Feature extraction and enrichment with graph patterns.
3. In-database ML techniques for graphs
The relationships between data sets matter. Discovering, analyzing, and learning those relationships is a central part to expanding our understand, and is a critical step to being able to predict and act upon the data. Unfortunately, these are not always simple or quick tasks.
To help the analyst we introduce RAPIDS, a collection of open-source libraries, incubated by NVIDIA and focused on accelerating the complete end-to-end data science ecosystem. Graph analytics is a critical piece of the data science ecosystem for processing linked data, and RAPIDS is pleased to offer cuGraph as our accelerated graph library.
Simply accelerating algorithms only addressed a portion of the problem. To address the full problem space, RAPIDS cuGraph strives to be feature-rich, easy to use, and intuitive. Rather than limiting the solution to a single graph technology, cuGraph supports Property Graphs, Knowledge Graphs, Hyper-Graphs, Bipartite graphs, and the basic directed and undirected graph.
A Python API allows the data to be manipulated as a DataFrame, similar and compatible with Pandas, with inputs and outputs being shared across the full RAPIDS suite, for example with the RAPIDS machine learning package, cuML.
This talk will present an overview of RAPIDS and cuGraph. Discuss and show examples of how to manipulate and analyze bipartite and property graph, plus show how data can be shared with machine learning algorithms. The talk will include some performance and scalability metrics. Then conclude with a preview of upcoming features, like graph query language support, and the general RAPIDS roadmap.
Artificial intelligence in IoT-to-core network operations and managementADVA
Danish Rafique’s OFC 2019 presentation explores the AI application space and its architectural integration into today’s end-to-end network management stack.
How Graph Data Science can turbocharge your Knowledge GraphNeo4j
Knowledge Graphs are becoming mission-critical across many industries. More recently, we are witnessing the application of Graph Data Science to Knowledge Graphs, offering powerful outcomes. But how do we define Knowledge Graphs in industry and how can they be useful for your project? In this talk, we will illustrate the various methods and models of Graph Data Science being applied to Knowledge Graphs and how they allow you to find implicit relationships in your graph which are impossible to detect in any other way. You will learn how graph algorithms from PageRank to Embeddings drive ever deeper insights in your data.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-yu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Chen-Ping Yu, Co-founder and CEO of Phiar, presents the "Separable Convolutions for Efficient Implementation of CNNs and Other Vision Algorithms" tutorial at the May 2019 Embedded Vision Summit.
Separable convolutions are an important technique for implementing efficient convolutional neural networks (CNNs), made popular by MobileNet’s use of depthwise separable convolutions. But separable convolutions are not a new concept, and their utility is not limited to CNNs. Separable convolutions have been widely studied and employed in classical computer vision algorithms as well, in order to reduce computation demands.
We begin this talk with an introduction to separable convolutions. We then explore examples of their application in classical computer vision algorithms and in efficient CNNs, comparing some recent neural network models. We also examine practical considerations of when and how to best utilize separable convolutions in order to maximize their benefits.
PyTorch Python Tutorial | Deep Learning Using PyTorch | Image Classifier Usin...Edureka!
( ** Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka PyTorch Tutorial (Blog: https://goo.gl/4zxMfU) will help you in understanding various important basics of PyTorch. It also includes a use-case in which we will create an image classifier that will predict the accuracy of an image data-set using PyTorch.
Below are the topics covered in this tutorial:
1. What is Deep Learning?
2. What are Neural Networks?
3. Libraries available in Python
4. What is PyTorch?
5. Use-Case of PyTorch
6. Summary
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Accelerating algorithmic and hardware advancements for power efficient on-dev...Qualcomm Research
Artificial Intelligence (AI), specifically deep learning, is revolutionizing industries, products, and core capabilities by delivering dramatically enhanced experiences. However, the deep neural networks of today are growing quickly in size and use too much memory, compute, and energy. Plus, to make AI truly ubiquitous, it needs to run on the end device within a tight power and thermal budget. One approach to address these issues is Bayesian deep learning. This presentation covers:
• Why AI algorithms and hardware need to be energy efficient
• How Bayesian deep learning is making neural networks more power efficient through model compression and quantization
• How we are doing fundamental research on AI algorithms and hardware to maximize power efficiency
Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Anal...Databricks
As data grows in size and connectedness dramatically in all dimensions, the potential for graph-enriched machine learning grows likewise, but scalable technologies are needed to both build models and apply them in real-time. Real-time deep-link graph pattern matching and analytics provides new opportunities for enriching your machine learning models with graph features.
‘In addition to the real-time deep-link aspect, the ability to process large datasets in a production pipeline provides a synergistic approach for the two distributed and performant platforms: Spark and TigerGraph. The TigerGraph graph database provides scalable real-time deep link graph analytics and augments Spark with graph analytics and predictions for a wide range of Machine Learning use cases.
In this session, we will explain the architecture and technical implementation for a TigerGraph+Spark graph-enhanced Machine Learning pipeline: Use TigerGraph both before training to extract (graph and non-graph) features and after training to apply the model on streaming data; use Spark to train and tune machine learning models at scale. As an example, we will present a solution in production at China Mobile that detects and prevents phone-based scams using machine learning with TigerGraph.
Specifically, the solution generates 118 graph features for 600 million users, to feed a machine learning system which detects three types of unwanted phone calls. TigerGraph then helps to deploy the model by extracting these 118 features in real-time for up to 10,000 calls per second, to give customers a real-time diagnosis of their incoming calls.
Similar to Graph Gurus Episode 19: Deep Learning Implemented by GSQL on a Native Parallel Graph Database (20)
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
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Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.