Deep recurrent neural networks are well-suited for sequence learning tasks like text classification and generation. The author discusses implementing recurrent neural networks in Spark for distributed deep learning on big data. Two use cases are described: predictive maintenance using sensor data to detect failures, and sentiment analysis of tweets using RNNs which achieve better accuracy than traditional classifiers.
(Paper Seminar detailed version) BART: Denoising Sequence-to-Sequence Pre-tra...hyunyoung Lee
(Detailed version) Paper seminar in NLP lab on "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension"(2021.03.04)
발표자: 이활석(NAVER)
발표일: 2017.11.
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨 지고 있습니다. 본 과정에서는 비지도학습의 가장 대표적인 방법인 오토인코더의 모든 것에 대해서 살펴보고자 합니다. 차원 축소관점에서 가장 많이 사용되는Autoencoder와 (AE) 그 변형 들인 Denoising AE, Contractive AE에 대해서 공부할 것이며, 데이터 생성 관점에서 최근 각광 받는 Variational AE와 (VAE) 그 변형 들인 Conditional VAE, Adversarial AE에 대해서 공부할 것입니다. 또한, 오토인코더의 다양한 활용 예시를 살펴봄으로써 현업과의 접점을 찾아보도록 노력할 것입니다.
1. Revisit Deep Neural Networks
2. Manifold Learning
3. Autoencoders
4. Variational Autoencoders
5. Applications
Overview of tree algorithms from decision tree to xgboostTakami Sato
For my understanding, I surveyed popular tree algorithms on Machine Learning and their evolution. This is the first time I wrote a presentation in English. So, I am happy if you give me a feedback.
(Paper Seminar detailed version) BART: Denoising Sequence-to-Sequence Pre-tra...hyunyoung Lee
(Detailed version) Paper seminar in NLP lab on "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension"(2021.03.04)
발표자: 이활석(NAVER)
발표일: 2017.11.
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨 지고 있습니다. 본 과정에서는 비지도학습의 가장 대표적인 방법인 오토인코더의 모든 것에 대해서 살펴보고자 합니다. 차원 축소관점에서 가장 많이 사용되는Autoencoder와 (AE) 그 변형 들인 Denoising AE, Contractive AE에 대해서 공부할 것이며, 데이터 생성 관점에서 최근 각광 받는 Variational AE와 (VAE) 그 변형 들인 Conditional VAE, Adversarial AE에 대해서 공부할 것입니다. 또한, 오토인코더의 다양한 활용 예시를 살펴봄으로써 현업과의 접점을 찾아보도록 노력할 것입니다.
1. Revisit Deep Neural Networks
2. Manifold Learning
3. Autoencoders
4. Variational Autoencoders
5. Applications
Overview of tree algorithms from decision tree to xgboostTakami Sato
For my understanding, I surveyed popular tree algorithms on Machine Learning and their evolution. This is the first time I wrote a presentation in English. So, I am happy if you give me a feedback.
발표자: 이활석 (Naver Clova)
발표일: 2017.11.
(현) NAVER Clova Vision
(현) TFKR 운영진
개요:
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨지고 있습니다.
특히 컴퓨터 비전 기술 분야에서는 지도학습에 해당하는 이미지 내에 존재하는 정보를 찾는 인식 기술에서,
비지도학습에 해당하는 특정 정보를 담는 이미지를 생성하는 기술인 생성 기술로 연구 동향이 바뀌어 가고 있습니다.
본 세미나에서는 생성 기술의 두 축을 담당하고 있는 VAE(variational autoencoder)와 GAN(generative adversarial network) 동작 원리에 대해서 간략히 살펴 보고, 관련된 주요 논문들의 결과를 공유하고자 합니다.
딥러닝에 대한 지식이 없더라도 생성 모델을 학습할 수 있는 두 방법론인 VAE와 GAN의 개념에 대해 이해하고
그 기술 수준을 파악할 수 있도록 강의 내용을 구성하였습니다.
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
발표자: 이활석 (Naver Clova)
발표일: 2017.11.
(현) NAVER Clova Vision
(현) TFKR 운영진
개요:
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨지고 있습니다.
특히 컴퓨터 비전 기술 분야에서는 지도학습에 해당하는 이미지 내에 존재하는 정보를 찾는 인식 기술에서,
비지도학습에 해당하는 특정 정보를 담는 이미지를 생성하는 기술인 생성 기술로 연구 동향이 바뀌어 가고 있습니다.
본 세미나에서는 생성 기술의 두 축을 담당하고 있는 VAE(variational autoencoder)와 GAN(generative adversarial network) 동작 원리에 대해서 간략히 살펴 보고, 관련된 주요 논문들의 결과를 공유하고자 합니다.
딥러닝에 대한 지식이 없더라도 생성 모델을 학습할 수 있는 두 방법론인 VAE와 GAN의 개념에 대해 이해하고
그 기술 수준을 파악할 수 있도록 강의 내용을 구성하였습니다.
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
"Mainstream access to deep learning technology will greatly impact most industries over the next three to five years."
So what exactly is deep learning? How does it work? And most importantly, why should you even care?
Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing.
Practical examples include:
-Vehicle, pedestrian and landmark identification for driver assistance
-Image recognition
-Speech recognition and translation
-Natural language processing
-Life sciences
-What You Will Learn
-Understand the intuition behind Artificial Neural Networks
-Apply Artificial Neural Networks in practice
-Understand the intuition behind Convolutional Neural Networks
-Apply Convolutional Neural Networks in practice
-Understand the intuition behind Recurrent Neural Networks
-Apply Recurrent Neural Networks in practice
-Understand the intuition behind Self-Organizing Maps
-Apply Self-Organizing Maps in practice
-Understand the intuition behind Boltzmann Machines
-Apply Boltzmann Machines in practice
-Understand the intuition behind AutoEncoders
-Apply AutoEncoders in practice
Separating Hype from Reality in Deep Learning with Sameer FarooquiDatabricks
Deep Learning is all the rage these days, but where does the reality of what Deep Learning can do end and the media hype begin? In this talk, I will dispel common myths about Deep Learning that are not necessarily true and help you decide whether you should practically use Deep Learning in your software stack.
I’ll begin with a technical overview of common neural network architectures like CNNs, RNNs, GANs and their common use cases like computer vision, language understanding or unsupervised machine learning. Then I’ll separate the hype from reality around questions like:
• When should you prefer traditional ML systems like scikit learn or Spark.ML instead of Deep Learning?
• Do you no longer need to do careful feature extraction and standardization if using Deep Learning?
• Do you really need terabytes of data when training neural networks or can you ‘steal’ pre-trained lower layers from public models by using transfer learning?
• How do you decide which activation function (like ReLU, leaky ReLU, ELU, etc) or optimizer (like Momentum, AdaGrad, RMSProp, Adam, etc) to use in your neural network?
• Should you randomly initialize the weights in your network or use more advanced strategies like Xavier or He initialization?
• How easy is it to overfit/overtrain a neural network and what are the common techniques to ovoid overfitting (like l1/l2 regularization, dropout and early stopping)?
Using Deep Learning to do Real-Time Scoring in Practical ApplicationsGreg Makowski
http://www.meetup.com/SF-Bay-ACM/events/227480571/
(see also YouTube for a recording of the presentation)
The talk will cover a brief review of neural network basics and the following types of neural network deep learning:
* autocorrelational - unsupervised learning for extracting features. He will describe how additional layers build complexity in the feature extraction.
* convolutional - how to detect shift invariant patterns in various data sources. Horizontal shift invariant detection applies to signals like speech recognition or IoT data. Horizontal and vertical shift invariance applies to images or videos, for faces or self driving cars
* discuss details of applying deep net systems for continuous or real time scoring
* reinforcement learning or Q Learning - such as learning how to play Atari video games
* continuous space word models - such as word2vec, skipgram training, NLP understanding and translation
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
Deep learning: the future of recommendationsBalázs Hidasi
An informative talk about deep learning and its potential uses in recommender systems. Presented at the Budapest Startup Safary, 21 April, 2016.
The breakthroughs of the last decade in neural network research and the quick increasing of computational power resulted in the revival of deep neural networks and the field focusing on their training: deep learning. Deep learning methods have succeeded in complex tasks where other machine learning methods have failed, such as computer vision and natural language processing. Recently deep learning has began to gain ground in recommender systems as well. This talk introduces deep learning and its applications, with emphasis on how deep learning methods can solve long standing recommendation problems.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Hardware machine learning provides an appealing architectural solution to the energy consumption and runtime bottlenecks in this era of big data. This work proposes a parallel digital VLSI architecture for the Cascade SVM algorithm.
Deep Learning and Tensorflow Implementation(딥러닝, 텐서플로우, 파이썬, CNN)_Myungyon Ki...Myungyon Kim
Deep learning and Tensorflow implementation
2016.11.16
<Cotents>
Feature Engineering
Deep Neural Network
Tensorflow
Tensorflow Implementation
Future works
References
This slides deals with several things about deep learning.
ex) History of Deep learning, Several difficulties and breakthroughs. Things related to deep learning such as activation functions, perceptrons, Backpropagation, pre-train, drop-out, Convolutional Neural Network (CNN), Simple implementation of Tensor Flow, Python, and so on.
딥러닝, 기계학습, 머신러닝, 텐서플로우, 파이썬
Similar to Deep Recurrent Neural Networks for Sequence Learning in Spark by Yves Mabiala (20)
FPGA-Based Acceleration Architecture for Spark SQL Qi Xie and Quanfu Wang Spark Summit
In this session we will present a Configurable FPGA-Based Spark SQL Acceleration Architecture. It is target to leverage FPGA highly parallel computing capability to accelerate Spark SQL Query and for FPGA’s higher power efficiency than CPU we can lower the power consumption at the same time. The Architecture consists of SQL query decomposition algorithms, fine-grained FPGA based Engine Units which perform basic computation of sub string, arithmetic and logic operations. Using SQL query decomposition algorithm, we are able to decompose a complex SQL query into basic operations and according to their patterns each is fed into an Engine Unit. SQL Engine Units are highly configurable and can be chained together to perform complex Spark SQL queries, finally one SQL query is transformed into a Hardware Pipeline. We will present the performance benchmark results comparing the queries with FGPA-Based Spark SQL Acceleration Architecture on XEON E5 and FPGA to the ones with Spark SQL Query on XEON E5 with 10X ~ 100X improvement and we will demonstrate one SQL query workload from a real customer.
VEGAS: The Missing Matplotlib for Scala/Apache Spark with DB Tsai and Roger M...Spark Summit
In this talk, we’ll present techniques for visualizing large scale machine learning systems in Spark. These are techniques that are employed by Netflix to understand and refine the machine learning models behind Netflix’s famous recommender systems that are used to personalize the Netflix experience for their 99 millions members around the world. Essential to these techniques is Vegas, a new OSS Scala library that aims to be the “missing MatPlotLib” for Spark/Scala. We’ll talk about the design of Vegas and its usage in Scala notebooks to visualize Machine Learning Models.
This presentation introduces how we design and implement a real-time processing platform using latest Spark Structured Streaming framework to intelligently transform the production lines in the manufacturing industry. In the traditional production line there are a variety of isolated structured, semi-structured and unstructured data, such as sensor data, machine screen output, log output, database records etc. There are two main data scenarios: 1) Picture and video data with low frequency but a large amount; 2) Continuous data with high frequency. They are not a large amount of data per unit. However the total amount of them is very large, such as vibration data used to detect the quality of the equipment. These data have the characteristics of streaming data: real-time, volatile, burst, disorder and infinity. Making effective real-time decisions to retrieve values from these data is critical to smart manufacturing. The latest Spark Structured Streaming framework greatly lowers the bar for building highly scalable and fault-tolerant streaming applications. Thanks to the Spark we are able to build a low-latency, high-throughput and reliable operation system involving data acquisition, transmission, analysis and storage. The actual user case proved that the system meets the needs of real-time decision-making. The system greatly enhance the production process of predictive fault repair and production line material tracking efficiency, and can reduce about half of the labor force for the production lines.
Improving Traffic Prediction Using Weather Data with Ramya RaghavendraSpark Summit
As common sense would suggest, weather has a definite impact on traffic. But how much? And under what circumstances? Can we improve traffic (congestion) prediction given weather data? Predictive traffic is envisioned to significantly impact how driver’s plan their day by alerting users before they travel, find the best times to travel, and over time, learn from new IoT data such as road conditions, incidents, etc. This talk will cover the traffic prediction work conducted jointly by IBM and the traffic data provider. As a part of this work, we conducted a case study over five large metropolitans in the US, 2.58 billion traffic records and 262 million weather records, to quantify the boost in accuracy of traffic prediction using weather data. We will provide an overview of our lambda architecture with Apache Spark being used to build prediction models with weather and traffic data, and Spark Streaming used to score the model and provide real-time traffic predictions. This talk will also cover a suite of extensions to Spark to analyze geospatial and temporal patterns in traffic and weather data, as well as the suite of machine learning algorithms that were used with Spark framework. Initial results of this work were presented at the National Association of Broadcasters meeting in Las Vegas in April 2017, and there is work to scale the system to provide predictions in over a 100 cities. Audience will learn about our experience scaling using Spark in offline and streaming mode, building statistical and deep-learning pipelines with Spark, and techniques to work with geospatial and time-series data.
A Tale of Two Graph Frameworks on Spark: GraphFrames and Tinkerpop OLAP Artem...Spark Summit
Graph is on the rise and it’s time to start learning about scalable graph analytics! In this session we will go over two Spark-based Graph Analytics frameworks: Tinkerpop and GraphFrames. While both frameworks can express very similar traversals, they have different performance characteristics and APIs. In this Deep-Dive by example presentation, we will demonstrate some common traversals and explain how, at a Spark level, each traversal is actually computed under the hood! Learn both the fluent Gremlin API as well as the powerful GraphFrame Motif api as we show examples of both simultaneously. No need to be familiar with Graphs or Spark for this presentation as we’ll be explaining everything from the ground up!
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark Marcin ...Spark Summit
Building accurate machine learning models has been an art of data scientists, i.e., algorithm selection, hyper parameter tuning, feature selection and so on. Recently, challenges to breakthrough this “black-arts” have got started. In cooperation with our partner, NEC Laboratories America, we have developed a Spark-based automatic predictive modeling system. The system automatically searches the best algorithm, parameters and features without any manual work. In this talk, we will share how the automation system is designed to exploit attractive advantages of Spark. The evaluation with real open data demonstrates that our system can explore hundreds of predictive models and discovers the most accurate ones in minutes on a Ultra High Density Server, which employs 272 CPU cores, 2TB memory and 17TB SSD in 3U chassis. We will also share open challenges to learn such a massive amount of models on Spark, particularly from reliability and stability standpoints. This talk will cover the presentation already shown on Spark Summit SF’17 (#SFds5) but from more technical perspective.
Apache Spark and Tensorflow as a Service with Jim DowlingSpark Summit
In Sweden, from the Rise ICE Data Center at www.hops.site, we are providing to reseachers both Spark-as-a-Service and, more recently, Tensorflow-as-a-Service as part of the Hops platform. In this talk, we examine the different ways in which Tensorflow can be included in Spark workflows, from batch to streaming to structured streaming applications. We will analyse the different frameworks for integrating Spark with Tensorflow, from Tensorframes to TensorflowOnSpark to Databrick’s Deep Learning Pipelines. We introduce the different programming models supported and highlight the importance of cluster support for managing different versions of python libraries on behalf of users. We will also present cluster management support for sharing GPUs, including Mesos and YARN (in Hops Hadoop). Finally, we will perform a live demonstration of training and inference for a TensorflowOnSpark application written on Jupyter that can read data from either HDFS or Kafka, transform the data in Spark, and train a deep neural network on Tensorflow. We will show how to debug the application using both Spark UI and Tensorboard, and how to examine logs and monitor training.
Apache Spark and Tensorflow as a Service with Jim DowlingSpark Summit
In Sweden, from the Rise ICE Data Center at www.hops.site, we are providing to reseachers both Spark-as-a-Service and, more recently, Tensorflow-as-a-Service as part of the Hops platform. In this talk, we examine the different ways in which Tensorflow can be included in Spark workflows, from batch to streaming to structured streaming applications. We will analyse the different frameworks for integrating Spark with Tensorflow, from Tensorframes to TensorflowOnSpark to Databrick’s Deep Learning Pipelines. We introduce the different programming models supported and highlight the importance of cluster support for managing different versions of python libraries on behalf of users. We will also present cluster management support for sharing GPUs, including Mesos and YARN (in Hops Hadoop). Finally, we will perform a live demonstration of training and inference for a TensorflowOnSpark application written on Jupyter that can read data from either HDFS or Kafka, transform the data in Spark, and train a deep neural network on Tensorflow. We will show how to debug the application using both Spark UI and Tensorboard, and how to examine logs and monitor training.
MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...Spark Summit
With the rapid growth of available datasets, it is imperative to have good tools for extracting insight from big data. The Spark ML library has excellent support for performing at-scale data processing and machine learning experiments, but more often than not, Data Scientists find themselves struggling with issues such as: low level data manipulation, lack of support for image processing, text analytics and deep learning, as well as the inability to use Spark alongside other popular machine learning libraries. To address these pain points, Microsoft recently released The Microsoft Machine Learning Library for Apache Spark (MMLSpark), an open-source machine learning library built on top of SparkML that seeks to simplify the data science process and integrate SparkML Pipelines with deep learning and computer vision libraries such as the Microsoft Cognitive Toolkit (CNTK) and OpenCV. With MMLSpark, Data Scientists can build models with 1/10th of the code through Pipeline objects that compose seamlessly with other parts of the SparkML ecosystem. In this session, we explore some of the main lessons learned from building MMLSpark. Join us if you would like to know how to extend Pipelines to ensure seamless integration with SparkML, how to auto-generate Python and R wrappers from Scala Transformers and Estimators, how to integrate and use previously non-distributed libraries in a distributed manner and how to efficiently deploy a Spark library across multiple platforms.
Next CERN Accelerator Logging Service with Jakub WozniakSpark Summit
The Next Accelerator Logging Service (NXCALS) is a new Big Data project at CERN aiming to replace the existing Oracle-based service.
The main purpose of the system is to store and present Controls/Infrastructure related data gathered from thousands of devices in the whole accelerator complex.
The data is used to operate the machines, improve their performance and conduct studies for new beam types or future experiments.
During this talk, Jakub will speak about NXCALS requirements and design choices that lead to the selected architecture based on Hadoop and Spark. He will present the Ingestion API, the abstractions behind the Meta-data Service and the Spark-based Extraction API where simple changes to the schema handling greatly improved the overall usability of the system. The system itself is not CERN specific and can be of interest to other companies or institutes confronted with similar Big Data problems.
Powering a Startup with Apache Spark with Kevin KimSpark Summit
In Between (A mobile App for couples, downloaded 20M in Global), from daily batch for extracting metrics, analysis and dashboard. Spark is widely used by engineers and data analysts in Between, thanks to the performance and expendability of Spark, data operating has become extremely efficient. Entire team including Biz Dev, Global Operation, Designers are enjoying data results so Spark is empowering entire company for data driven operation and thinking. Kevin, Co-founder and Data Team leader of Between will be presenting how things are going in Between. Listeners will know how small and agile team is living with data (how we build organization, culture and technical base) after this presentation.
Improving Traffic Prediction Using Weather Datawith Ramya RaghavendraSpark Summit
As common sense would suggest, weather has a definite impact on traffic. But how much? And under what circumstances? Can we improve traffic (congestion) prediction given weather data? Predictive traffic is envisioned to significantly impact how driver’s plan their day by alerting users before they travel, find the best times to travel, and over time, learn from new IoT data such as road conditions, incidents, etc. This talk will cover the traffic prediction work conducted jointly by IBM and the traffic data provider. As a part of this work, we conducted a case study over five large metropolitans in the US, 2.58 billion traffic records and 262 million weather records, to quantify the boost in accuracy of traffic prediction using weather data. We will provide an overview of our lambda architecture with Apache Spark being used to build prediction models with weather and traffic data, and Spark Streaming used to score the model and provide real-time traffic predictions. This talk will also cover a suite of extensions to Spark to analyze geospatial and temporal patterns in traffic and weather data, as well as the suite of machine learning algorithms that were used with Spark framework. Initial results of this work were presented at the National Association of Broadcasters meeting in Las Vegas in April 2017, and there is work to scale the system to provide predictions in over a 100 cities. Audience will learn about our experience scaling using Spark in offline and streaming mode, building statistical and deep-learning pipelines with Spark, and techniques to work with geospatial and time-series data.
Hiding Apache Spark Complexity for Fast Prototyping of Big Data Applications—...Spark Summit
In many cases, Big Data becomes just another buzzword because of the lack of tools that can support both the technological requirements for developing and deploying of the projects and/or the fluency of communication between the different profiles of people involved in the projects.
In this talk, we will present Moriarty, a set of tools for fast prototyping of Big Data applications that can be deployed in an Apache Spark environment. These tools support the creation of Big Data workflows using the already existing functional blocks or supporting the creation of new functional blocks. The created workflow can then be deployed in a Spark infrastructure and used through a REST API.
For better understanding of Moriarty, the prototyping process and the way it hides the Spark environment to the Big Data users and developers, we will present it together with a couple of examples based on a Industry 4.0 success cases and other on a logistic success case.
How Nielsen Utilized Databricks for Large-Scale Research and Development with...Spark Summit
Large-scale testing of new data products or enhancements to existing products in a research and development environment can be a technical challenge for data scientists. In some cases, tools available to data scientists lack production-level capacity, whereas other tools do not provide the algorithms needed to run the methodology. At Nielsen, the Databricks platform provided a solution to both of these challenges. This breakout session will cover a specific Nielsen business case where two methodology enhancements were developed and tested at large-scale using the Databricks platform. Development and large-scale testing of these enhancements would not have been possible using standard database tools.
Spline: Apache Spark Lineage not Only for the Banking Industry with Marek Nov...Spark Summit
Data lineage tracking is one of the significant problems that financial institutions face when using modern big data tools. This presentation describes Spline – a data lineage tracking and visualization tool for Apache Spark. Spline captures and stores lineage information from internal Spark execution plans and visualizes it in a user-friendly manner.
Goal Based Data Production with Sim SimeonovSpark Summit
Since the invention of SQL and relational databases, data production has been about specifying how data is transformed through queries. While Apache Spark can certainly be used as a general distributed query engine, the power and granularity of Spark’s APIs enables a revolutionary increase in data engineering productivity: goal-based data production. Goal-based data production concerns itself with specifying WHAT the desired result is, leaving the details of HOW the result is achieved to a smart data warehouse running on top of Spark. That not only substantially increases productivity, but also significantly expands the audience that can work directly with Spark: from developers and data scientists to technical business users. With specific data and architecture patterns spanning the range from ETL to machine learning data prep and with live demos, this session will demonstrate how Spark users can gain the benefits of goal-based data production.
Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Le...Spark Summit
Have you imagined a simple machine learning solution able to prevent revenue leakage and monitor your distributed application? To answer this question, we offer a practical and a simple machine learning solution to create an intelligent monitoring application based on simple data analysis using Apache Spark MLlib. Our application uses linear regression models to make predictions and check if the platform is experiencing any operational problems that can impact in revenue losses. The application monitor distributed systems and provides notifications stating the problem detected, that way users can operate quickly to avoid serious problems which directly impact the company’s revenue and reduce the time for action. We will present an architecture for not only a monitoring system, but also an active actor for our outages recoveries. At the end of the presentation you will have access to our training program source code and you will be able to adapt and implement in your company. This solution already helped to prevent about US$3mi in losses last year.
Getting Ready to Use Redis with Apache Spark with Dvir VolkSpark Summit
Getting Ready to use Redis with Apache Spark is a technical tutorial designed to address integrating Redis with an Apache Spark deployment to increase the performance of serving complex decision models. To set the context for the session, we start with a quick introduction to Redis and the capabilities Redis provides. We cover the basic data types provided by Redis and cover the module system. Using an ad serving use-case, we look at how Redis can improve the performance and reduce the cost of using complex ML-models in production. Attendees will be guided through the key steps of setting up and integrating Redis with Spark, including how to train a model using Spark then load and serve it using Redis, as well as how to work with the Spark Redis module. The capabilities of the Redis Machine Learning Module (redis-ml) will be discussed focusing primarily on decision trees and regression (linear and logistic) with code examples to demonstrate how to use these feature. At the end of the session, developers should feel confident building a prototype/proof-of-concept application using Redis and Spark. Attendees will understand how Redis complements Spark and how to use Redis to serve complex, ML-models with high performance.
Deduplication and Author-Disambiguation of Streaming Records via Supervised M...Spark Summit
Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by – Application of Databricks and AWS makes this a scalable implementation. Compute resources are comparably lower than traditional legacy technology using big boxes 24/7. Scalability is crucial as Elsevier’s Scopus data, the biggest scientific abstract repository, covers roughly 250 million authorships from 70 million abstracts covering a few hundred years. – We create a fingerprint for each content by deep learning and/or word2vec algorithms to expedite pairwise similarity calculation. These encoders substantially reduce compute time while maintaining semantic similarity (unlike traditional TFIDF or predefined taxonomies). We will briefly discuss how to optimize word2vec training with high parallelization. Moreover, we show how these encoders can be used to derive a standard representation for all our entities namely such as documents, authors, users, journals, etc. This standard representation can simplify the recommendation problem into a pairwise similarity search and hence it can offer a basic recommender for cross-product applications where we may not have a dedicate recommender engine designed. – Traditional author-disambiguation or record deduplication algorithms are batch-processing with small to no training data. However, we have roughly 25 million authorships that are manually curated or corrected upon user feedback. Hence, it is crucial to maintain historical profiles and hence we have developed a machine learning implementation to deal with data streams and process them in mini batches or one document at a time. We will discuss how to measure the accuracy of such a system, how to tune it and how to process the raw data of pairwise similarity function into final clusters. Lessons learned from this talk can help all sort of companies where they want to integrate their data or deduplicate their user/customer/product databases.
MatFast: In-Memory Distributed Matrix Computation Processing and Optimization...Spark Summit
The use of large-scale machine learning and data mining methods is becoming ubiquitous in many application domains ranging from business intelligence and bioinformatics to self-driving cars. These methods heavily rely on matrix computations, and it is hence critical to make these computations scalable and efficient. These matrix computations are often complex and involve multiple steps that need to be optimized and sequenced properly for efficient execution. This work presents new efficient and scalable matrix processing and optimization techniques based on Spark. The proposed techniques estimate the sparsity of intermediate matrix-computation results and optimize communication costs. An evaluation plan generator for complex matrix computations is introduced as well as a distributed plan optimizer that exploits dynamic cost-based analysis and rule-based heuristics The result of a matrix operation will often serve as an input to another matrix operation, thus defining the matrix data dependencies within a matrix program. The matrix query plan generator produces query execution plans that minimize memory usage and communication overhead by partitioning the matrix based on the data dependencies in the execution plan. We implemented the proposed matrix techniques inside the Spark SQL, and optimize the matrix execution plan based on Spark SQL Catalyst. We conduct case studies on a series of ML models and matrix computations with special features on different datasets. These are PageRank, GNMF, BFGS, sparse matrix chain multiplications, and a biological data analysis. The open-source library ScaLAPACK and the array-based database SciDB are used for performance evaluation. Our experiments are performed on six real-world datasets are: social network data ( e.g., soc-pokec, cit-Patents, LiveJournal), Twitter2010, Netflix recommendation data, and 1000 Genomes Project sample. Experiments demonstrate that our proposed techniques achieve up to an order-of-magnitude performance.
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.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
volume_up
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.
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
2. Outline
• Thales & Big Data
• On the difficulty of Sequence Learning
• Deep Learning for Sequence Learning
• Spark implementation of Deep Learning
• Use cases
– Predictive maintenance
– NLP
3. Thales & Big Data
Thales systems produce a huge quantity of data
Transportation systems (ticketing, supervision, …)
Security (radar traces, network logs, …)
Satellite (photos, videos, …)
which is often
Massive
Heterogeneous
Extremely dynamic
and where understanding the dynamics of the monitored phenomena
is mandatory Sequence Learning
4. What is sequence learning ?
Sequence learning refers to a set of ML tasks where a model has
to either deal with sequences as input, produce sequences as
output or both
Goal : Understand the dynamic of a sequence to
– Classify
– Predict
– Model
Typical applications
– Text
• Classify texts (sentiment analysis)
• Generate textual description of images (image captioning)
– Video
• Video classification
– Speech
• Speech to text
5. How is it typically handled ?
Taking into account the dynamic is difficult
– Often people do not bother
• E.g. text analysis using bag of word (one hot encoding)
– Problem for certain tasks such as sentiment classification (order of the words is important)
– Or use popular statistical approaches
• (Hidden) Markov model for prediction (and classification)
– Shortterm dependency (order 1) : 𝑃(𝑋$ = 𝑥 (𝑋$'( = 𝑥$'(,… , 𝑋$', = 𝑥$',)⁄ ) = 𝑃(𝑋$ = 𝑥$ 𝑋$'( = 𝑥$'()⁄
• Autoregressive approaches for time series forecasting
The chair is red 1 0 1 1 0 0 0 0
The cat is on a chair
The cat is young 1 1 0 0 1 1 0 0
1 1 1 0 0 1 1 1
The is chair red young cat on a
6. Link with artificial neural network ?
Artificial neural network is a set of statistical models inspired from the brain
– Transforms the input by applying at each layer (non linear) functions
– More layers equals more capabilities (≥ 2 hidden layers : Deep Learning)
• From manual features building to feature learning
Set of transformation and activation operations
– Affine : 𝒀 = 𝑾 𝒕
𝑿 + 𝒃, sigmoid activation :
𝟏
𝟏8𝐞𝐱𝐩 ('𝑿)
, tanh activation : 𝒀 = 𝐭𝐚𝐧𝐡 ( 𝑿)
• Only affine + activation layers = multi layer perceptron (available in Spark ML since 1.5.0)
– Convolutional : Apply a spatial convolution on the 1D/2D input (signal, image, …) : 𝐘 = 𝒄𝒐𝒏𝒗 𝑿, 𝑾 + 𝒃
• Learns spatial features used for classification (images) , prediction
– Recurrent : Introduces a recurrent part to learn dependencies between observations (features related to
the dynamic)
Objective
– Find the best weights W to minimize the difference between the predicted output and the desired one
(using back-propagation algorithm)
input
hidden
layers
output
7. Able to cope with varying size sequences either at the input or at the output
Recurrent Neural Network basics
One to many
(fixedsize input,
sequence output)
e.g. Image captioning
Many to many
(sequence input to sequence
output)
e.g. Speech to text
Many to one
(sequence input to fixedsize
output)
e.g. Text classification
Artificial neural networks with one or more recurrent layers
Classical neural network Recurrent neural network
𝒀 𝒌'𝟑 𝒀 𝒌'𝟐 𝒀 𝒌'𝟏 𝒀 𝒌
𝒀 𝒌
𝑿 𝒌'𝟑 𝑿 𝒌'𝟐 𝑿 𝒌'𝟏 𝑿 𝒌
𝒀 𝒌 = 𝒇(𝑾 𝒕 𝑿 𝒌 + 𝑯𝒀 𝒌'𝟏)
𝑿 𝒌𝑿
𝒀 𝒌 = 𝒇(𝑾 𝒕 𝑿 𝒌)
𝒀
Unrolled through time
𝒀 𝒌'𝟑 𝒀 𝒌'𝟐 𝒀 𝒌'𝟏 𝒀 𝒌
𝑿
𝒀 𝒌'𝟑 𝒀 𝒌'𝟐 𝒀 𝒌'𝟏 𝒀 𝒌
𝑿 𝒌'𝟑 𝑿 𝒌'𝟐 𝑿 𝒌'𝟏 𝑿 𝒌
𝑿 𝒌'𝟑 𝑿 𝒌'𝟐 𝑿 𝒌'𝟏 𝑿 𝒌
𝒀
8. On the difficulty of training recurrent networks
RNNs are (were) known to be difficult to learn
– More weights and more computational steps
• More computationally expensive (accelerator needed for matrix ops : Blas or GPU)
• More data needed to converge (scalability over Big Data architectures : Spark)
– Theano, Tensor Flow, Caffe do not have distributed versions
– Unable to learn long range dependencies (Graves & Al 2014)
• At a given time t, RNN does not remember the observations before 𝑋J',
⇒ New RNN architectures with memory preservation (more context)
𝑍$ = 𝑓 𝑊N
O
𝑋$ + 𝐻N 𝑌$'(
𝑅$ = 𝑓(𝑊S
O
𝑋$ + 𝐻S 𝑌$'()
𝐻T$ = tanh(𝑊YJZ[
O
𝑋$ + 𝑈 𝑌$'( o 𝑅$ )
𝑌$ = 1 − 𝑍$ 𝑌$'( + 𝑍$ 𝐻T$
LSTM GRU
9. Recurrent neural networks in Spark
Spark implementation of DL algorithms (data parallel)
– All the needed blocks
• Affine, convolutional, recurrent layers (Simple and GRU)
• Sigmoid, tanh, reLU activations
• SGD, rmsprop, adadelta optimizers
– CPU (and GPU backend)
– Fully compatible with existing DL library in Spark ML
Performance
– On 6 nodes cluster (CPU)
• 5.46 average speedup (some communication overhead)
– About the same speedup as MLP in Spark ML
Driver
Worker 1
Worker 2
Worker 3
Resulting gradients (2)
Model broadcast (1)
10. Use case 1 : predictive maintenance (1)
Context
– Thales and its clients build systems in different domains
• Transportation (ticketing, controlling)
• Defense (radar)
• Satellites
– Need better and more accurate maintenance services
• From planned maintenance (every x days) to an alert maintenance
• From expert detection to automatic failure prediction
• From whole subsystem changes to more localized reparations
Goal
– Detect early signs of a (sub)system failure using data coming
from sensors monitoring the health of a system (HUMS)
11. Use case 1 : predictive maintenance (2)
Example on a real system
– 20 sensors (20 values every 5 minutes), label (failure or not)
– Take 3 hours of data and predict the probability of failure in the next hour (fully
customizable)
Learning using MLLIB
12. Use case 1 : predictive maintenance (3)
Recurrent net learning
Impact of recurrent nets
– Logistic regression
• 70% detection with 70% accuracy
– Recurrent Neural Network
• 85% detection with 75% accuracy
13. Use case 2 : Sentiment analysis (1)
Context
– Social network analysis application developed at Thales (Twitter, Facebook,
blogs, forums)
• Analyze both the content of the texts and the relations (texts, actors)
– Multiple (big data) analysis
• Actor community detection
• Text clustering (themes)
• …
Focus on
– Sentiment analysis on the collected texts
• Classify texts based on their sentiment
14. Use case 2 : Sentiment analysis (2)
Learning dataset
– Sentiment140 + Kaggle challenge (1.5M labeled tweets)
– 50% positives, 50% negatives
Compare Bag of words + classifier approaches (Naïve Bayes, SVM, logistic
regression) versus RNN