Art of Feature Engineering for Data Science with Nabeel Sarwar
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We will discuss what feature engineering is all about , various techniques to use and how to scale to 20000 column datasets using random forest, svd, pca. Also demonstrated is how we can build a service around these to save time and effort when building 100s of models. We will share how we did all this using spark ml to build logistic regression, neural networks, Bayesian networks, etc.
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In this session, Zaidi will discuss the sparklyr package, which is a feature-rich and tidy interface for data science with Spark, and will show how it can be coupled with Microsoft R Server and extended with it’s lower-level API to become a full, first-class citizen of Spark. Learn how easy it is to go from single-threaded, memory-bound R functions to multi-threaded, multi-node, out-of-memory applications that can be deployed in a distributed cluster environment with minimal amount of code changes. You’ll also get best practices for reproducibility and performance by looking at a real-world case study of default risk classification and prediction entirely through R and Spark.
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Presentation consists of an amazing bundle of Pro tips and tricks for building an insanely scalable Apache Spark and Spark Streaming based data pipeline.
Presentation consists of 4 parts:
* Quick intro to Spark
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* Data Warehouse and Messaging
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An in depth introduction to Apache Spark. Presented at Bangalore Apache Spark Meetup on 21/02/2015.
Near Data Computing Architectures: Opportunities and Challenges for Apache Spark
This talk was given at Spark Summit Europe 2017.
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Spark - The Ultimate Scala Collections by Martin Odersky
Spark - The Ultimate Scala Collections by Martin Odersky
Resource-Efficient Deep Learning Model Selection on Apache Spark
Resource-Efficient Deep Learning Model Selection on Apache Spark
Building a Dataset Search Engine with Spark and Elasticsearch: Spark Summit E...
Building a Dataset Search Engine with Spark and Elasticsearch: Spark Summit E...
Tagging and Processing Data in Real Time-(Hari Shreedharan and Siddhartha Jai...
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MatFast: In-Memory Distributed Matrix Computation Processing and Optimization...
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Extending the R API for Spark with sparklyr and Microsoft R Server with Ali Z...
Extending the R API for Spark with sparklyr and Microsoft R Server with Ali Z...
Extreme Apache Spark: how in 3 months we created a pipeline that can process ...
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Near Data Computing Architectures: Opportunities and Challenges for Apache Spark
Near Data Computing Architectures: Opportunities and Challenges for Apache Spark
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Feature Hashing for Scalable Machine Learning with Nick Pentreath
Feature hashing is a powerful technique for handling high-dimensional features in machine learning. It is fast, simple, memory-efficient, and well suited to online learning scenarios. While an approximation, it has surprisingly low accuracy tradeoffs in many machine learning problems. Feature hashing has been made somewhat popular by libraries such as Vowpal Wabbit and scikit-learn. In Spark MLlib, it is mostly used for text features; however, its use cases extend more broadly. Many Spark users are not familiar with the ways in which feature hashing might be applied to their problems. In this talk, I will cover the basics of feature hashing, and how to use it for all feature types in machine learning. I will also introduce a more flexible and powerful feature hashing transformer for use within Spark ML pipelines. Finally, I will explore the performance and scalability tradeoffs of feature hashing on various datasets.
Low Touch Machine Learning with Leah McGuire (Salesforce)
Leveraging your data to make better decisions is something every business wants to do, but doing it correctly involves weeks or months of work from highly skilled, hard to find individuals. However, many of the laborious steps a machine learning specialist follows in creating a custom application can be automated with enough compute power and flexibility. By leveraging Spark to do machine learning at scale, Salesforce Einstein has created a system that lets individuals with domain knowledge, but no machine learning expertise, create high quality, high impact machine learning applications.
VSSML17 L5. Basic Data Transformations and Feature Engineering
Valencian Summer School in Machine Learning 2017 - Day 2
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https://bigml.com/events/valencian-summer-school-in-machine-learning-2017
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Overview of Machine Learning and Feature Engineering
Machine Learning 101 Tutorial at Strata NYC, Sep 2015
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How to Share State Across Multiple Apache Spark Jobs using Apache Ignite with...
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A Tale of Two Graph Frameworks on Spark: GraphFrames and Tinkerpop OLAP Artem...
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Improving Traffic Prediction Using Weather Data with Ramya Raghavendra
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.
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark Marcin ...
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.
Indicium: Interactive Querying at Scale Using Apache Spark, Zeppelin, and Spa...
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MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...
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.
VEGAS: The Missing Matplotlib for Scala/Apache Spark with Roger Menezes and D...
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.
Working with Skewed Data: The Iterative Broadcast with Fokko Driesprong Rob K...
Skewed data is the enemy when joining tables using Spark. It shuffles a large proportion of the data onto a few overloaded nodes, bottlenecking Spark’s parallelism and resulting in out of memory errors. The go-to answer is to use broadcast joins; leaving the large, skewed dataset in place and transmitting a smaller table to every machine in the cluster for joining. But what happens when your second table is too large to broadcast, and does not fit into memory? Or even worse, when a single key is bigger than the total size of your executor? Firstly, we will give an introduction into the problem. Secondly, the current ways of fighting the problem will be explained, including why these solutions are limited. Finally, we will demonstrate a new technique – the iterative broadcast join – developed while processing ING Bank’s global transaction data. This technique, implemented on top of the Spark SQL API, allows multiple large and highly skewed datasets to be joined successfully, while retaining a high level of parallelism. This is something that is not possible with existing Spark join types.
One-Pass Data Science In Apache Spark With Generative T-Digests with Erik Erl...
The T-Digest has earned a reputation as a highly efficient and versatile sketching data structure; however, its applications as a fast generative model are less appreciated. Several common algorithms from machine learning use randomization of feature columns as a building block. Column randomization is an awkward and expensive operation when performed directly, but when implemented with generative T-Digests, it can be accomplished elegantly in a single pass that also parallelizes across Spark data partitions. In this talk Erik will review the principles of T-Digest sketching, and how T-Digests can be applied as generative models. He will explain how generative T-Digests can be used to implement fast randomization of columnar data, and conclude with demonstrations of T-Digest randomization applied to Variable Importance, Random Forest Clustering and Feature Reduction. Attendees will leave this talk with an understanding of T-Digest sketching, how T-Digests can be used as generative models, and insights into applying generative T-Digests to accelerate their own data science projects.
Histogram Equalized Heat Maps from Log Data via Apache Spark with Arvind Rao
Reverse geocoding is one of HERE Technologies most heavily used services. From its access logs geocodes can be extracted and then counted with respect to some cellulation of the earth–-creating a sparse heat map. For our Place & Address Search products we use such a heat map to define a notion of relative place importance to rank and index addresses and places. However, the large data size, sparsity, and variations in traffic from which different global heat maps may be derived, makes faithful visualization and comparison a challenge. Additionally, common implementations of spatial image processing techniques that can help address the aforementioned challenges don’t map directly onto Spark’s computing engine.
In this talk Arvind Rao will describe implementations of histogram equalization and kernel-based sparse image processing methods on Spark. Histogram equalization, which is best known as a method of contrast enhancement, automatically normalizes images, facilitating comparison. Along the way, Arvind will talk about how HERE uses heat maps as a feature in their autocompletion service, and say just enough about perception of contrast to put histogram equalization in context.
Natural Language Understanding at Scale with Spark-Native NLP, Spark ML, and ...
Natural language processing is a key component in many data science systems that must understand or reason about text. Common use cases include question answering, paraphrasing or summarization, sentiment analysis, natural language BI, language modeling, and disambiguation. Building such systems usually requires combining three types of software libraries: NLP annotation frameworks, machine learning frameworks, and deep learning frameworks. Ideally, all three of these pieces should be able to be integrated into a single workflow. This makes development, experimentation, and deploying results much easier. Spark’s MLlib provides a number of machine learning algorithms, and now there are also projects making deep learning achievable in MLlib pipelines. All we need is the NLP annotation frameworks. SparkNLP adds NLP annotations into the MLlib ecosystem. This talk will introduce SparkNLP: how to use it, its current functionality, and where it is going in the future.
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Experimental Design for Distributed Machine Learning with Myles Baker
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VSSML17 L5. Basic Data Transformations and Feature Engineering
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Overview of Machine Learning and Feature Engineering
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No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark Marcin ...
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Indicium: Interactive Querying at Scale Using Apache Spark, Zeppelin, and Spa...
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MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...
MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...
VEGAS: The Missing Matplotlib for Scala/Apache Spark with Roger Menezes and D...
VEGAS: The Missing Matplotlib for Scala/Apache Spark with Roger Menezes and D...
Working with Skewed Data: The Iterative Broadcast with Fokko Driesprong Rob K...
Working with Skewed Data: The Iterative Broadcast with Fokko Driesprong Rob K...
One-Pass Data Science In Apache Spark With Generative T-Digests with Erik Erl...
One-Pass Data Science In Apache Spark With Generative T-Digests with Erik Erl...
Histogram Equalized Heat Maps from Log Data via Apache Spark with Arvind Rao
Histogram Equalized Heat Maps from Log Data via Apache Spark with Arvind Rao
Natural Language Understanding at Scale with Spark-Native NLP, Spark ML, and ...
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Machine learning for IoT - unpacking the blackbox
Have you ever considered Machine Learning as a black box? It sounds as a kind of magic happening. Although being one among many solutions available, Azure ML has proved to be a great balance between flexibility, usability and affordable price. But how does Azure ML compare with the other ML providers? How to choose the appropriate algorithm? Do you understand the key performance indicators and how to improve the quality of your models? The session is about understanding the black box and using it for IoT workload and not only.
L5. Data Transformation and Feature Engineering
Valencian Summer School 2015
Day 1
Lecture 5
Data Transformation and Feature Engineering
Charles Parker (Alston Trading)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Feature Engineering
Tips & Tricks for Feature Engineering / Applied Machine Learning. This presentation was given as part of the Sao Paulo ML meetup.
Preprocessing.ppt
data preprocessing
techniques of data cleaning
univerisity
knowledge discovery and data mining
Data extraction, cleanup & transformation tools 29.1.16
To cover the concepts of Data preprocessing in data warehouse.
Automated product categorization
How Skroutz S.A. utilizes Deep Learning and Machine Learning techniques to efficiently serve product categorization! Based on my talk at Athens PyData meetup!
Automated product categorization
The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization
Introduction to computer vision with Convoluted Neural Networks
Introduction to computer vision with Convoluted Neural Networks - going over history of CNNs, describing basic concepts such as convolution and discussing applications of computer vision and image recognition technologies
Continuous Inspection - Uma abordagem efetiva para melhoria contínua da quali...
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Data Normalization and Alignment in Heterogeneous Data Sets
Data Normalization and Alignment in Heterogeneous Data Sets
Data extraction, cleanup & transformation tools 29.1.16
Data extraction, cleanup & transformation tools 29.1.16
Introduction to computer vision with Convoluted Neural Networks
Introduction to computer vision with Convoluted Neural Networks
Continuous Inspection - Uma abordagem efetiva para melhoria contínua da quali...
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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.
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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.
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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.
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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.
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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...
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 Volk
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.
Apache Spark-Bench: Simulate, Test, Compare, Exercise, and Yes, Benchmark wit...
spark-bench is an open-source benchmarking tool, and it’s also so much more. spark-bench is a flexible system for simulating, comparing, testing, and benchmarking Spark applications and Spark itself. spark-bench originally began as a benchmarking suite to get timing numbers on very specific algorithms mostly in the machine learning domain. Since then it has morphed into a highly configurable and flexible framework suitable for many use cases. This talk will discuss the high level design and capabilities of spark-bench before walking through some major, practical use cases. Use cases include, but are certainly not limited to: regression testing changes to Spark; comparing performance of different hardware and Spark tuning options; simulating multiple notebook users hitting a cluster at the same time; comparing parameters of a machine learning algorithm on the same set of data; providing insight into bottlenecks through use of compute-intensive and i/o-intensive workloads; and, yes, even benchmarking. In particular this talk will address the use of spark-bench in developing new features features for Spark core.
Apache Spark—Apache HBase Connector: Feature Rich and Efficient Access to HBa...
Both Spark and HBase are widely used, but how to use them together with high performance and simplicity is a very challenging topic. Spark HBase Connector(SHC) provides feature rich and efficient access to HBase through Spark SQL. It bridges the gap between the simple HBase key value store and complex relational SQL queries and enables users to perform complex data analytics on top of HBase using Spark. SHC implements the standard Spark data source APIs, and leverages the Spark catalyst engine for query optimization. To achieve high performance, SHC constructs the RDD from scratch instead of using the standard HadoopRDD. With the customized RDD, all critical techniques can be applied and fully implemented, such as partition pruning, column pruning, predicate pushdown and data locality. The design makes the maintenance easy, while achieving a good tradeoff between performance and simplicity. In addition to fully supporting all the Avro schemas natively, SHC has also integrated natively with Phoenix data types. With SHC, Spark can execute batch jobs to read/write data from/into Phoenix tables. Phoenix can also read/write data from/into HBase tables created by SHC. For example, users can run a complex SQL query on top of an HBase table created by Phoenix inside Spark, perform a table join against an Dataframe which reads the data from a Hive table, or integrate with Spark Streaming to implement a more complicated system. In this talk, apart from explaining why SHC is of great use, we will also demo how SHC works, how to use SHC in secure/non-secure clusters, how SHC works with multiple secure HBase clusters, etc. This talk will also benefit people who use Spark and other data sources (besides HBase) as it inspires them with ideas of how to support high performance data source access at the Spark DataFrame level.
Variant-Apache Spark for Bioinformatics with Piotr Szul
This talk will showcase work done by the bioinformatics team at CSIRO in Sydney, Australia to make Spark more useful and usable for the bioinformatics community. They have created a custom library, variant-spark, which provides a DSL and also a custom implementation of Spark ML via random forests for genomic pipeline processing. We’ve created a demo, using their ‘Hipster-genome’ and a Databricks notebook to better explain their library to the world-wide bioinformatics community. This notebooks compares results with another popular genomics library (HAIL.io) as well.
Running Spark Inside Containers with Haohai Ma and Khalid Ahmed
This presentation describes the journey we went through in containerizing Spark workload into multiple elastic Spark clusters in a multi-tenant kubernetes environment. Initially we deployed Spark binaries onto a host-level filesystem, and then the Spark drivers, executors and master can transparently migrate to run inside a Docker container by automatically mounting host-level volumes. In this environment, we do not need to prepare a specific Spark image in order to run Spark workload in containers. We then utilized Kubernetes helm charts to deploy a Spark cluster. The administrator could further create a Spark instance group for each tenant. A Spark instance group, which is akin to the Spark notion of a tenant, is logically an independent kingdom for a tenant’s Spark applications in which they own dedicated Spark masters, history server, shuffle service and notebooks. Once a Spark instance group is created, it automatically generates its image and commits to a specified repository. Meanwhile, from Kubernetes’ perspective, each Spark instance group is a first-class deployment and thus the administrator can scale up/down its size according to the tenant’s SLA and demand. In a cloud-based data center, each Spark cluster can provide a Spark as a service while sharing the Kubernetes cluster. Each tenant that is registered into the service gets a fully isolated Spark instance group. In an on-prem Kubernetes cluster, each Spark cluster can map to a Business Unit, and thus each user in the BU can get a dedicated Spark instance group. The next step on this journey will address the resource sharing across Spark instance groups by leveraging new Kubernetes’ features (Kubernetes31068/9), as well as the Elastic workload containers depending on job demands (Spark18278). Demo: https://www.youtube.com/watch?v=eFYu6o3-Ea4&t=5s
Best Practices for Using Alluxio with Apache Spark with Gene Pang
Alluxio, formerly Tachyon, is a memory speed virtual distributed storage system and leverages memory for storing data and accelerating access to data in different storage systems. Many organizations and deployments use Alluxio with Apache Spark, and some of them scale out to over PB’s of data. Alluxio can enable Spark to be even more effective, in both on-premise deployments and public cloud deployments. Alluxio bridges Spark applications with various storage systems and further accelerates data intensive applications. In this talk, we briefly introduce Alluxio, and present different ways how Alluxio can help Spark jobs. We discuss best practices of using Alluxio with Spark, including RDDs and DataFrames, as well as on-premise deployments and public cloud deployments.
Smack Stack and Beyond—Building Fast Data Pipelines with Jorg Schad
There are an ever increasing number of use cases, like online fraud detection, for which the response times of traditional batch processing are too slow. In order to be able to react to such events in close to real-time, you need to go beyond classical batch processing and utilize stream processing systems such as Apache Spark Streaming, Apache Flink, or Apache Storm. These systems, however, are not sufficient on their own. For an efficient and fault-tolerant setup, you also need a message queue and storage system. One common example for setting up a fast data pipeline is the SMACK stack. SMACK stands for Spark (Streaming) – the stream processing system Mesos – the cluster orchestrator Akka – the system for providing custom actors for reacting upon the analyses Cassandra – the storage system Kafka – the message queue Setting up this kind of pipeline in a scalable, efficient and fault-tolerant manner is not trivial. First, this workshop will discuss the different components in the SMACK stack. Then, participants will get hands-on experience in setting up and maintaining data pipelines.
Near Data Computing Architectures for Apache Spark: Challenges and Opportunit...
Scale-out big data processing frameworks like Apache Spark have been designed to use on off the shelf commodity machines where each machine has the modest amount of compute , memory and storage capacity. Recent advancement in the hardware technology motivates understanding Spark performance on novel hardware architectures. Our earlier work has shown that the performance of Spark based data analytics is bounded by the frequent accesses to the DRAM. In this talk, we argue in favor of Near Data Computing Architectures that enable processing the data where it resides (e.g Smart SSDs and Compute Memories) for Apache Spark. We envision a programmable logic based hybrid near-memory and near-storage compute architecture for Apache Spark. Furthermore we discuss the challenges involved to achieve 10x performance gain for Apache Spark on NDC architectures.
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【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【Q微信741003700】文凭即可
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三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
Adjusting primitives for graph : SHORT REPORT / NOTES
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
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2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
原版定制【微信:41543339】【(BCU毕业证书)伯明翰城市大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project PresentationBoston Institute of Analytics
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/一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
原版定制【微信:41543339】【(Deakin毕业证书)迪肯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
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三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
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1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
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IIT毕业证【微信95270640】购买（伊利诺伊理工大学毕业证成绩单硕士学历）Q微信95270640代办IIT学历认证留信网伪造伊利诺伊理工大学学位证书精仿伊利诺伊理工大学本科/硕士文凭证书补办伊利诺伊理工大学 diplomaoffer,Transcript购买伊利诺伊理工大学毕业证成绩单购买IIT假毕业证学位证书购买伪造伊利诺伊理工大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
#一整套伊利诺伊理工大学文凭证件办理#—包含伊利诺伊理工大学伊利诺伊理工大学毕业证假文凭学历认证|使馆认证|归国人员证明|教育部认证|留信网认证永远存档教育部学历学位认证查询办理国外文凭国外学历学位认证#我们提供全套办理服务。
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国外毕业证学位证成绩单办理方法：
1客户提供办理伊利诺伊理工大学伊利诺伊理工大学毕业证假文凭信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
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6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
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实体公司专业为您服务如有需要请联系我: 微信95270640声和哐咣的关门声待山娃醒来时父亲早已上班去了床头总搁着山娃最爱吃的馒头和肉包还有白花花的豆浆父亲中午留在工地吃饭和午休山娃的中饭是对面快餐店送来的不用山娃付钱父亲早跟老板谈妥了钱到时一起结父亲给山娃配了台手机二手货诺基亚的父亲说有什么事只管给他挂电话能拥有自己的手机山娃很高兴除了玩游戏发短信除了挂电话给爷爷奶奶和母亲山娃还给班主任邱老师连挂了二个电话并给同学阿强和阿昌家挂山娃兴奋地向他们诉说城市一
Malana- Gimlet Market Analysis (Portfolio 2)
A market analysys on Spotify's parent Podcast company Gimlet.
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
原版定制【微信:41543339】【(Bradford毕业证书)布拉德福德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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CU毕业证【微信95270640】（卡尔顿大学毕业证成绩单本科学历）Q微信95270640(补办CU学位文凭证书)卡尔顿大学留信网学历认证怎么办理卡尔顿大学毕业证成绩单精仿本科学位证书硕士文凭证书认证Seneca College diplomaoffer,Transcript办理硕士学位证书造假卡尔顿大学假文凭学位证书制作CU本科毕业证书硕士学位证书精仿卡尔顿大学学历认证成绩单修改制作，办理真实认证、留信认证、使馆公证、购买成绩单，购买假文凭，购买假学位证，制造假国外大学文凭、毕业公证、毕业证明书、录取通知书、Offer、在读证明、雅思托福成绩单、假文凭、假毕业证、请假条、国际驾照、网上存档可查！
如果您是以下情况，我们都能竭诚为您解决实际问题：【公司采用定金+余款的付款流程，以最大化保障您的利益，让您放心无忧】
1、在校期间，因各种原因未能顺利毕业，拿不到官方毕业证+微信95270640
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面向美国乔治城大学毕业留学生提供以下服务:
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我们从事工作十余年的有着丰富经验的业务顾问，熟悉海外各国大学的学制及教育体系，并且以挂科生解决毕业材料不全问题为基础，为客户量身定制1对1方案，未能毕业的回国留学生成功搭建回国顺利发展所需的桥梁。我们一直努力以高品质的教育为起点，以诚信、专业、高效、创新作为一切的行动宗旨，始终把“诚信为主、质量为本、客户第一”作为我们全部工作的出发点和归宿点。同时为海内外留学生提供大学毕业证购买、补办成绩单及各类分数修改等服务；归国认证方面，提供《留信网入库》申请、《国外学历学位认证》申请以及真实学籍办理等服务，帮助众多莘莘学子实现了一个又一个梦想。
专业服务，请勿犹豫联系我
如果您真实毕业回国，对于学历认证无从下手，请联系我，我们免费帮您递交
诚招代理：本公司诚聘当地代理人员，如果你有业余时间，或者你有同学朋友需要，有兴趣就请联系我
你赢我赢，共创双赢
你做代理，可以帮助卡尔顿大学同学朋友
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你做代理，可以挽救卡尔顿大学一个个人才
你做代理，你将是别人人生卡尔顿大学的转折点
你做代理，可以改变自己，改变他人，给他人和自己一个机会他交友与城里人交友但他俩就好像是两个世界里的人根本拢不到一块儿不知不觉山娃倒跟周围出租屋里的几个小伙伴成了好朋友因为他们也是从乡下进城过暑假的小学生快乐的日子总是过得飞快山娃尚未完全认清那几位小朋友时他们却一个接一个地回家了山娃这时才恍然发现二个月的暑假已转到了尽头他的城市生活也将划上一个不很圆满的句号了值得庆幸的是山娃早记下了他们的学校和联系方式说也奇怪在山娃离城的头一天父亲居然请假陪山娃耍了活
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UPenn毕业证【微信95270640】办理宾夕法尼亚大学毕业证原版一模一样、UPenn毕业证制作【Q微信95270640】《宾夕法尼亚大学毕业证购买流程》《UPenn成绩单制作》宾夕法尼亚大学毕业证书UPenn毕业证文凭宾夕法尼亚大学
本科毕业证书,学历学位认证如何办理【留学国外学位学历认证、毕业证、成绩单、大学Offer、雅思托福代考、语言证书、学生卡、高仿教育部认证等一切高仿或者真实可查认证服务】代办国外（海外）英国、加拿大、美国、新西兰、澳大利亚、新西兰等国外各大学毕业证、文凭学历证书、成绩单、学历学位认证真实可查。
办国外宾夕法尼亚大学宾夕法尼亚大学硕士学位证成绩单教育部学历学位认证留信认证大使馆认证留学回国人员证明修改成绩单信封申请学校offer录取通知书在读证明offer letter。
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专业服务请勿犹豫联系我！联系人微信号：95270640诚招代理：本公司诚聘当地代理人员如果你有业余时间有兴趣就请联系我们。
国外宾夕法尼亚大学宾夕法尼亚大学硕士学位证成绩单办理过程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。我们在哪里父母对我们的爱和思念为我们的生命增加了光彩给予我们自由追求的力量生活的力量我们也不忘感恩正因为这股感恩的线牵着我们使我们在一年的结束时刻义无反顾的踏上了回家的旅途人们常说父母恩最难回报愿我能以当年爸爸妈妈对待小时候的我们那样耐心温柔地对待我将渐渐老去的父母体谅他们以反哺之心奉敬父母以感恩之心孝顺父母哪怕只为父母换洗衣服为父母喂饭送汤按摩酸痛的腰背握着父母的手扶着他们一步一步地慢慢散步.娃
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
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.
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
原版定制【微信:41543339】【(Adelaide毕业证书)阿德莱德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
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三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
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3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
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一比一原版(QU毕业证)皇后大学毕业证成绩单
QU毕业证【微信95270640】办理皇后大学毕业证原版一模一样、QU毕业证制作【Q微信95270640】《皇后大学毕业证购买流程》《QU成绩单制作》皇后大学毕业证书QU毕业证文凭皇后大学
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可办理以下真实皇后大学存档留学生信息存档认证：
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3购买英美真实学籍（不用正常就读直接出学历）；
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2开始安排制作皇后大学毕业证成绩单电子图；
3皇后大学毕业证成绩单电子版做好以后发送给您确认；
4皇后大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5皇后大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — — — — — — — — 《文凭顾问Q/微：95270640》很感动很无奈房东的儿子小伍笨手笨脚的不会说普通话满口粤语态度十分傲慢一副盛气凌人的样子山娃试图接近他跟他交友与城里人交友但他俩就好像是两个世界里的人根本拢不到一块儿不知不觉山娃倒跟周围出租屋里的几个小伙伴成了好朋友因为他们也是从乡下进城过暑假的小学生快乐的日子总是过得飞快山娃尚未完全认清那几位小朋友时他们却一个接一个地回家了山娃这时才恍然发现二个月的暑假已转到了尽头他的城市生活也将划上一个不很圆义
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
UIUC毕业证offer【微信95270640】☀《伊利诺伊大学|厄巴纳-香槟分校毕业证购买》GoogleQ微信95270640《UIUC毕业证模板办理》加拿大文凭、本科、硕士、研究生学历都可以做,二、业务范围：
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(诚招代理)办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡
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办理伊利诺伊大学|厄巴纳-香槟分校伊利诺伊大学|厄巴纳-香槟分校毕业证offer流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
-办理真实使馆公证（即留学回国人员证明）
-办理各国各大学文凭（世界名校一对一专业服务,可全程监控跟踪进度）
-全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！
（详情请加一下 文凭顾问+微信:95270640）欢迎咨询！的鬼地方父亲的家在高楼最底屋最下面很矮很黑是很不显眼的地下室父亲的家安在别人脚底下须绕过高楼旁边的垃圾堆下八个台阶才到父亲的家很狭小除了一张单人床和一张小方桌几乎没有多余的空间山娃一下子就联想起学校的男小便处山娃很想笑却怎么也笑不出来山娃很迷惑父亲的家除了一扇小铁门连窗户也没有墓穴一般阴森森有些骇人父亲的城也便成了山娃的城父亲的家也便成了山娃的家父亲让山娃呆在屋里做作业看电视最多只能在门口透透气间
一比一原版(BU毕业证)波士顿大学毕业证成绩单
BU毕业证【微信95270640】购买（波士顿大学毕业证成绩单硕士学历）Q微信95270640代办BU学历认证留信网伪造波士顿大学学位证书精仿波士顿大学本科/硕士文凭证书补办波士顿大学 diplomaoffer,Transcript购买波士顿大学毕业证成绩单购买BU假毕业证学位证书购买伪造波士顿大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
专业为留学生办理波士顿大学波士顿大学毕业证offer【100%存档可查】留学全套申请材料办理。本公司承诺所有毕业证成绩单成品全部按照学校原版工艺对照一比一制作和学校一样的羊皮纸张保证您证书的质量！
如果你回国在学历认证方面有以下难题请联系我们我们将竭诚为你解决认证瓶颈
1所有材料真实但资料不全无法提供完全齐整的原件。【如：成绩单丶毕业证丶回国证明等材料中有遗失的。】
2获得真实的国外最终学历学位但国外本科学历就读经历存在问题或缺陷。【如：国外本科是教育部不承认的或者是联合办学项目教育部没有备案的或者外本科没有正常毕业的。】
3学分转移联合办学等情况复杂不知道怎么整理材料的。时间紧迫自己不清楚递交流程的。
如果你是以上情况之一请联系我们我们将在第一时间内给你免费咨询相关信息。我们将帮助你整理认证所需的各种材料.帮你解决国外学历认证难题。
国外波士顿大学波士顿大学毕业证offer办理方法：
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2开始安排制作波士顿大学毕业证成绩单电子图；
3波士顿大学毕业证成绩单电子版做好以后发送给您确认；
4波士顿大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
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Art of Feature Engineering for Data Science with Nabeel Sarwar
- 1. Art of Feature Engineering For Data Science Nabeel Sarwar Machine Learning Engineer at Comcast Spark Summit Europe - October 26th, 2017
- 2. What is Comcast NBCUniversal? • Much like Ireland’s Sky • Provide Broadband Services in the United States - Internet - Video - Media - Mobile • ~25 million customers on combination of broadband services • Use Case: machine learning to optimize customer experience - While complying with all applicable privacy policies and laws
- 3. XFINITY TV XFINITY Internet XFINITY Voice XFINITY Home ® Digital & OtherOther *Minority interestand/or non-controlling interest. Slide is notcomprehensiveofall ComcastNBCUniversal assets Updated:December 22,2015
- 4. Where in the process? Obtain Select Clean Transform & Enrich Define Model Selection Tuning & Eval Feedback Imagine Arrows Everywhere
- 5. Feature Engineering 101 • Cheat sheet to some of the terminology and best practices • Why is feature engineering necessary? • How and how much feature engineering? • Feature Engineering Process variations • Select • Clean • Normalize and Transform • Philosophies - Confirm a hypothesis - Explore trends - Middle ground approach
- 6. Features • ML algorithms need pieces of information • Each individual piece in a sample: feature • Encoded in all sort of different ways • Differentiate by use case - Insurance: Model of car - Imaging: Color of pixels - General Relativity (black hole detection): coordinates in space - Online Food Ordering: what you ate last • Differentiate by model - Sequencing: Last event states - Imaging: Channels of the pixels - Reinforcement Learning: Actions from state to state • Differentiate by available data: - How do you partition? - Feature selection
- 7. Example: Titanic Survival Prediction • Who survives on the titanic? • Select the features • Prioritize who survives - Age - Role on ship - Speaking tongue of passenger • Pick Decision Tree • Bucket Age • Keep role as is • Encode speaking tongue into binary variable whether they speak English or not
- 8. How to actually do it
- 9. Categorical Variables • Discrete (but not necessarily disjoint) levels • Can often seem numeric • Encoding methods - Leave as is (but encode into some number - StringIndexer) - One Hot Encoding - Binary Encoding • Text: Tokenizing & Word2Vec • Decision Trees and variants can often do worse with one hot encoding
- 10. Numerical Variables • Can be continuous or discrete • Can become categorical through binning and bucketting • To scale or not variables - Some variables might be weighted higher than others (KNN?) - Per mini-batch in neural network training - Normalize or max-min scaling • Always determine relevant stats: - Mean - Variance - Kurtosis - Mode - Median • Specifically for words: term frequency-inverse documentfrequency (tf-idf)
- 11. Cleaning Data • Correcting for input mistakes or known mistakes in the data (normalization) - If predicting text: Knowing the last few sequence was Iceland instead of ice land • Transforming missing data and outliers - Missing Data -> Imputation - Mean - Mode - Matrix Factorization (A = LDU) • Converting data into known formats for rest of pipeline - Can be very simple: Mapping categories to different numbers - Or very difficult: Cleaning irregularly sampled data
- 12. Selecting Features • Domain Knowledge • Statistically correlate features - Weights - Gini - Correlation - Information Gain - Information Criteria (AIC, BIC, Bayes Factors) • Global Gains vs Local Gains • Throw all the data and then down sample • Throw in little data and then increase until acceptable tolerance • Noisy or dirty data • Some models prefer no intra feature other interactions - Ordinary Least Squares and some exponential families
- 13. Transformations and Enrichment • Build Features from Features - Add - Subtract (the mean) - Multiply - Ratios - Polynomials (kernel trick in SVMs) - Rational Differences - Logarithms, Exponentials, Sigmoids - Fourier or Laplace Transforms • Encode categorical variables • Scaling numerical variables • Look up tables to bring in additional features • Transforming dates - Year, month, day, hour, minute, second - Holidays and Special Events val encoder = new OneHotEncoder() .setInputCol(”categories") .setOutputCol(”oneHot") val encodedDF = encoder.transform(categoryDF)
- 14. Dimension Reduction • Curse of Dimensionality - Unhealthy prunes - Distances look the same - Slow training • Optimization in production • Reduce number of dimensions by learning the most important “directions” or combination of features - Finding the right basis (eigen) vectors • Principal Component Analysis - Singular Value Decomposition on Covariance Matrix • Factor Analysis - Expectation-Maximization to find basis vectors • Non-negative Matrix Factorization - Poisson-gamma factorization • Just remove features or transform better!
- 15. A Harder Use Case: Star-Galaxy Separation • Stars and galaxies look like little points in space, hard to differentiate on images • Can read temperatures • Know that Stars are one-dimensional based on temperature • Galaxies are composed of various stars • Bin the temperatures • Logarithm of temperatures • Form Features from the combination of differences • Logarithms on same magnitudes - No need to scale
- 18. Philosophy • Explore: - Not sure what exactly is there - Try out different combinations until you see trends - Select all and throw out as needed • Confirmation: - Have some hypothesis on how data is supposed to behave - Statistical tests - Add Feature if it passes your confirmation test • Hybrid: - Data sets often have a combination of features that fit both philosophies
- 19. New Horizons • Deep Learning: Feature Engineering -> Architecture building - Still need good data and good features • Meta Features - Models building features for other models • Automation! - Combinatorial complexity