This document summarizes Josh Patterson's work on parallel machine learning algorithms. It discusses his past publications and work on routing algorithms and metaheuristics. It then outlines his work developing parallel versions of algorithms like linear regression, logistic regression, and neural networks using Hadoop and YARN. It presents performance results showing these parallel algorithms can achieve close to linear speedup. It also discusses techniques used like vector caching and unit testing frameworks. Finally, it discusses future work on algorithms like Adagrad and parallel quasi-Newton methods.
Josh Patterson, Principal at Patterson Consulting: Introduction to Parallel Iterative Machine Learning Algorithms on Hadoop’s NextGeneration YARN Framework
Daniel Shank, Data Scientist, Talla at MLconf SF 2016MLconf
Neural Turing Machines: Perils and Promise: Daniel Shank is a Senior Data Scientist at Talla, a company developing a platform for intelligent information discovery and delivery. His focus is on developing machine learning techniques to handle various business automation tasks, such as scheduling, polls, expert identification, as well as doing work on NLP. Before joining Talla as the company’s first employee in 2015, Daniel worked with TechStars Boston and did consulting work for ThriveHive, a small business focused marketing company in Boston. He studied economics at the University of Chicago.
Distributed implementation of a lstm on spark and tensorflowEmanuel Di Nardo
Academic project based on developing a LSTM distributing it on Spark and using Tensorflow for numerical operations.
Source code: https://github.com/EmanuelOverflow/LSTM-TensorSpark
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...MLconf
Understanding Deep Learning for Big Data: The complexity and scale of big data impose tremendous challenges for their analysis. Yet, big data also offer us great opportunities. Some nonlinear phenomena, features or relations, which are not clear or cannot be inferred reliably from small and medium data, now become clear and can be learned robustly from big data. Typically, the form of the nonlinearity is unknown to us, and needs to be learned from data as well. Being able to harness the nonlinear structures from big data could allow us to tackle problems which are impossible before or obtain results which are far better than previous state-of-the-arts.
Nowadays, deep neural networks are the methods of choice when it comes to large scale nonlinear learning problems. What makes deep neural networks work? Is there any general principle for tackling high dimensional nonlinear problems which we can learn from deep neural works? Can we design competitive or better alternatives based on such knowledge? To make progress in these questions, my machine learning group performed both theoretical and experimental analysis on existing and new deep learning architectures, and investigate three crucial aspects on the usefulness of the fully connected layers, the advantage of the feature learning process, and the importance of the compositional structures. Our results point to some promising directions for future research, and provide guideline for building new deep learning models.
Web spam classification using supervised artificial neural network algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
Josh Patterson, Principal at Patterson Consulting: Introduction to Parallel Iterative Machine Learning Algorithms on Hadoop’s NextGeneration YARN Framework
Daniel Shank, Data Scientist, Talla at MLconf SF 2016MLconf
Neural Turing Machines: Perils and Promise: Daniel Shank is a Senior Data Scientist at Talla, a company developing a platform for intelligent information discovery and delivery. His focus is on developing machine learning techniques to handle various business automation tasks, such as scheduling, polls, expert identification, as well as doing work on NLP. Before joining Talla as the company’s first employee in 2015, Daniel worked with TechStars Boston and did consulting work for ThriveHive, a small business focused marketing company in Boston. He studied economics at the University of Chicago.
Distributed implementation of a lstm on spark and tensorflowEmanuel Di Nardo
Academic project based on developing a LSTM distributing it on Spark and using Tensorflow for numerical operations.
Source code: https://github.com/EmanuelOverflow/LSTM-TensorSpark
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...MLconf
Understanding Deep Learning for Big Data: The complexity and scale of big data impose tremendous challenges for their analysis. Yet, big data also offer us great opportunities. Some nonlinear phenomena, features or relations, which are not clear or cannot be inferred reliably from small and medium data, now become clear and can be learned robustly from big data. Typically, the form of the nonlinearity is unknown to us, and needs to be learned from data as well. Being able to harness the nonlinear structures from big data could allow us to tackle problems which are impossible before or obtain results which are far better than previous state-of-the-arts.
Nowadays, deep neural networks are the methods of choice when it comes to large scale nonlinear learning problems. What makes deep neural networks work? Is there any general principle for tackling high dimensional nonlinear problems which we can learn from deep neural works? Can we design competitive or better alternatives based on such knowledge? To make progress in these questions, my machine learning group performed both theoretical and experimental analysis on existing and new deep learning architectures, and investigate three crucial aspects on the usefulness of the fully connected layers, the advantage of the feature learning process, and the importance of the compositional structures. Our results point to some promising directions for future research, and provide guideline for building new deep learning models.
Web spam classification using supervised artificial neural network algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
Teaching Recurrent Neural Networks using Tensorflow (May 2016)Rajiv Shah
This talk will provide an introduction to recurrent neural networks (RNNs). RNNs are designed to model sequential information and have provided impressive results for a variety of problems, such as speech recognition, language modeling, translation and image captioning. This talk walks through code in tensorflow for modeling a sine wave, performing basic addition, and generating handwriting. This was for a Chicago Tensorflow meetup in May 2016.
Melanie Warrick, Deep Learning Engineer, Skymind.io at MLconf SF - 11/13/15MLconf
Attention Neural Net Model Fundamentals: Neural networks have regained popularity over the last decade because they are demonstrating real world value in different applications (e.g. targeted advertising, recommender engines, Siri, self driving cars, facial recognition). Several model types are currently explored in the field with recurrent neural networks (RNN) and convolution neural networks (CNN) taking the top focus. The attention model, a recently developed RNN variant, has started to play a larger role in both natural language processing and image analysis research.
This talk will cover the fundamentals of the attention model structure and how its applied to visual and speech analysis. I will provide an overview of the model functionality and math including a high-level differentiation between soft and hard types. The goal is to give you enough of an understanding of what the model is, how it works and where to apply it.
An Introduction to TensorFlow architectureMani Goswami
Introduces you to the internals of TensorFlow and deep dives into distributed version of TensorFlow. Refer to https://github.com/manigoswami/tensorflow-examples for examples.
Simulation of Single and Multilayer of Artificial Neural Network using Verilogijsrd.com
Artificial neural network play an important role in VLSI circuit to find and diagnosis multiple fault in digital circuit. In this paper, the example of single layer and multi-layer neural network had been discussed secondly implement those structure by using verilog code and same idea must be implement in mat lab for getting number of iteration and verilog code gives us time taken to adjust the weight when error become almost equal to zero. The purposed aim at reducing resource requirement, without much compromises on the speed that neural network can be realized on single chip at lower cost.
Time-series forecasting of indoor temperature using pre-trained Deep Neural N...Francisco Zamora-Martinez
Artificial neural networks have proved to be good at time-series forecasting
problems, being widely studied at literature. Traditionally, shallow
architectures were used due to convergence problems when dealing with deep
models. Recent research findings enable deep architectures training, opening a
new interesting research area called deep learning. This paper presents a study
of deep learning techniques applied to time-series forecasting in a real indoor
temperature forecasting task, studying performance due to different
hyper-parameter configurations. When using deep models, better generalization
performance at test set and an over-fitting reduction has been observed.
A Threshold Logic Unit (TLU) is a mathematical function conceived as a crude model, or abstraction of biological neurons. Threshold logic units are the constitutive units in an artificial neural network. In this paper a positive clock-edge triggered T flip-flop is designed using Perceptron Learning Algorithm, which is a basic design algorithm of threshold logic units. Then this T flip-flop is used to design a two-bit up-counter that goes through the states 0, 1, 2, 3, 0, 1… Ultimately, the goal is to show how to design simple logic units based on threshold logic based perceptron concepts.
April 2016 HUG: The latest of Apache Hadoop YARN and running your docker apps...Yahoo Developer Network
Apache Hadoop YARN is a modern resource-management platform that handles resource scheduling, isolation and multi-tenancy for a variety of data processing engines that can co-exist and share a single data-center in a cost-effective manner.
In the first half of the talk, we are going to give a brief look into some of the big efforts cooking in the Apache Hadoop YARN community.
We will then dig deeper into one of the efforts - supporting Docker runtime in YARN. Docker is an application container engine that enables developers and sysadmins to build, deploy and run containerized applications. In this half, we'll discuss container runtimes in YARN, with a focus on using the DockerContainerRuntime to run various docker applications under YARN. Support for container runtimes (including the docker container runtime) was recently added to the Linux Container Executor (YARN-3611 and its sub-tasks). We’ll walk through various aspects of running docker containers under YARN - resource isolation, some security aspects (for example container capabilities, privileged containers, user namespaces) and other work in progress features like image localization and support for different networking modes.
Speakers:
Vinod Kumar Vavilapalli is the Hadoop YARN and MapReduce guy at Hortonworks. He is a long term Hadoop contributor at Apache, Hadoop committer and a member of the Apache Hadoop PMC. He has a Bachelors degree from Indian Institute of Technology Roorkee in Computer Science and Engineering. He has been working on Hadoop for nearly 9 years and he still has fun doing it. Straight out of college, he joined the Hadoop team at Yahoo! Bangalore, before Hortonworks happened. He is passionate about using computers to change the world for better, bit by bit.
Sidharta Seethana is a software engineer at Hortonworks. He works on the YARN team, focussing on bringing new kinds of workloads to YARN. Prior to joining Hortonworks, Sidharta spent 10 years at Yahoo! Inc., working on a variety of large scale distributed systems for core platforms/web services, search and marketplace properties, developer network and personalization.
April 2016 HUG: CaffeOnSpark: Distributed Deep Learning on Spark ClustersYahoo Developer Network
Deep learning is a critical capability for gaining intelligence from datasets. Many existing frameworks require a separated cluster for deep learning, and multiple programs have to be created for a typical machine learning pipeline. The separated clusters require large datasets to be transferred between clusters, and introduce unwanted system complexity and latency for end-to-end learning.
Yahoo introduced CaffeOnSpark to alleviate those pain points and bring deep learning onto Hadoop and Spark clusters. By combining salient features from deep learning framework Caffe and big-data framework Apache Spark, CaffeOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. The framework is complementary to non-deep learning libraries MLlib and Spark SQL, and its data-frame style API provides Spark applications with an easy mechanism to invoke deep learning over distributed datasets. Its server-to-server direct communication (Ethernet or InfiniBand) achieves faster learning and eliminates scalability bottleneck.
Recently, we have released CaffeOnSpark at github.com/yahoo/CaffeOnSpark under Apache 2.0 License. In this talk, we will provide a technical overview of CaffeOnSpark, its API and deployment on a private cloud or public cloud (AWS EC2). A demo of IPython notebook will also be given to demonstrate how CaffeOnSpark will work with other Spark packages (ex. MLlib).
Speakers:
Andy Feng is a VP Architecture at Yahoo, leading the architecture and design of big data and machine learning initiatives. He has architected major platforms for personalization, ads serving, NoSQL, and cloud infrastructure.
Jun Shi is a Principal Engineer at Yahoo who specializes in machine learning platforms and large-scale machine learning algorithms. Prior to Yahoo, he was designing wireless communication chips at Broadcom, Qualcomm and Intel.
Mridul Jain is Senior Principal at Yahoo, focusing on machine learning and big data platforms (especially realtime processing). He has worked on trending algorithms for search, unstructured content extraction, realtime processing for central monitoring platform, and is the co-author of Pig on Storm.
Teaching Recurrent Neural Networks using Tensorflow (May 2016)Rajiv Shah
This talk will provide an introduction to recurrent neural networks (RNNs). RNNs are designed to model sequential information and have provided impressive results for a variety of problems, such as speech recognition, language modeling, translation and image captioning. This talk walks through code in tensorflow for modeling a sine wave, performing basic addition, and generating handwriting. This was for a Chicago Tensorflow meetup in May 2016.
Melanie Warrick, Deep Learning Engineer, Skymind.io at MLconf SF - 11/13/15MLconf
Attention Neural Net Model Fundamentals: Neural networks have regained popularity over the last decade because they are demonstrating real world value in different applications (e.g. targeted advertising, recommender engines, Siri, self driving cars, facial recognition). Several model types are currently explored in the field with recurrent neural networks (RNN) and convolution neural networks (CNN) taking the top focus. The attention model, a recently developed RNN variant, has started to play a larger role in both natural language processing and image analysis research.
This talk will cover the fundamentals of the attention model structure and how its applied to visual and speech analysis. I will provide an overview of the model functionality and math including a high-level differentiation between soft and hard types. The goal is to give you enough of an understanding of what the model is, how it works and where to apply it.
An Introduction to TensorFlow architectureMani Goswami
Introduces you to the internals of TensorFlow and deep dives into distributed version of TensorFlow. Refer to https://github.com/manigoswami/tensorflow-examples for examples.
Simulation of Single and Multilayer of Artificial Neural Network using Verilogijsrd.com
Artificial neural network play an important role in VLSI circuit to find and diagnosis multiple fault in digital circuit. In this paper, the example of single layer and multi-layer neural network had been discussed secondly implement those structure by using verilog code and same idea must be implement in mat lab for getting number of iteration and verilog code gives us time taken to adjust the weight when error become almost equal to zero. The purposed aim at reducing resource requirement, without much compromises on the speed that neural network can be realized on single chip at lower cost.
Time-series forecasting of indoor temperature using pre-trained Deep Neural N...Francisco Zamora-Martinez
Artificial neural networks have proved to be good at time-series forecasting
problems, being widely studied at literature. Traditionally, shallow
architectures were used due to convergence problems when dealing with deep
models. Recent research findings enable deep architectures training, opening a
new interesting research area called deep learning. This paper presents a study
of deep learning techniques applied to time-series forecasting in a real indoor
temperature forecasting task, studying performance due to different
hyper-parameter configurations. When using deep models, better generalization
performance at test set and an over-fitting reduction has been observed.
A Threshold Logic Unit (TLU) is a mathematical function conceived as a crude model, or abstraction of biological neurons. Threshold logic units are the constitutive units in an artificial neural network. In this paper a positive clock-edge triggered T flip-flop is designed using Perceptron Learning Algorithm, which is a basic design algorithm of threshold logic units. Then this T flip-flop is used to design a two-bit up-counter that goes through the states 0, 1, 2, 3, 0, 1… Ultimately, the goal is to show how to design simple logic units based on threshold logic based perceptron concepts.
April 2016 HUG: The latest of Apache Hadoop YARN and running your docker apps...Yahoo Developer Network
Apache Hadoop YARN is a modern resource-management platform that handles resource scheduling, isolation and multi-tenancy for a variety of data processing engines that can co-exist and share a single data-center in a cost-effective manner.
In the first half of the talk, we are going to give a brief look into some of the big efforts cooking in the Apache Hadoop YARN community.
We will then dig deeper into one of the efforts - supporting Docker runtime in YARN. Docker is an application container engine that enables developers and sysadmins to build, deploy and run containerized applications. In this half, we'll discuss container runtimes in YARN, with a focus on using the DockerContainerRuntime to run various docker applications under YARN. Support for container runtimes (including the docker container runtime) was recently added to the Linux Container Executor (YARN-3611 and its sub-tasks). We’ll walk through various aspects of running docker containers under YARN - resource isolation, some security aspects (for example container capabilities, privileged containers, user namespaces) and other work in progress features like image localization and support for different networking modes.
Speakers:
Vinod Kumar Vavilapalli is the Hadoop YARN and MapReduce guy at Hortonworks. He is a long term Hadoop contributor at Apache, Hadoop committer and a member of the Apache Hadoop PMC. He has a Bachelors degree from Indian Institute of Technology Roorkee in Computer Science and Engineering. He has been working on Hadoop for nearly 9 years and he still has fun doing it. Straight out of college, he joined the Hadoop team at Yahoo! Bangalore, before Hortonworks happened. He is passionate about using computers to change the world for better, bit by bit.
Sidharta Seethana is a software engineer at Hortonworks. He works on the YARN team, focussing on bringing new kinds of workloads to YARN. Prior to joining Hortonworks, Sidharta spent 10 years at Yahoo! Inc., working on a variety of large scale distributed systems for core platforms/web services, search and marketplace properties, developer network and personalization.
April 2016 HUG: CaffeOnSpark: Distributed Deep Learning on Spark ClustersYahoo Developer Network
Deep learning is a critical capability for gaining intelligence from datasets. Many existing frameworks require a separated cluster for deep learning, and multiple programs have to be created for a typical machine learning pipeline. The separated clusters require large datasets to be transferred between clusters, and introduce unwanted system complexity and latency for end-to-end learning.
Yahoo introduced CaffeOnSpark to alleviate those pain points and bring deep learning onto Hadoop and Spark clusters. By combining salient features from deep learning framework Caffe and big-data framework Apache Spark, CaffeOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. The framework is complementary to non-deep learning libraries MLlib and Spark SQL, and its data-frame style API provides Spark applications with an easy mechanism to invoke deep learning over distributed datasets. Its server-to-server direct communication (Ethernet or InfiniBand) achieves faster learning and eliminates scalability bottleneck.
Recently, we have released CaffeOnSpark at github.com/yahoo/CaffeOnSpark under Apache 2.0 License. In this talk, we will provide a technical overview of CaffeOnSpark, its API and deployment on a private cloud or public cloud (AWS EC2). A demo of IPython notebook will also be given to demonstrate how CaffeOnSpark will work with other Spark packages (ex. MLlib).
Speakers:
Andy Feng is a VP Architecture at Yahoo, leading the architecture and design of big data and machine learning initiatives. He has architected major platforms for personalization, ads serving, NoSQL, and cloud infrastructure.
Jun Shi is a Principal Engineer at Yahoo who specializes in machine learning platforms and large-scale machine learning algorithms. Prior to Yahoo, he was designing wireless communication chips at Broadcom, Qualcomm and Intel.
Mridul Jain is Senior Principal at Yahoo, focusing on machine learning and big data platforms (especially realtime processing). He has worked on trending algorithms for search, unstructured content extraction, realtime processing for central monitoring platform, and is the co-author of Pig on Storm.
Neural Networks, Spark MLlib, Deep LearningAsim Jalis
What are neural networks? How to use the neural networks algorithm in Apache Spark MLlib? What is Deep Learning? Presented at Data Science Meetup at Galvanize on 2/17/2016.
For code see IPython/Jupyter/Toree notebook at http://nbviewer.jupyter.org/gist/asimjalis/4f911882a1ab963859ce
A TALE of DATA PATTERN DISCOVERY IN PARALLELJenny Liu
In the era of IoTs and A.I., distributed and parallel computing is embracing big data driven and algorithm focused applications and services. With rapid progress and development on parallel frameworks, algorithms and accelerated computing capacities, it still remains challenging on deliver an efficient and scalable data analysis solution. This talk shares a research experience on data pattern discovery in domain applications. In particular, the research scrutinizes key factors in analysis workflow design and data parallelism improvement on cloud.
A Tale of Data Pattern Discovery in ParallelJenny Liu
In the era of IoTs and A.I., distributed and parallel computing is embracing big data driven and algorithm focused applications and services. With rapid progress and development on parallel frameworks, algorithms and accelerated computing capacities, it still remains challenging on deliver an efficient and scalable data analysis solution. This talk shares a research experience on data pattern discovery in domain applications. In particular, the research scrutinizes key factors in analysis workflow design and data parallelism improvement on cloud.
From Simulation to Online Gaming: the need for adaptive solutions Gabriele D'Angelo
In many fields such as distributed simulation and online gaming the missing piece is adaptivity. There is a strong need for dynamic and adaptive solutions that can improve performances and react to problems.
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)?
This presentation describes a intelligent IT monitoring solution that uses Nagios as source of information, Esper as the CEP engine and a PCA algorithm.
Towards neuralprocessingofgeneralpurposeapproximateprogramsParidha Saxena
Did validation of one of the machine learning algorithms of neural networks,and compared the results for its implementation on hardware (FPGA) using xilinx, with that of a sequential code execution(using FANN).
Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...inside-BigData.com
In this deck from FOSDEM 2020, Frank McQuillan from Pivotal presents: Efficient Model Selection for Deep Neural Networks on Massively Parallel Processing Databases.
"In this session we will present an efficient way to train many deep learning model configurations at the same time with Greenplum, a free and open source massively parallel database based on PostgreSQL. The implementation involves distributing data to the workers that have GPUs available and hopping model state between those workers, without sacrificing reproducibility or accuracy. Then we apply optimization algorithms to generate and prune the set of model configurations to try.
Deep neural networks are revolutionizing many machine learning applications, but hundreds of trials may be needed to generate a good model architecture and associated hyperparameters. This is the challenge of model selection. It is time consuming and expensive, especially if you are only training one model at a time.
Massively parallel processing databases can have hundreds of workers, so can you use this parallel compute architecture to address the challenge of model selection for deep nets, in order to make it faster and cheaper?
It’s possible!
We will demonstrate results from this project using a version of Hyperband, which is a well known hyperparameter optimization algorithm, and the deep learning frameworks Keras and TensorFlow, all running on Greenplum database using Apache MADlib. Other topics will include architecture, scalability results and bright opportunities for the future."
Watch the video: https://wp.me/p3RLHQ-lsQ
Learn more: https://fosdem.org/2020/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This work is proposed the feed forward neural network with symmetric table addition method to design the
neuron synapses algorithm of the sine function approximations, and according to the Taylor series
expansion. Matlab code and LabVIEW are used to build and create the neural network, which has been
designed and trained database set to improve its performance, and gets the best a global convergence with
small value of MSE errors and 97.22% accuracy.
Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Sched...IJECEIAES
Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.
Building scalable, highly-available applications that perform well is not an easy task. These features cannot be simply “bolted” onto an existing application – they have to be architected into it. Unfortunately, the things we need to do to achieve them are often in conflict with each other, and finding the right balance is crucial. In this session we will discuss why scaling web applications is difficult and will look at some of solutions we have come up with in the past to deal with the issues involved. We will then look at how in-memory data grids can make our jobs easier by providing a solid architectural foundation to build our applications on top of. If you are new to in-memory data grids, you are guaranteed to leave the presentation eager to learn more. However, even if you are already using one you will likely walk out with a few ideas on how to improve performance and scalability of your applications.
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Presentation on BornoNet Research Paper and Python BasicsShibbir Ahmed
The slides are of a presentation on BornoNet Research Paper and Python basics done by our team recently in our Mobile and Telecommunication course of undergraduate studies.
Abstract—In evolutionary high-level synthesis, design solutions
have to be evaluated to extract information about some
figures of merit (such as performance, area, etc.) and to allow
the genetic algorithm to evolve and converge to Pareto-optimal
solutions. Since the execution time of such evaluations increases
with the complexity of the specification, this could lead to
unacceptable execution time of the overall methodology. This
paper presents a model to exploit fitness inheritance in a multiobjective
optimization algorithm (i.e. NSGA-II [1]) by substituting
the expensive real evaluations with an estimation based
on neighbors in an hypothetical design space. The estimations
are based on a measure of distance between individuals and
a weighted average on fitnesses of closer ones. The results
shows that the Pareto-optimal set obtained by applying the
proposed model good approximates the set obtained without
fitness inheritance and overall execution time is reduced more
than 25% in average.
by Vikram Madan, Sr. Product Manager, AWS Deep Learning
In this workshop, we will provide cover deep learning fundamentals and focus on the powerful and scalable Apache MXNet open source deep learning framework. At the end of this tutorial you’ll be able to train your own deep neural network and fine tune existing state of the art models for image and object recognition. We’ll also deep dive on setting up your deep learning infrastructure on AWS and model deployment on AWS Lambda.
Similar to MLConf 2013: Metronome and Parallel Iterative Algorithms on YARN (20)
Modeling Electronic Health Records with Recurrent Neural NetworksJosh Patterson
Time series data is increasingly ubiquitous. This trend is especially obvious in health and wellness, with both the adoption of electronic health record (EHR) systems in hospitals and clinics and the proliferation of wearable sensors. In 2009, intensive care units in the United States treated nearly 55,000 patients per day, generating digital-health databases containing millions of individual measurements, most of those forming time series. In the first quarter of 2015 alone, over 11 million health-related wearables were shipped by vendors. Recording hundreds of measurements per day per user, these devices are fueling a health time series data explosion. As a result, we will need ever more sophisticated tools to unlock the true value of this data to improve the lives of patients worldwide.
Deep learning, specifically with recurrent neural networks (RNNs), has emerged as a central tool in a variety of complex temporal-modeling problems, such as speech recognition. However, RNNs are also among the most challenging models to work with, particularly outside the domains where they are widely applied. Josh Patterson, David Kale, and Zachary Lipton bring the open source deep learning library DL4J to bear on the challenge of analyzing clinical time series using RNNs. DL4J provides a reliable, efficient implementation of many deep learning models embedded within an enterprise-ready open source data ecosystem (e.g., Hadoop and Spark), making it well suited to complex clinical data. Josh, David, and Zachary offer an overview of deep learning and RNNs and explain how they are implemented in DL4J. They then demonstrate a workflow example that uses a pipeline based on DL4J and Canova to prepare publicly available clinical data from PhysioNet and apply the DL4J RNN.
Building Deep Learning Workflows with DL4JJosh Patterson
In this session we will take a look at a practical review of what is deep learning and introduce DL4J. We’ll look at how it supports deep learning in the enterprise on the JVM. We’ll discuss the architecture of DL4J’s scale-out parallelization on Hadoop and Spark in support of modern machine learning workflows. We’ll conclude with a workflow example from the command line interface that shows the vectorization pipeline in Canova producing vectors for DL4J’s command line interface to build deep learning models easily.
Georgia Tech cse6242 - Intro to Deep Learning and DL4JJosh Patterson
Introduction to deep learning and DL4J - http://deeplearning4j.org/ - a guest lecture by Josh Patterson at Georgia Tech for the cse6242 graduate class.
Hadoop Summit 2014 - San Jose - Introduction to Deep Learning on HadoopJosh Patterson
As the data world undergoes its cambrian explosion phase our data tools need to become more advanced to keep pace. Deep Learning has emerged as a key tool in the non-linear arms race of machine learning. In this session we will take a look at how we parallelize Deep Belief Networks in Deep Learning on Hadoop’s next generation YARN framework with Iterative Reduce. We’ll also look at some real world examples of processing data with Deep Learning such as image classification and natural language processing.
Have you ever been recommended a friend on Facebook? Or an item you might be interested in on Amazon? If so then you’ve benefitted from the value of recommendation systems. Recommendation systems apply knowledge discovery techniques to the problem of making recommendations that are personalized for each user. Recommendation systems are one way we can use algorithms to help us sort through the masses of information to find the “good stuff” in a very personalized way.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
5. 5
Machine Learning and Optimization
Direct Methods
Normal Equation
Iterative Methods
Newton’s Method
Quasi-Newton
Gradient Descent
Heuristics
AntNet
PSO
Genetic Algorithms
6. Linear Regression
In linear regression, data is
modeled using linear predictor
functions
unknown model parameters are
estimated from the data.
We use optimization techniques
like Stochastic Gradient Descent to
find the coeffcients in the model
Y = (1*x0) + (c1*x1) + … + (cN*xN)
8. 8
Stochastic Gradient Descent
Training
Training Data
Simple gradient descent procedure
Loss functions needs to be convex
(with exceptions)
Linear Regression
SGD
Loss Function: squared error of
prediction
Prediction: linear combination of
coefficients and input variables
Model
9. 9
Mahout’s SGD
Currently Single Process
Multi-threaded parallel, but not cluster parallel
Runs locally, not deployed to the cluster
Tied to logistic regression implementation
10. 10
Distributed Learning Strategies
McDonald, 2010
Distributed Training Strategies for the Structured
Perceptron
Langford, 2007
Vowpal Wabbit
Jeff Dean’s Work on Parallel SGD
DownPour SGD
12. 12
YARN
Yet Another Resource Negotiator
Framework for scheduling
distributed applications
Allows for any type of parallel
application to run natively on
hadoop
MRv2 is now a distributed
application
Node
Manager
Container
App Mstr
Client
Resource
Manager
Node
Manager
Client
App Mstr
MapReduce Status
Job Submission
Node Status
Resource Request
Container
Node
Manager
Container
Container
14. 14
SGD: Serial vs Parallel
Split 1
Split 2
Split 3
Training Data
Worker 1
Partial
Model
Worker 2
…
Partial Model
Master
Model
Global Model
Worker N
Partial
Model
15. Parallel Iterative Algorithms on YARN
Based directly on work we did with Knitting Boar
Parallel logistic regression
And then added
Parallel linear regression
Parallel Neural Networks
Packaged in a new suite of parallel iterative algorithms
called Metronome
100% Java, ASF 2.0 Licensed, on github
16. Linear Regression Results
Total Processing Time
Linear Regression - Parallel vs Serial
200
150
100
Parallel Runs
Serial Runs
50
0
64
128
192
256
Megabytes Processed Total
320
18. Convergence Testing
Debugging parallel iterative algorithms during
testing is hard
Processes on different hosts are difficult to observe
Using the Unit Test framework IRUnit we can
simulate the IterativeReduce framework
We know the plumbing of message passing works
Allows us to focus on parallel algorithm design/testing
while still using standard debugging tools
19.
20. What are Neural Networks?
Inspired by nervous systems in biological
systems
Models layers of neurons in the brain
Can learn non-linear functions
Recently enjoying a surge in popularity
21. Multi-Layer Perceptron
First layer has input neurons
Last layer has output neurons
Each neuron in the layer
connected to all neurons in the
next layer
Neuron has activation
function, typically sigmoid /
logistic
Input to neuron is the sum of the
weight * input of connections
22. Backpropogation Learning
Calculates the gradient of the error of the network
regarding the network's modifiable weights
Intuition
Run forward pass of example through network
Compute activations and output
Iterating output layer back to input layer (backwards)
For each neuron in the layer
Compute node’s responsibility for error
Update weights on connections
23. Parallelizing Neural Networks
Dean, (NIPS, 2012)
First Steps: Focus on linear convex models, calculating
distributed gradient
Model Parallelism must be combined with distributed
optimization that leverages data parallelization
simultaneously process distinct training examples in
each of the many model replicas
periodically combine their results to optimize our
objective function
Single pass frameworks such as MapReduce “ill-suited”
24. Costs of Neural Network Training
Connections count explodes quickly as neurons and layers increase
Example: {784, 450, 10} network has 357,300 connections
Need fast iterative framework
Example: 30 sec MR setup cost: 10k Epochs: 30s x 10,000 == 300,000 seconds of setup time
5,000 minutes or 83 hours
3 ways to speed up training
Subdivide dataset between works (data parallelism)
Max transfer rate of disks and Vector caching to max data throughput
Minimize inter-epoch setup times with proper iterative framework
25. Vector In-Memory Caching
Since we make lots of passes over same dataset
In memory caching makes sense here
Once a record is vectorized it is cached in memory
on the worker node
Speedup (single pass, “no cache” vs “cached”):
~12x
26. Neural Networks Parallelization Speedup
Training Speedup Factor (Multiple)
6.00
5.00
4.00
UCI Iris
3.00
UCI Lenses
UCI Wine
2.00
UCI Dermatology
NIST Handwriting Downsample
1.00
1
2
3
4
Number of Parallel Processing Units
5
27.
28. Lessons Learned
Linear scale continues to be achieved with
parameter averaging variations
Tuning is critical
Need to be good at selecting a learning rate
31. Unit Testing and IRUnit
Simulates the IterativeReduce parallel framework
Uses the same app.properties file that YARN applications do
Examples
https://github.com/jpatanooga/Metronome/blob/master/src/test/jav
a/tv/floe/metronome/linearregression/iterativereduce/TestSimulat
eLinearRegressionIterativeReduce.java
https://github.com/jpatanooga/KnittingBoar/blob/master/src/test/j
ava/com/cloudera/knittingboar/sgd/iterativereduce/TestKnittingB
oar_IRUnitSim.java
Editor's Notes
Talk about how you normally would use the Normal equation, notes from Andrew Ng
“Unlikely optimization algorithms such as stochastic gradient descent show amazing performance for large-scale problems.“Bottou, 2010SGD has been around for decadesyet recently Langford, Bottou, others have shown impressive speed increasesSGD has been shown to train multiple orders of magnitude faster than batch style learnerswith no loss on model accuracy
“Unlikely optimization algorithms such as stochastic gradient descent show amazing performance for large-scale problems.“Bottou, 2010SGD has been around for decadesyet recently Langford, Bottou, others have shown impressive speed increasesSGD has been shown to train multiple orders of magnitude faster than batch style learnerswith no loss on model accuracy
The most important additions in Mahout’s SGD are:confidence weighted learning rates per termevolutionary tuning of hyper-parametersmixed ranking and regressiongrouped AUCImplications of it being local is that you are limited to the compute capacity of the local machine as opposed to even a single machine on the cluster.
Bottou similar to Xu2010 in the 2010 paper
Benefits of data flow: runtime can decide where to run tasks and can automatically recover from failuresAcyclic data flow is a powerful abstraction, but is not efficient for applications that repeatedly reuse a working set of data:Iterative algorithms (many in machine learning)• No single programming model or framework can excel atevery problem; there are always tradeoffs between simplicity, expressivity, fault tolerance, performance, etc.
POLR: Parallel Online Logistic RegressionTalking points:wanted to start with a known tool to the hadoop community, with expected characteristicsMahout’s SGD is well known, and so we used that as a base point
3 major costs of BSP style computations:Max unit compute timeCost of global communicationCost of barrier sync at end of super step