Introduction to deep learning and DL4J - http://deeplearning4j.org/ - a guest lecture by Josh Patterson at Georgia Tech for the cse6242 graduate class.
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.
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.
David Kale and Ruben Fizsel from Skymind talk about deep learning for the JVM and enterprise using deeplearning4j (DL4J). Deep learning (nouveau neural nets) have sparked a renaissance in empirical machine learning with breakthroughs in computer vision, speech recognition, and natural language processing. However, many popular deep learning frameworks are targeted to researchers and poorly suited to enterprise settings that use Java-centric big data ecosystems. DL4J bridges the gap, bringing high performance numerical linear algebra libraries and state-of-the-art deep learning functionality to the JVM.
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.
DeepLearning4J and Spark: Successes and Challenges - François GarillotSteve Moore
At the recent Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered this lightning talk to a sold-out crowd.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
Deep Learning on Apache® Spark™ : Workflows and Best PracticesJen Aman
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark.
Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including:
* optimizing cluster setup;
* configuring the cluster;
* ingesting data; and
* monitoring long-running jobs.
We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters.
Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
This is an 1 hour presentation on Neural Networks, Deep Learning, Computer Vision, Recurrent Neural Network and Reinforcement Learning. The talks later have links on how to run Neural Networks on
Distributed Inference on Large Datasets Using Apache MXNet and Apache Spark ...Databricks
Deep Learning has become ubiquitous with abundance of data, commoditization of compute and storage. Pre-trained models are readily available for many use-cases. Distributed Inference has many applications such as pre-computing results offline, backfilling historic data with predictions from state-of-the-art models, etc.Inference on large scale datasets comes with many challenges prevalent in distributed data processing.
Attendees will learn how to efficiently run deep learning prediction on large data sets, leveraging Apache Spark and Apache MXNet (incubating).
In this session, we’ll cover core Deep Learning Concepts such as:
Types of Learning, a) Supervised Learning b) Unsupervised Learning c) Active Learning d) Reinforcement Learning
Supervised Learning types – classification, regression, Image classification
Types of Neural Networks – Feed forward Networks, CNNs, RNNs, GANs * Apache MXNet(Incubating) Deep Learning Framework. MXNet concepts ie., NDArray, Symbolic APIs and Module APIs. MXNet Gluon APIs * Distributed Inference using Apache MXNet and Apache Spark on Amazon EMR.
In this section, I will cover some of the use-cases of Distributed Inference, the challenges associated with running distributed Inference.
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & Deep Learning ...Databricks
We all know what they say – the bigger the data, the better. But when the data gets really big, how do you use it? This talk will cover three of the most popular deep learning frameworks: TensorFlow, Keras, and Deep Learning Pipelines, and when, where, and how to use them.
We’ll also discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data), as well as help you answer questions such as:
– As a developer how do I pick the right deep learning framework for me?
– Do I want to develop my own model or should I employ an existing one
– How do I strike a trade-off between productivity and control through low-level APIs?
In this session, we will show you how easy it is to build an image classifier with Tensorflow, Keras, and Deep Learning Pipelines in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you, and perhaps with a better sense for how to fool an image classifier!
What is Deep Learning
Rise of Deep Learning
Phases of Deep Learning - Training and Inference
AI & Limitations of Deep Learning
Apache MXNet History, Apache MXNet concepts
How to use Apache MXNet and Spark together for Distributed Inference.
Snorkel: Dark Data and Machine Learning with Christopher RéJen Aman
Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. However, building such applications is challenging: real world data is expressed in natural language, images, or other “dark” data formats which are fraught with imprecision and ambiguity and so are difficult for machines to understand. This talk will describe Snorkel, whose goal is to make routine Dark Data and other prediction tasks dramatically easier. At its core, Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets. In Snorkel, a user implicitly creates large training sets by writing simple programs that label data, instead of performing manual feature engineering or tedious hand-labeling of individual data items. We’ll provide a set of tutorials that will allow folks to write Snorkel applications that use Spark.
Snorkel is open source on github and available from Snorkel.Stanford.edu.
Apache Toree provides the interactive notebook for Spark/Scala. Toree is a IPython/Jupyter kernel. It lets you mix Spark/Scala code with markdown, execute the notebook, and publish it on the web.
Asim will talk about how to install and get started with Apache Toree, how to use it to develop Spark applications interactively in notebooks, and how to publish your notebooks.
Kaz Sato, Evangelist, Google at MLconf ATL 2016MLconf
Machine Intelligence at Google Scale: Tensor Flow and Cloud Machine Learning: The biggest challenge of Deep Learning technology is the scalability. As long as using single GPU server, you have to wait for hours or days to get the result of your work. This doesn’t scale for production service, so you need a Distributed Training on the cloud eventually. Google has been building infrastructure for training the large scale neural network on the cloud for years, and now started to share the technology with external developers. In this session, we will introduce new pre-trained ML services such as Cloud Vision API and Speech API that works without any training. Also, we will look how TensorFlow and Cloud Machine Learning will accelerate custom model training for 10x – 40x with Google’s distributed training infrastructure.
The term "machine learning" is increasingly bandied about in corporate settings and cocktail parties, but what is it, really? In this session we'll answer that question, providing an approachable overview of machine learning concepts, technologies, and use cases. We'll then take a deeper dive into machine learning topics such as supervised learning, unsupervised learning, and deep learning. We'll also survey various machine learning APIs and platforms. Technologies including Spring and Cloud Foundry will be leveraged in the demos. You'll be the hit of your next party when you're able to express the near-magical inner-workings of artificial neural networks!
Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep L...Simplilearn
Deep Learning covers all the essential Deep Learning frameworks that are necessary to build AI models. In this presentation, you will learn about the development of essential frameworks such as TensorFlow, Keras, PyTorch, Theano, etc. You will also understand the programming languages used to build the frameworks, the different companies that use these frameworks, the characteristics of these Deep Learning frameworks, and type of models that were built using these frameworks. Now, let us get started with understanding the different popular Deep Learning frameworks being used in industries.
Below are the different Deep Learning frameworks we'll be discussing in this presentation:
1. TensorFlow
2. Keras
3. PyTorch
4. Theano
5. Deep Learning 4 Java
6. Caffe
7. Chainer
8. Microsoft CNTK
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
David Kale and Ruben Fizsel from Skymind talk about deep learning for the JVM and enterprise using deeplearning4j (DL4J). Deep learning (nouveau neural nets) have sparked a renaissance in empirical machine learning with breakthroughs in computer vision, speech recognition, and natural language processing. However, many popular deep learning frameworks are targeted to researchers and poorly suited to enterprise settings that use Java-centric big data ecosystems. DL4J bridges the gap, bringing high performance numerical linear algebra libraries and state-of-the-art deep learning functionality to the JVM.
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.
DeepLearning4J and Spark: Successes and Challenges - François GarillotSteve Moore
At the recent Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered this lightning talk to a sold-out crowd.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
Deep Learning on Apache® Spark™ : Workflows and Best PracticesJen Aman
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark.
Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including:
* optimizing cluster setup;
* configuring the cluster;
* ingesting data; and
* monitoring long-running jobs.
We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters.
Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
This is an 1 hour presentation on Neural Networks, Deep Learning, Computer Vision, Recurrent Neural Network and Reinforcement Learning. The talks later have links on how to run Neural Networks on
Distributed Inference on Large Datasets Using Apache MXNet and Apache Spark ...Databricks
Deep Learning has become ubiquitous with abundance of data, commoditization of compute and storage. Pre-trained models are readily available for many use-cases. Distributed Inference has many applications such as pre-computing results offline, backfilling historic data with predictions from state-of-the-art models, etc.Inference on large scale datasets comes with many challenges prevalent in distributed data processing.
Attendees will learn how to efficiently run deep learning prediction on large data sets, leveraging Apache Spark and Apache MXNet (incubating).
In this session, we’ll cover core Deep Learning Concepts such as:
Types of Learning, a) Supervised Learning b) Unsupervised Learning c) Active Learning d) Reinforcement Learning
Supervised Learning types – classification, regression, Image classification
Types of Neural Networks – Feed forward Networks, CNNs, RNNs, GANs * Apache MXNet(Incubating) Deep Learning Framework. MXNet concepts ie., NDArray, Symbolic APIs and Module APIs. MXNet Gluon APIs * Distributed Inference using Apache MXNet and Apache Spark on Amazon EMR.
In this section, I will cover some of the use-cases of Distributed Inference, the challenges associated with running distributed Inference.
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & Deep Learning ...Databricks
We all know what they say – the bigger the data, the better. But when the data gets really big, how do you use it? This talk will cover three of the most popular deep learning frameworks: TensorFlow, Keras, and Deep Learning Pipelines, and when, where, and how to use them.
We’ll also discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data), as well as help you answer questions such as:
– As a developer how do I pick the right deep learning framework for me?
– Do I want to develop my own model or should I employ an existing one
– How do I strike a trade-off between productivity and control through low-level APIs?
In this session, we will show you how easy it is to build an image classifier with Tensorflow, Keras, and Deep Learning Pipelines in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you, and perhaps with a better sense for how to fool an image classifier!
What is Deep Learning
Rise of Deep Learning
Phases of Deep Learning - Training and Inference
AI & Limitations of Deep Learning
Apache MXNet History, Apache MXNet concepts
How to use Apache MXNet and Spark together for Distributed Inference.
Snorkel: Dark Data and Machine Learning with Christopher RéJen Aman
Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. However, building such applications is challenging: real world data is expressed in natural language, images, or other “dark” data formats which are fraught with imprecision and ambiguity and so are difficult for machines to understand. This talk will describe Snorkel, whose goal is to make routine Dark Data and other prediction tasks dramatically easier. At its core, Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets. In Snorkel, a user implicitly creates large training sets by writing simple programs that label data, instead of performing manual feature engineering or tedious hand-labeling of individual data items. We’ll provide a set of tutorials that will allow folks to write Snorkel applications that use Spark.
Snorkel is open source on github and available from Snorkel.Stanford.edu.
Apache Toree provides the interactive notebook for Spark/Scala. Toree is a IPython/Jupyter kernel. It lets you mix Spark/Scala code with markdown, execute the notebook, and publish it on the web.
Asim will talk about how to install and get started with Apache Toree, how to use it to develop Spark applications interactively in notebooks, and how to publish your notebooks.
Kaz Sato, Evangelist, Google at MLconf ATL 2016MLconf
Machine Intelligence at Google Scale: Tensor Flow and Cloud Machine Learning: The biggest challenge of Deep Learning technology is the scalability. As long as using single GPU server, you have to wait for hours or days to get the result of your work. This doesn’t scale for production service, so you need a Distributed Training on the cloud eventually. Google has been building infrastructure for training the large scale neural network on the cloud for years, and now started to share the technology with external developers. In this session, we will introduce new pre-trained ML services such as Cloud Vision API and Speech API that works without any training. Also, we will look how TensorFlow and Cloud Machine Learning will accelerate custom model training for 10x – 40x with Google’s distributed training infrastructure.
The term "machine learning" is increasingly bandied about in corporate settings and cocktail parties, but what is it, really? In this session we'll answer that question, providing an approachable overview of machine learning concepts, technologies, and use cases. We'll then take a deeper dive into machine learning topics such as supervised learning, unsupervised learning, and deep learning. We'll also survey various machine learning APIs and platforms. Technologies including Spring and Cloud Foundry will be leveraged in the demos. You'll be the hit of your next party when you're able to express the near-magical inner-workings of artificial neural networks!
Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep L...Simplilearn
Deep Learning covers all the essential Deep Learning frameworks that are necessary to build AI models. In this presentation, you will learn about the development of essential frameworks such as TensorFlow, Keras, PyTorch, Theano, etc. You will also understand the programming languages used to build the frameworks, the different companies that use these frameworks, the characteristics of these Deep Learning frameworks, and type of models that were built using these frameworks. Now, let us get started with understanding the different popular Deep Learning frameworks being used in industries.
Below are the different Deep Learning frameworks we'll be discussing in this presentation:
1. TensorFlow
2. Keras
3. PyTorch
4. Theano
5. Deep Learning 4 Java
6. Caffe
7. Chainer
8. Microsoft CNTK
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
Deep Learning for NLP (without Magic) - Richard Socher and Christopher ManningBigDataCloud
A tutorial given at NAACL HLT 2013.
Richard Socher and Christopher Manning
http://nlp.stanford.edu/courses/NAACL2013/
Machine learning is everywhere in today's NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations. The goal of the tutorial is to make the inner workings of these techniques transparent, intuitive and their results interpretable, rather than black boxes labeled "magic here". The first part of the tutorial presents the basics of neural networks, neural word vectors, several simple models based on local windows and the math and algorithms of training via backpropagation. In this section applications include language modeling and POS tagging. In the second section we present recursive neural networks which can learn structured tree outputs as well as vector representations for phrases and sentences. We cover both equations as well as applications. We show how training can be achieved by a modified version of the backpropagation algorithm introduced before. These modifications allow the algorithm to work on tree structures. Applications include sentiment analysis and paraphrase detection. We also draw connections to recent work in semantic compositionality in vector spaces. The principle goal, again, is to make these methods appear intuitive and interpretable rather than mathematically confusing. By this point in the tutorial, the audience members should have a clear understanding of how to build a deep learning system for word-, sentence- and document-level tasks. The last part of the tutorial gives a general overview of the different applications of deep learning in NLP, including bag of words models. We will provide a discussion of NLP-oriented issues in modeling, interpretation, representational power, and optimization.
Introduction to parallel iterative deep learning on hadoop’s next generation...Anh Le
Presented at the recent O’Reilly OSCON – Open Source Convention 2014 by Josh Patterson (Patterson Consulting) and Adam Gibson (Skymind.io) is “Introduction to Parallel Iterative Deep Learning on Hadoop’s Next-Generation YARN Framework.”
Hadoop Summit 2014 Distributed Deep LearningAdam Gibson
Deep Learning on Hadoop with DeepLearning4j and Metronome
Deep-learning is useful in detecting anomalies like fraud, spam and money laundering; identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
Distributed Deep Learning on Hadoop
Deep-learning is useful in detecting anomalies like fraud, spam and money laundering; identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
Covers basics Artificial neural networks and motivation for deep learning and explains certain deep learning networks, including deep belief networks and autoencoders. It also details challenges of implementing a deep learning network at scale and explains how we have implemented a distributed deep learning network over Spark.
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
Synthetic dialogue generation with Deep LearningS N
A walkthrough of a Deep Learning based technique which would generate TV scripts using Recurrent Neural Network. The model will generate a completely new TV script for a scene, after being training from a dataset. One will learn the concepts around RNN, NLP and various deep learning techniques.
Technologies to be used:
Python 3, Jupyter, TensorFlow
Source code: https://github.com/syednasar/talks/tree/master/synthetic-dialog
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Handwritten Recognition using Deep Learning with RPoo Kuan Hoong
R User Group Malaysia Meet Up - Handwritten Recognition using Deep Learning with R
Source code available at: https://github.com/kuanhoong/myRUG_DeepLearning
Deep Learning: Evolution of ML from Statistical to Brain-like Computing- Data...Impetus Technologies
Presentation on 'Deep Learning: Evolution of ML from Statistical to Brain-like Computing'
Speaker- Dr. Vijay Srinivas Agneeswaran,Director, Big Data Labs, Impetus
The main objective of the presentation is to give an overview of our cutting edge work on realizing distributed deep learning networks over GraphLab. The objectives can be summarized as below:
- First-hand experience and insights into implementation of distributed deep learning networks.
- Thorough view of GraphLab (including descriptions of code) and the extensions required to implement these networks.
- Details of how the extensions were realized/implemented in GraphLab source – they have been submitted to the community for evaluation.
- Arrhythmia detection use case as an application of the large scale distributed deep learning network.
Similar to Georgia Tech cse6242 - Intro to Deep Learning and DL4J (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.
MLConf 2013: Metronome and Parallel Iterative Algorithms on YARNJosh Patterson
Online learning techniques, such as Stochastic Gradient Descent (SGD), are powerful when applied to risk minimization and convex games on large problems. However, their sequential design prevents them from taking advantage of newer distributed frameworks such as Hadoop/MapReduce. In this session, we will take a look at how we parallelize parameter estimation for linear models on the next-gen YARN framework Iterative Reduce and the parallel machine learning library Metronome. We also take a look at non-linear modeling with the introduction of parallel neural network training in Metronome as well.
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.
A Deep Dive into Classification with Naive Bayes. Along the way we take a look at some basics from Ian Witten's Data Mining book and dig into the algorithm.
Presented on Wed Apr 27 2011 at SeaHUG in Seattle, WA.
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/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
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).
The affect of service quality and online reviews on customer loyalty in the E...
Georgia Tech cse6242 - Intro to Deep Learning and DL4J
1.
2. Josh Patterson
Email:
josh@pattersonconsultingtn.com
Twitter:
@jpatanooga
Github:
https://github.com/jpata
nooga
Past
Published in IAAI-09:
“TinyTermite: A Secure Routing Algorithm”
Grad work in Meta-heuristics, Ant-algorithms
Tennessee Valley Authority (TVA)
Hadoop and the Smartgrid
Cloudera
Principal Solution Architect
Today: Patterson Consulting
3. Overview
• What is Deep Learning?
• Deep Belief Networks
• DL4J
4.
5. What is Deep Learning?
Algorithm that tries to learn simple features in lower
layers
And more complex features in higher layers
6. Interesting Properties of Deep Learning
Reduces a problem with overfitting in neural
networks.
Introduces new techniques for "unsupervised feature
learning”
introduces new more automatic ways to figure out the
parts of your data you should feed into your learning
algorithm.
7. Chasing Nature
Learning sparse representations of auditory signals
leads to filters that closely correspond to neurons in
early audio processing in mammals
When applied to speech
Learned representations showed a striking
resemblance to the cochlear filters in the auditory
cortext
8. Yann LeCunn on Deep Learning
Has become the dominant method for acoustic
modeling in speech recognition
Quickly becoming the dominant method for several
vision tasks such as
object recognition
object detection
semantic segmentation.
9.
10. What is a Deep Belief Network?
Generative probabilistic model
Composed of one visible layer
Many hidden layers
Restricted Boltzman Machines
Each hidden layer learns relationship between units in
lower layer
Higher layer representations tend to become more complex
11. Restricted Boltzmann Machines
• Unsupervised model
• Does feature learning by repeated sampling of the input data.
• Learns how to reconstruct data for good feature detection.
12. Deep Belief Network Training
Pre-Train
We should each RBM layer unlabeled vectors
“unsupervised learning”
For each layer we want to minimize the Cross Entropy
Fine-Tune
We move the learned weights (hidden bias units) from the
RBMs to a traditional feed-forward neural network
We run gentle back-propagation with some labeled data
14. Deep Belief Network Diagram
• DBNs are classifiers
• Layers of RBMs
• Capped with a Logistic Layer
• RBMs are feature extractors
• RBMs learn features via
sampling
• Creates “simpler problem” for
later layers in stack
16. DeepLearning4J
Implementation in Java
Self-contained & built on Akka, Hazelcast, Jblas
Runs on desktop
Runs on Hadoop via YARN natively to scale out
Distributed to run faster and with more features than
current Theano-based implementations
17. Vectorized Implementation
Handles lots of data concurrently.
Any number of examples at once, but the code does
not change.
Faster: Allows for native/GPU execution.
One format: Everything is a matrix.
18. What are Good Applications for Deep Learning?
Image Processing
High MNIST Scores
Audio Processing
Current Champ on TIMIT dataset
Text / NLP Processing
Word2vec, etc
19. Parameter Averaging
McDonald, 2010
Distributed Training Strategies for the Structured Perceptron
Langford, 2007
Vowpal Wabbit
Jeff Dean’s Work on Parallel SGD
DownPour SGD
19
20. Parallelizing Deep Belief Networks
Two phase training
Pre Train
Fine tune
Each phase can do multiple passes over dataset
Entire network is averaged at master
21. PreTrain and Lots of Data
We’re exploring how to better leverage the
unsupervised aspects of the PreTrain phase of
Deep Belief Networks
Allows for the use of far less unlabeled data
Allows us to more easily modeled the massive amounts
of structured data in HDFS