Machine Learning Use Cases with Azure

Nervana's deep learning platform provides unprecedented computing power through specialized hardware. It includes a fast deep learning framework called Neon that is 10 times faster than other frameworks on GPUs. Neon also includes pre-trained models and is under active development to improve capabilities like distributed computing and integration with other frameworks. Nervana aims to make deep learning more accessible and applicable across industries like healthcare, automotive, finance, and more.

[Research] azure ml anatomy of a machine learning service - Sharat Chikkerur

PAPIs.io

Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016

MLconf

This document discusses using DL4J and DataVec to build deep learning workflows for modeling time series sensor data with recurrent neural networks. It provides an example of loading and transforming time series data from sensors using DataVec, configuring an RNN using DL4J to classify the trends in the sensor data, and training the network both locally and distributed on Spark. The document promotes DL4J and DataVec as tools that can help enterprises overcome challenges to operationalizing deep learning and producing machine learning models at scale.

MLConf 2016 SigOpt Talk by Scott Clark

This document discusses Bayesian global optimization and its application to tuning machine learning models. It begins by outlining some of the challenges of tuning ML models, such as the non-intuitive nature of the task. It then introduces Bayesian global optimization as an approach to efficiently search the hyperparameter space to find optimal configurations. The key aspects of Bayesian global optimization are described, including using Gaussian processes to build models of the objective function from sampled points and finding the next best point to sample via expected improvement. Several examples are provided demonstrating how Bayesian global optimization outperforms standard tuning methods in optimizing real-world ML tasks.

AWS re:Invent 2016: Using MXNet for Recommendation Modeling at Scale (MAC306)

For many companies, recommendation systems solve important machine learning problems. But as recommendation systems grow to millions of users and millions of items, they pose significant challenges when deployed at scale. The user-item matrix can have trillions of entries (or more), most of which are zero. To make common ML techniques practical, sparse data requires special techniques. Learn how to use MXNet to build neural network models for recommendation systems that can scale efficiently to large sparse datasets.

Image Classification Done Simply using Keras and TensorFlow

Rajiv Shah

This presentation walks through the process of building an image classifier using Keras with a TensorFlow backend. It will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. The presentation will start with building a simple convolutional network, augmenting the data, using a pretrained network, and finally using transfer learning by modifying the last few layers of a pretrained network. The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found at https://github.com/rajshah4/image_keras, and the presentation will discuss how to extend the code to your own pictures to make a custom image classifier.

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.

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Ido Shilon

This document discusses deep learning research at Nielsen's Machine Learning Lab. It begins with an introduction to the speaker's background and an overview of machine learning categories. It then discusses Nielsen's use of deep learning for tasks like look-alike modeling and online ad targeting. The rest of the document covers motivations for deep learning, neural network architectures like convolutional and recurrent networks, and techniques to improve inference speed, such as using GPUs, sparse matrix multiplication, and network trimming.

Introduction to Keras

John Ramey

An introduction to Keras, a high-level neural networks library written in Python. Keras makes deep learning more accessible, is fantastic for rapid protyping, and can run on top of TensorFlow, Theano, or CNTK. These slides focus on examples, starting with logistic regression and building towards a convolutional neural network. The presentation was given at the Austin Deep Learning meetup: https://www.meetup.com/Austin-Deep-Learning/events/237661902/

Deep learning on mobile

Kaz Sato, Evangelist, Google at MLconf ATL 2016

MLconf

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.

Distributed Deep Learning on AWS with Apache MXNet

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.

A Scaleable Implementation of Deep Learning on Spark -Alexander Ulanov

Spark Summit

This document summarizes research on implementing deep learning models using Spark. It describes: 1) Implementing a multilayer perceptron (MLP) model for digit recognition in Spark using batch processing and matrix optimizations to improve efficiency. 2) Analyzing the tradeoffs of computation and communication in parallelizing the gradient calculation for batch training across multiple nodes to find the optimal number of workers. 3) Benchmark results showing Spark MLP achieves similar performance to Caffe on a single node and outperforms it by scaling nearly linearly when using multiple nodes.

Deep Learning with Microsoft Cognitive Toolkit

This document provides an overview of Microsoft's Cognitive Toolkit (CNTK), a deep learning framework. It discusses key aspects of CNTK including its components, how to get started, and its capabilities. CNTK provides tools for common deep learning tasks like computer vision, natural language processing, and time series prediction. It also supports distributed training on multiple GPUs and machines and has APIs in Python, C++, and C#.

Deep learning with Keras

QuantUniversity

Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better! In this workshop, we will discuss the basics of Neural Networks and discuss how Deep Learning Neural networks are different from conventional Neural Network architectures. We will review a bit of mathematics that goes into building neural networks and understand the role of GPUs in Deep Learning. We will also get an introduction to Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano and Tensorflow.

Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017

StampedeCon

This technical session provides a hands-on introduction to TensorFlow using Keras in the Python programming language. TensorFlow is Google’s scalable, distributed, GPU-powered compute graph engine that machine learning practitioners used for deep learning. Keras provides a Python-based API that makes it easy to create well-known types of neural networks in TensorFlow. Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to train neural networks of much greater complexity. Deep learning allows a model to learn hierarchies of information in a way that is similar to the function of the human brain.

Deep Learning, Microsoft Cognitive Toolkit (CNTK) and Azure Machine Learning ...

Naoki (Neo) SATO

The document provides information about Microsoft's Cognitive Toolkit (CNTK), including benchmark performance comparisons with other deep learning frameworks and examples of using CNTK for common neural network architectures and natural language processing tasks. It shows that CNTK achieves state-of-the-art performance and scales nearly linearly with multiple GPUs. The document also provides code examples for defining common neural network components and training models with CNTK.

(BDT311) Deep Learning: Going Beyond Machine Learning

Edge AI and Vision Alliance

"New Dataflow Architecture for Machine Learning," a Presentation from Wave Co...

Wave Computing is a startup that has developed a new dataflow architecture called the Dataflow Processing Unit (DPU) to accelerate deep learning training by up to 1000x. Their initial market focus is on machine learning in the datacenter. They have invented a Coarse Grain Reconfigurable Array architecture that can statically schedule dataflow graphs onto a massive array of processors. Wave is now accepting qualified customers for its Early Access Program to provide select companies early access to benchmark Wave's machine learning computers before official sales begin.

Introduction to Deep Learning with Will Constable

This document discusses deep learning frameworks Neon and Nervana Cloud. It provides an introduction to deep learning, describing how it uses learned features from multiple levels to make predictions rather than designed features. The document outlines Neon's model design, training workflow, model zoo, Caffe compatibility, and backend interface. It also describes the Nervana Cloud for importing, building, training, and deploying models using Neon on AWS hardware and S3 storage. A demo of the tools is proposed.

An Introduction to Deep Learning (May 2018)

This document provides an introduction to deep learning, including common network architectures and use cases. It defines artificial intelligence, machine learning, and deep learning. It discusses how neural networks are trained using stochastic gradient descent and backpropagation to minimize loss and optimize weights. Common network types are described, such as convolutional neural networks for image recognition and LSTM networks for sequence prediction. Examples of deep learning applications include machine translation, object detection, segmentation, and generation of images, text, and video. Resources for learning more about deep learning are provided.

Deep learning on mobile - 2019 Practitioner's Guide

This document provides an overview of deep learning on mobile devices. It discusses why deep learning is important for mobile, including issues like privacy, reliability and latency. It then covers topics like how to train models for mobile using techniques like transfer learning and fine-tuning. The document also discusses frameworks for running models efficiently on mobile like Core ML, TensorFlow Lite and Google's ML Kit. It explores how hardware impacts performance and how to optimize models. Finally, it touches on applications of deep learning on mobile and techniques like federated learning.

V like Velocity, Predicting in Real-Time with Azure ML

This document discusses using Azure Machine Learning and stream processing to enable predictive maintenance for aircraft engines. It describes a use case of predicting whether a device will fail within the next two weeks using real-time sensor data streams. It then outlines the challenges of stream processing and applying machine learning to streaming data. The proposed solution architecture involves using Event Hub for data ingestion, Stream Analytics for stream processing and aggregations, Machine Learning for model training and predictions, and DocumentDB for storing prediction results. It provides examples of the Stream Analytics and Machine Learning workflows used to enable predictive maintenance from real-time sensor data streams.

Nervana and the Future of Computing

The document discusses Nervana's approach to building hardware optimized for deep learning. It describes Nervana's tensor processing unit (TPU) which provides unprecedented compute density, a scalable distributed architecture, memory near the computation, and power efficiency. The TPU is optimized to take advantage of the characteristics of deep learning workloads and provides 10-100x gains over GPUs. Nervana is also developing software like their Neon library and cloud services to make deep learning more accessible and efficient.

MLconf 2017 Seattle Lunch Talk - Using Optimal Learning to tune Deep Learning...

In this talk we introduce Bayesian Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. Deep learning pipelines are notoriously expensive to train and often have many tunable parameters including hyperparameters, the architecture, feature transformations that can have a large impact on the efficacy of the model. We will motivate the problem by giving several example applications using multiple open source deep learning frameworks and open datasets. We’ll compare the results of Bayesian Optimization to standard techniques like grid search, random search, and expert tuning.

Machine Learning and its Use Cases (dsth Meetup#3)

Data Science Thailand

Pixels.camp - Machine Learning: Building Successful Products at Scale

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See video at: https://www.youtube.com/watch?v=p7s1lcaeoZk How to build Machine Learning products that scale and autonomously evolve using open source technologies like Spark, Cassandra, Hadoop and many others. While data technologies have been exploding and becoming commoditized, using them effectively to build a product that delivers real value to users can be a mysterious art. A lot of companies still use "gather data, think about it later" but then fail to put that data to work. Let’s demystify machine learning system’s Data Science lifecycle (from data to production to a continuously evolvable system). Explore the fundamental recipe to build data-learning products that put data to work and provide experiences that are, ironically, more human.

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The document discusses the power of emotion in customer experience. It notes that while emotion is the least focused on element by companies, it has the greatest impact on customer loyalty. Customers give companies low ratings for how they make them feel emotionally. The document then explores what drives customer emotion both physically and psychologically. It provides advice on how companies can design customer experiences to better account for emotion, such as through qualitative customer research, focusing on customer journeys, and aiming to make customers feel happy rather than just satisfied.

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Machine Learning Using Cloud Services

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This document provides an overview of machine learning and how cloud services can help with machine learning projects. It defines machine learning and describes the main types (supervised, unsupervised, reinforcement learning). It then discusses how the cloud helps with the main parts of a machine learning workflow: fetching and preparing data using cloud data warehousing, training models using GPUs and cloud-based computation, and deploying models using serverless functions and APIs. It also mentions some pre-built AI services like IBM Watson and Amazon AI.

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This document discusses how machine learning and the cloud are transforming one another. It explains that machine learning involves computers learning from data without being explicitly programmed. It then outlines how major companies are using machine learning and lists some examples. It describes how the cloud is allowing more organizations to leverage machine learning by providing services that can analyze large amounts of data. Finally, it provides details on Google Cloud's machine learning services and tools.

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