Join the greatest minds in AI and data science for this 2-day interactive event packed with deep-dive technical sessions, talks on real-world business use cases and a hands-on training. You'll discover the strategies and insights you need to optimize and transform your business and prepare for the wave of AI.
H2O’s Deep Water puts deep learning in the hands of enterprise users
Download File: https://miimms.com/2vGHw3
The poet A. R. Ammons once wrote, "A word too much repeated falls out of being" and although the term AI (encompassing machine & deep learning domains) for sure feels "too much repeated',' it's not about to fall "out of being" any time soon. In this session you'll hear about how your artificial intelligence (AI) needs an infrastructure agenda (IA). As business hopes move from phases rennovaton to those of innovation, there's a massive problem: talent, trust, and time. All of these friction points to realising the true value of AI. In this sesion, which include live demos and won't be your standards set of powerpoint slideware, you'll hear how the things H20 and IBM are partnering around to empower the many on the journey to AI.
Bio: Kevin Doyle is the lead architect of IBM Spectrum Conductor at IBM, where he works with customers to deploy and manage all workloads; especially Spark and deep learning workloads to on-premise clusters. Kevin has been working on distributed computing, grid, cloud, and big data for the past five years with a focus on the management and lifecycle of workloads.
Many of these nodes are provided through open source integrations (why reinvent the wheel?). This provides seamless access to large open source projects such as Keras and Tensorflow for deep learning, Apache Spark for big data processing, Python and R for scripting, and more. These integrations can be used in combination with other KNIME nodes meaning that data scientists can freely select from a vast variety of options when tackling an analysis problem.
Bio: Yoann Lechevallier is a Senior Systems Engineer at BlueData, where he focuses on helping enterprise customers deploy AI, machine learning, and big data analytics applications running on containers. Yoann has deep expertise in systems integration, performance tuning, and data analysis. He recently built containerized environments for H2O Flow, Sparkling Water, and Driverless AI for deployment with the BlueData EPIC software platform. He also developed a data connector for H2O Driverless AI to enable compute / storage separation with BlueData. Prior to BlueData, Yoann has held positions in consulting, benchmark engineering, and professional services at Splunk, IBM, Bull SAS, Seanodes, and Sun Microsystems. Yoann has extensive experience working with leading enterprises throughout Europe, the Middle East, and Africa - including financial services and insurance (Barclays, RBS, HSBC, Vanquis, Lloyds, BNP, UBS, KBC, JPMC, Prudential, Royal London), telecommunications (BT, H3G, Nokia), and healthcare (HSCIC, Sidra). Yoann holds a Master of Science degree from INSA in Rouen, France as well as a Masters degree in Embedded Computing from SUPAERO in Toulouse, France.
Bio: Yoann Lechevallier is a Senior Systems Engineer at BlueData, where he focuses on helping enterprise customers deploy AI, machine learning, and big data analytics applications running on containers. Yoann has deep expertise in systems integration, performance tuning, and data analysis. He recently built containerized environments for H2O Flow, Sparkling Water, and Driverless AI for deployment with the BlueData EPIC software platform. He also developed a data connector for H2O Driverless AI to enable compute / storage separation with BlueData.Prior to BlueData, Yoann has held positions in consulting, benchmark engineering, and professional services at Splunk, IBM, Bull SAS, Seanodes, and Sun Microsystems. Yoann has extensive experience working with leading enterprises throughout Europe, the Middle East, and Africa - including financial services and insurance (Barclays, RBS, HSBC, Vanquis, Lloyds, BNP, UBS, KBC, JPMC, Prudential, Royal London), telecommunications (BT, H3G, Nokia), and healthcare (HSCIC, Sidra).Yoann holds a Master of Science degree from INSA in Rouen, France as well as a Masters degree in Embedded Computing from SUPAERO in Toulouse, France.
This will be a hands-on training of our groundbreaking products, H2O Driverless AI, H2O-3 and Sparkling Water. Join your fellow data scientists, developers and engineers in this technical deep-dive of H2O.
The pathway to combat business challenges using next-gen technologies is often laden with strategic hurdles. Today, the battle for building ideal AI applications has brought us to explore two emerging machine learning frameworks, namely TensorFlow and H2O.ai. While the former is renowned for its high computational power, the latter is empowering Fortune 500 companies to expedite deep learning. As a leading provider of TensorFlow development services, Oodles AI evaluates the comparison of TensorFlow Vs H2O for building enterprise-grade applications.
The proliferation of visual data across enterprises, industries, and the digital landscape has globalized image and video processing applications. TensorFlow reinforces the development of large-scale deep learning models including image classification and object detection for diverse use cases.
We, at Oodles AI, are constantly exploring the enterprise advantages and applications of both TensorFlow and H2O to provide customer-centric AI development services. Our AI team has experiential knowledge in building and deploying dynamic ML models powered by deep neural networks. Our AI capabilities with TensorFlow and H2O expand to-
This is the second installment in a four-part review of 2016 in machine learning and deep learning. Part One, here, covered general trends. In Part Two, we review the year in open source machine learning and deep learning projects. Parts Three and Four will cover commercial machine learning and deep learning software and services.
The team delivered three releases in 2016, adding algorithms and other features, including deep learning and GPU support. Given the support from IBM, it seems likely that the project will hit Release 1.0 this year and graduate to top-level status.
SINGA is a distributed deep learning project originally developed at the National University of Singapore and donated to Apache in 2015. The platform currently supports feed-forward models, convolutional neural networks, restricted Boltzmann machines, and recurrent neural networks. It includes a stochastic gradient descent algorithm for model training.
We include in this category software whose primary purpose is deep learning. Many general-purpose machine learning packages also support deep learning, but the packages listed here are purpose-built for the task.
In 2016, Microsoft rebranded its deep learning framework as Microsoft Cognitive Toolkit (MCT) and released Version 2.0 to beta, with a new Python API and many other enhancements. In VentureBeat, Jordan Novet reports.
In the Huffington Post, Chollet explains how Keras differs from other DL frameworks. Short version: Keras abstracts deep learning architecture from the computational back end, which made it easy to port from Theano to TensorFlow.
Deeplearning4j (DL4J) is a project of Skymind, a commercial venture. IT is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J runs on distributed GPUs and CPUs. Skymind benchmarks well against Caffe, TensorFlow, and Torch.
> I agree with you that Google has designed TensorFlow to drive inference business to its cloud platform. Where I disagree with you is in thinking that there is a great benefit for organizations to build out their own deep learning back ends rather than training in the cloud.
As previously described, Neural Networks (NNs) are a subset of ML techniques. These networks are not intended to be realistic models of the brain, but rather robust algorithms and data structures able to model difficult problems. NNs have units (neurons) organized in layers. There are basically three layer categories: input layers, hidden (middle) layers and output layers. NNs can be divided into shallow (one hidden layer) and deep (more hidden layers) networks. The predictive capability of NNs comes from this hierarchical multilayered structure. Through proper training the network can learn how to optimally represent inputs as features at different scales or resolutions and combine them into higher-order feature representations relating these representations to output variables and therefore learning how to make predictions.
Chainer supports CUDA/cuDNN using CuPy, for high performance training and inference, and the Intel Math Kernel Library (Intel MKL) for Deep Neural Networks (MKL-DNN), which accelerates DL frameworks on Intel based architectures. It also contains libraries for industrial applications; e.g., ChainerCV (for computer vision), ChainerRL (for deep reinforcement learning) and ChainerMN (for scalable multi-node distributed DL) (ChainerMN 2018). 2ff7e9595c
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