TensorFlow


Create a Character-based Seq2Seq model using Python and Tensorflow

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In this article, I will share my findings on creating a character-based Sequence-to-Sequence model (Seq2Seq) and I will share some of the results I have found. All of this is just a tiny part of my Master Thesis and it took quite a while for me to learn how to convert the theoretical concepts into practical models. I will also share the lessons that I have learned. This blog post is about Natural Language Processing (NLP in short). It is not easy for computers to interpret texts.


PODCAST: Machine Learning, AgTech and Tensorflow HPE Newsroom

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The age of highly accessible, open source machine learning tools is upon us. No longer niche, everyone -- from data scientists to Japanese cucumber farmers -- is using machine-learning technologies. But what is machine learning? Machine learning is exactly what it sounds like -- software that can learn to solve a problem. Using large sets of data, an algorithm can be trained to understand that data.


AWS SageMaker brings machine learning to developers

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Amazon Web Services released a tool this week to empower developers to build smarter, artificial intelligence-driven applications like the AI experts. The role of the software tester has undergone significant upheaval and change in recent years. To help get you situated in today's landscape, we filled this guide with advice, research, and user reviews of popular test management tools. You forgot to provide an Email Address. This email address doesn't appear to be valid.


Create a Character-based Seq2Seq model using Python and Tensorflow

#artificialintelligence

In this article, I will share my findings on creating a character-based Sequence-to-Sequence model (Seq2Seq) and I will share some of the results I have found. All of this is just a tiny part of my Master Thesis and it took quite a while for me to learn how to convert the theoretical concepts into practical models. I will also share the lessons that I have learned. This blog post is about Natural Language Processing (NLP in short). It is not easy for computers to interpret texts.


Operationalizing Machine Learning

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Machine Learning (ML) powers an increasing number of the applications and services that we use daily. For organizations who are beginning to leverage datasets to generate business insights -- the next step after you've developed and trained your model is deploying the model to use in a production scenario. That could mean integration directly within an application or website, or it may mean making the model available as a service. As ML continues to mature the emphasis starts to shift from development towards deployment. You need to transition from developing models to real world production scenarios that are concerned with issues of inference performance, scaling, load balancing, training time, reproducibility and visibility.


google/kubeflow

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The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way for spinning up best of breed OSS solutions. This document details the steps needed to run the kubeflow project in any environment in which Kubernetes runs. Our goal is to help folks use ML more easily, by letting Kubernetes to do what it's great at: Because ML practitioners use so many different types of tools, it is a key goal that you can customize the stack to whatever your requirements (within reason), and let the system take care of the "boring stuff." While we have started with a narrow set of technologies, we are working with many different projects to include additional tooling.


Stan vs PyMc3 (vs Edward) – Towards Data Science

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The holy trinity when it comes to being Bayesian. I will provide my experience in using the first two packages and my high level opinion of the third (haven't used it in practice). Of course then there is the mad men (old professors who are becoming irrelevant) who actually do their own Gibbs sampling. You specify the generative model for the data. You feed in the data as observations and then it samples from the posterior of the data for you.


Google wants to solve new AI problems: Jeffrey Dean

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Tokyo: Jeffrey (Jeff) Dean is a Google senior fellow in a research group at Google where he leads the company's artificial intelligence (AI) project called Google Brain. Along with his team, Dean, who joined Google in 1999, is currently implementing the company's vision as articulated by chief executive Sundar Pichai--to build an "AI-first" world. In an interview on the sidelines of a "Google #MadewithAI" event, held recently in Tokyo, Dean explains what this vision encompasses and the challenges involved in implementing it. What are the major steps involved in this process of implementing the Google strategy of building an AI-first world? The steps involve making products that are useful, help others innovate and solve humanity's big challenges.


Jorge Muñoz on AI and the Brain – Good AI Lab

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This article is part of an ongoing "Humans of AI" series consisting of interviews with AI experts and visionaries around the world. When he's not working on one of his several projects, machine learning developer Jorge Muñoz spends much of his time trying to keep up to date on the cutting edge of artificial intelligence research. "Every month there is something new; every month there are people doing something interesting," he said. "The field is getting really huge, so it's getting really difficult to stay informed on what everyone is doing." The AI explosion of the last few years "as it's actually started working" has created a huge demand for resources to learn about AI, Muñoz said.


Updated AWS Deep Learning AMIs: New Versions of TensorFlow, Apache MXNet, Keras, and PyTorch

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The AMIs also come with improved framework support for NVIDIA Volta. They include PyTorch v0.3.0, and support NVIDIA CUDA 9 and cuDNN 7, with significant performance improvements for training models on NVIDIA Volta GPUs. As well, they include a version of TensorFlow built from the master and merged with NVIDIA processors for Volta support. We've also added Keras 2.0 support on the CUDA 9 version of the AWS Deep Learning AMIs to work with TensorFlow as the default backend.