Examples of using Tensorflow in English and their translations into German
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Abstraction layer on top of Tensorflow or Theano.
TensorFlow and Keras help her in her studies.
We rely on the Machine Learning Framework TensorFlow.
TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.
RiseML adds support for Machine Learning workloads and frameworks like TensorFlow.
A neural network based on TensorFlow continuously analyzes current Internet routing.
TensorFlow, particularly and its popularity among developers could play into Google's hands.
Den ersten Vortrag«Machine Learning with Neural Networks, TensorFlow and Keras» startete Christoph Pletz mit den Basics von….
In addition, Google has developed its own Tensor Processing Units(TPUs)that are specifically adapted for the use with TensorFlow.
For example,Google also offers a Python API for its framework Tensorflow for neural networks, and PYTHON is also used in robotics.
Furthermore, TensorFlow is the leading AI engine and for developers the most important AI platform, which serves as the foundation of numerous AI projects.
The nice thing about it is that several software frameworks already exist- Google's TensorFlow can be used one-to-one on the hardware.
Google's latest open source software TensorFlow Lite for machine learning developers pre-release, is an exciting change in the area of AI.
What to expect: The main environments used are Matlab and/or Python,in addition to which any Tensorflow experience is welcomed.
TensorFlow Lite represents the first comprehensible steps in order to make Artificial Intelligence- powered devices not only accessible but also disposable.
And most likely,customers will also create their machine learning software with TensorFlow- the coding framework developed and operated by Google.
Tensorflow uses so-called tensorflow records, which contain the image and training data combined as a byte stream.
According to IBM, the new system demonstrablyimproves the performance of AI frameworks such as TensorFlow, Chainer and Caffe as well as accelerated databases such as Kinetica.
TensorFlow also provides various other neural network architectures and a vast number of features one could play around with for language learning and translation.
In Part 2 of this blog we will cover how to build a system optimized for benchmarking GPU Deep Learning performance using Ubuntu 18.04,NVIDIA GPU Cloud(NGC) and TensorFlow.
A growing number of freely available solutions for machine leaning,such as Google's TensorFlow, lets software developers make use of the latest technology without requiring particular knowledge.
LiCO accelerates Deep Learning(DL) and Machine Learning(ML) with integratedsupport for the most popular AI libraries and frameworks, such as Tensorflow, MXNet and Caffe.
Our speakers will be presenting some of the most prominent tools, such as Google TensorFlow, IBM Watson Developer Cloud, SAP Leonardo Machine Learning and DeepLearning4J and MLlib on Apache Spark.
In this 3-part blog series, we will discuss how to build a system, with an emphasis on benchmarking GPU performance for Deep Learning using Ubuntu 18.04,NVIDIA GPU Cloud(NGC) and TensorFlow.
LiCO provides workflows for both AI and HPC, and supportsmultiple AI frameworks, including TensorFlow, Caffe, Neon, and MXNet, allowing you to leverage a single cluster for diverse workload requirements.
Industrial VPU computers offer reliable, long-lasting, low power platforms for object and facial recognition, security access,and machine learning applications using Google Tensorflow and Facebook Caffe frameworks.
The new DDL software addresses that, and it should make it possible torun popular open source codes like Tensorflow, Caffe, Torch and Chainer over massive neural networks and data sets with very high performance and accuracy.
The Qualcomm® Neural Processing SDK for artificial intelligence(AI) is designed to help developers run one or more neural network models trained in Caffe/Caffe2,ONNX, or TensorFlow on Snapdragon mobile platforms, whether that is the CPU, GPU or DSP.
Together with UK-based production company Stinkdigital,the team used Google's open source platform TensorFlow to train a neural network- an algorithm modelled on the human brain- with 50,000 data sets based on the style preferences of more than 600 fashionistas.
DIL follows an open source strategy and has strong expertise in applying tools and frameworks such as R,SciKit Learn, Tensorflow, Keras, Apache Spark, Apache Cassandra and Apache Hadoop.