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Artificial Intelligence and Deep Learning
Artificial Intelligence and Deep Learning
- 1 JIRAs
- 2 Design/Theory Preparation
- 3 Installing Tensorflow
- 4 AIaaS
- 4.1 Open Source
- 4.1.1 Acumos
- 4.1.1.1 Docker architecture
- 4.1.1 Acumos
- 4.2 Commercial
- 4.1 Open Source
Developers should be prepared to integrate with and use their deep learning portfolio. Before we get some scope on their API's and endpoints some background in deep learning (a specialized subset of machine learning concentrated on the "Greedy Layer-Wise Unsupervised Pretraining procedure" - Hinton, 2006 - University of Toronto) and the "Long Short-Term Memory model - Hochreiter and Schmidhuber 1997" will be required.
JIRAs
LOG-500: Machine Learning on ONAP Logs - streamed and bulk ML processingClosed