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
LOG-511: AWS Machine Learning RI for ONAP LogsClosed
LOG-104: Investigate Jaeger / opentracing / zipkin distributed tracing agent/serverClosed
Design/Theory Preparation
Get the following Nov 2016 MIT book from Amazon by Ian Goodfellow, Yoshua Benglo, Aaron Courville - (one for work, and one for home - as it is usually out of stock). Review your linear transformation and matrix math to prep.
https://developers.google.com/machine-learning/crash-course/
Installing Tensorflow
follow/verify via https://www.tensorflow.org/install/install_mac
obrien:obrienlabs amdocs$ docker run -it tensorflow/tensorflow bash
Unable to find image 'tensorflow/tensorflow:latest' locally
latest: Pulling from tensorflow/tensorflow
22dc81ace0ea: Pull complete
1a8b3c87dba3: Pull complete
91390a1c435a: Pull complete
07844b14977e: Pull complete
b78396653dae: Pull complete
22bb9efa20f2: Pull complete
e385adcc1f05: Pull complete
da0eaa434771: Pull complete