How to calculate a timeseries applying Multi-ply Section Perceptron in Keras

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Practice makes perfect. Please practise this recipe on your own IDE to speed up your learning in the field of Applied Data Science.

What should I learn from this recipe?

You will learn:

How to code a keras and tensorflow model in Python.
How to create training and testing dataset using scikit-learn.
How to train a tensorflow and keras model.
How to predict a time series using Multi Layer Perceptron in Keras.

Download code at SETScholars:

How to predict a time series using Multi Layer Perceptron in Keras

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The Wealthy person Who Made anything Share Lot of money at 25

March 12 — Investor Josh Sason turned penny stock investments into a multi-million dollar fortune and is now working on building an entertainment empire. Bloomberg’s Zeke Faux reports on “In The Loop.” (Corrects name.)

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Developing Wise Espresso Uses with the use of Neural Platforms, Using the Neuroph Platform

Artificial neural networks provide solutions for ill-defined problems including recognition, such as image, character, and gesture; forecasting, such as stock market prediction; and optimization, such as JVM parameters. This session introduces the Neuroph Java open source neural network framework and shows how to use it, via demos and code samples. The session is intended for developers interested in artificial intelligence and the problems outlined above. You can learn more at: http://neuroph.sourceforge.net/
You will learn about
• The Java neural network framework Neuroph and its features and development
• Solving problems by using neural networks
• Using neural networks for image recognition, stock market prediction, and JVM tweaking
• Why and how Neuroph moved to the NetBeans platform and the resulting gains

Copyright © 2013 Oracle and/or its affiliates. Oracle® is a registered trademark of Oracle and/or its affiliates. All rights reserved. Oracle disclaims any warranties or representations as to the accuracy or completeness of this recording, demonstration, and/or written materials (the „Materials“). The Materials are provided „as is“ without any warranty of any kind, either express or implied, including without limitation warranties of merchantability, fitness for a particular purpose, and non-infringement.
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