Keras is an open source neural network library that is written in the Python language. It should be noted that it is capable of running on top of other frameworks/software libraries, such as Microsoft Cognitive Toolkit, TensorFlow, and Theano. For those who don’t know, a neural network is actually a computing system that is meant to imitate the neural activity of animal brains, and is a collection of nodes called “artificial neurons”.

Keras as a neural network helps to optimize a lot of functions, and is used to make working with image and text data infinitely easier. This could help to improve productivity for countless platforms and companies who work with data. It is a tool that caters to the recent technological trend of deep learning, where companies are using AI to optimize their companies and find out more information about their consumer base, such as predictive trends or overall consumer trends.

Keras has over 200,000 users already, and was recently the 10th most cited tool in the 2018 Nuggets 2018 software poll, which indicates that it is rising in popularity and relevancy in the tech sector. It ultimately helps many companies experiment faster with certain processes, as well.

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    I am looking at developing a decision making support tool that will assist companies to choose best software solution among a set of alternatives. This tool -black box - will use an install base database to train the model and will receive as inputs a couple of informations about the company (Exe: Nbre of employees, sector, Turnover, Budget..etc).

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    As title describes. More information will be provided if selected.

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    I want a simple NN that Is able to transform a Low Resolution image to high resolution image. The dataset has both type of image, LR, and HR. I'm not interested in very high performance... Instead I need 3 type of NN. 1 By using SCNN approach. 2. By using Variational Autoencoders 3. By using Gan. I will repeat that I'm not interested in very high performance... But what I will se...

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