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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    An application where user is asked to read a paragraph and upon reading the text is highlighted simultaneously. At the end of paragraph voice sample is collected and stored in a relational db. An analysis is preformed on the voice sample to determine age and gender.

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    Need someone to do a image classification project. templates and data will be provided. Need it done ASAP!

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    Need someone to do a image classification project. templates and data will be provided.

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    Deep learning 3 days left

    I have Mask RCNN model it is working, but I need to modify it as it read only one image with one Chanel as jpg file I need to modify it to read npy images with 2 and 3 channels. Also it only calculate the loss I need to use accuracy and AUC and sensitivity and specificity. It is working as pre-trained model, I need to see it with and without pretrained model

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