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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    Symbols are predefined, we need a code to identify type of symbol and extract text inside it and export same to excel.

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    Symbols are predefined, we need to identify type of symbol and extract text inside it and export same to excel.

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    Project - Defense against adversarial attack dataset: MNIST, CIFAR10 adversarial attacks : There are several attacks possible on deep learning classifiers(for example, Lets say there is a handwritten image of 5 the deep learning classifier(here I used CNN) may classify it as 6 or any other number based on attack). github : [login to view URL] Defense GAN: [login to view URL], [login to view URL] ...

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    1) Make a mLSTM Keras layer via subclass [login to view URL] 2) Briefly test new layer with baseline model.

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    I want a code written for the robot to take images or video and recognize the images.

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