![]() ![]() As it can be seen, it can run on top of different frameworks seamlessly.Īs we mentioned in the previous post, in a Neural Network each node in a specific layer takes the weighted sum of the outputs from the previous layer, applies a mathematical function to them, and then passes that result to the next layer. It works the same, independently of the back-end that is used. It’s core principle is to make the process of building a neural network, training it, and then using it to make predictions, easy and accessible for anyone with a basic programming knowledge, while still allowing developers to fully customise the parameters of the ANN.īasically, Keras is actually just an interface that can run on top of different Deep Learning frameworks like CNTK, Tensorflow, or Theano for example. It was developed by François Chollet, a Deep Learning researcher from Google. Keras is an open source, high level library for developing neural network models. ![]() For the best learning experience, I suggest you first read the post, and then go through the code while glancing at the sections of the post that go along with it. This is interesting because having defined a task or application (creating a yes/no chatbot to answer specific questions), we will learn how to translate the insights from a research work onto an actual model that we can then use to reach our application goals.ĭon’t be scared if this is your first time implementing an NLP model I will go through every step, and put a link to the code at the end. ![]()
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