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Title: Introduction to Keras Library in Python - A Comprehensive Tutorial
Keras is an open-source high-level neural networks API written in Python that runs on top of other popular deep learning libraries like TensorFlow and Theano. It provides a user-friendly and modular interface for building, training, and deploying deep learning models. This tutorial will guide you through the basics of Keras, covering key concepts and providing code examples.
Before starting, make sure you have Python installed on your machine. You can install Keras using the following command:
Additionally, you may want to install a backend engine such as TensorFlow:
Let's begin by importing the necessary modules and creating a simple neural network using Keras.
In this example, we create a simple feedforward neural network with one hidden layer. The model is then compiled with binary cross-entropy loss and the Adam optimizer.
Keras simplifies the process of loading common datasets. Let's use the famous MNIST dataset as an example:
Now, let's build a CNN using Keras for image classification:
This example demonstrates building a simple CNN for the MNIST dataset.
Keras simplifies the process of creating and training deep learning models. This tutorial covered the basics, from building a simple neural network to creating a convolutional neural network for image classification. Further exploration and experimentation will help you gain a deeper understanding of Keras and its capabilities.
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