Learn how to use Deep Learning with Keras to improve your Github projects. This tutorial will show you how to implement a Deep Learning model using the Keras framework.

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## Introduction to Deep Learning with Keras

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with many layers consisting of interconnected nodes. These algorithms are designed to improve the accuracy of predictions by using a large number of hidden layers in a neural network.

Keras is a Python library for deep learning that wraps the efficient numerical libraries Theano and TensorFlow. In this tutorial, you will discover how to use Keras to develop and evaluate deep learning models for regression, classification, and prediction problems.

After completing this tutorial, you will know:

How to load data and make it ready for use with Keras.

How to design and train deep learning models with Keras.

How to evaluate Keras deep learning models.

We will achieve all of these objectives by working through a practical case study that uses the Pima Indians onset of diabetes dataset.

## What is Keras?

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Use Keras if you need a deep learning library that:

Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).

Supports both convolution based networks and recurrent networks, as well as combinations of the two.

Runs seamlessly on CPU and GPU.

## Installing Keras

Keras is a machine learning framework for Python that allows for easy and fast prototyping, supports both convolution based networks and recurrent networks, and runs seamlessly on both CPU and GPU devices. In this tutorial, we will be using the Keras library to build a simple convolutional neural network to classify the MNIST dataset.

Before we can begin, we need to install Keras. The easiest way to do this is using pip:

“`shell

pip install keras

“`

If you are using a virtual environment, you may need to activate it before running the above command. You can do this by running the following command:

“`shell

source activate myenv

“`

## Using Keras

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Using Keras, you can easily design and train deep learning models without having to worry about the low-level details such as tensor shapes, file formats, optimization algorithms, etc.

If you are new to Keras, these are the two things you should focus on:

-Understand Keras’ workflow; i.e. how you can define a network in Keras and how easy it is to train & validate it.

-Get familiar with the various types of layers that are available in Keras, and see how they can be used to build different types of neural networks.

## Keras layers

Keras layers are the fundamental building blocks of deep learning models in Keras. A layer is a container for performing a specific task, such as convolution or pooling. Keras provides a wide variety of layer types, such as convolutional, pooling, and fully connected. You can also create your own custom layers.

## Keras models

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Use Keras if you need a deep learning library that:

-Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).

-Supports both convolution based networks and recurrent networks, as well as combinations of the two.

-Runs seamlessly on CPU and GPU.

## Keras callbacks

Deep Learning with Keras on Github – Keras CallbacksA callback is a set of functions to be applied at given stages of the training procedure. You can use callbacks to get a view on internal states and statistics of the model during training. You can pass a list of callbacks (as the keyword argument callbacks) to the .fit() method of the Sequential or Model classes. The relevant methods of the callbacks will then be called at each stage of the training.

## Keras datasets

There are many datasets available for deep learning, but sometimes it’s hard to know where to start or what’s available. That’s why the Keras team maintains a set of more than 100 open-source deep learning models, called the Keras Model Zoo.

The Keras Model Zoo is a collection of pretrained deep learning models that you can use in your own projects. Each model is trained on a different dataset, so you can experiment with different problems and compare results.

To find the right model for your task, you can search the Model Zoo by problem type (classification, detection, etc.), dataset (ImageNet, CIFAR-10, etc.), or model architecture (VGG16, ResNet50, etc.). You can also browse the models by task or dataset.

Once you’ve found a model that you want to use, you can install it using the keras_applications module. For example, to install the VGG16 model:

from keras_applications import vgg16

model = vgg16.VGG16(weights=’imagenet’)

## Keras applications

Keras Applications are deep learning models that are made available alongside pre-trained weights. These models can be used for prediction, feature extraction, and fine-tuning.

Keras Applications is the applications module of the Keras deep learning library. It provides model definitions and pre-trained weights for a number of popular architetures, such as VGG16, ResNet50, Xception, MobileNet, and more.

In addition to the models available in the Keras applications module, you can also find a number of community-developed application packages that are compatible with Keras.

## Conclusion

As we have seen, Keras is a powerful library for both beginners and experts to create state-of-the-art models with ease. It has a wide range of applications, from image classification and video analysis to time series forecasting and text generation. In this tutorial, we have only scratched the surface of what Keras can do.

If you want to explore more, I would recommend checking out the official Keras documentation [here](https://keras.io/). You can also find a lot of great Keras resources [here](https://github.com/keras-team/keras/tree/master/examples).

I hope you enjoyed this tutorial!

Keyword: Deep Learning with Keras on Github