Author: tayyaba

In this chapter, let us write a simple MPL based ANN to do regression prediction. Till now, we have only done the classification based prediction. Now, we will try to predict the next possible value by analyzing the previous (continuous) values and its influencing factors. The Regression MPL can be represented as below − The…

Let us modify the model from MPL to Convolution Neural Network (CNN) for our earlier digit identification problem. CNN can be represented as below − The core features of the model are as follows − Step 1 − Import the modules Let us import the necessary modules. Step 2 − Load data Let us import the mnist…

This chapter deals with the model evaluation and model prediction in Keras. Let us begin by understanding the model evaluation. Model Evaluation Evaluation is a process during development of the model to check whether the model is best fit for the given problem and corresponding data. Keras model provides a function, evaluate which does the…

Previously, we studied the basics of how to create model using Sequential and Functional API. This chapter explains about how to compile the model. The compilation is the final step in creating a model. Once the compilation is done, we can move on to training phase. Let us learn few concepts required to better understand…

As learned earlier, Keras model represents the actual neural network model. Keras provides a two mode to create the model, simple and easy to use Sequential API as well as more flexible and advanced Functional API. Let us learn now to create model using both Sequential and Functional API in this chapter. Sequential The core idea of Sequential API is simply arranging the Keras…

Keras allows to create our own customized layer. Once a new layer is created, it can be used in any model without any restriction. Let us learn how to create new layer in this chapter. Keras provides a base layer class, Layer which can sub-classed to create our own customized layer. Let us create a simple layer…

As learned earlier, Keras layers are the primary building block of Keras models. Each layer receives input information, do some computation and finally output the transformed information. The output of one layer will flow into the next layer as its input. Let us learn complete details about layers in this chapter. Introduction A Keras layer…

As we learned earlier, Keras modules contains pre-defined classes, functions and variables which are useful for deep learning algorithm. Let us learn the modules provided by Keras in this chapter. Available modules Let us first see the list of modules available in the Keras. Let us see backend module and utils model in this chapter. backend module backend module is used for…

Keras provides a complete framework to create any type of neural networks. Keras is innovative as well as very easy to learn. It supports simple neural network to very large and complex neural network model. Let us understand the architecture of Keras framework and how Keras helps in deep learning in this chapter. Architecture of…

Deep learning is an evolving subfield of machine learning. Deep learning involves analyzing the input in layer by layer manner, where each layer progressively extracts higher level information about the input. Let us take a simple scenario of analyzing an image. Let us assume that your input image is divided up into a rectangular grid…