Book Image

Intelligent Projects Using Python

By : Santanu Pattanayak
Book Image

Intelligent Projects Using Python

By: Santanu Pattanayak

Overview of this book

This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI. By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle.
Table of Contents (12 chapters)


Much like RBMs, autoencoders are a class of unsupervised learning algorithms that aim to uncover the hidden structures within data. In principal component analysis (PCA), we try to capture the linear relationships among input variables, and try to represent the data in a reduced dimension space by taking linear combinations (of the input variables) that account for most of the variance in data. However, PCA would not be able to capture the nonlinear relationships between the input variables.

Autoencoders are neural networks that can capture the nonlinear interactions between input variables while representing the input in different dimensions in a hidden layer. Most of the time, the dimensions of the hidden layer are smaller to those of the input. This we skipped, with the assumption that there is an inherent low-dimensional structure to the high-dimensional data. For instance, high-dimensional images can be represented by a low-dimensional manifold, and autoencoders are often used to discover that structure. The following diagram illustrates the neural architecture of an autoencoder:

Figure 1.20: Autoencoder architecture

An autoencoder has two parts: an encoder and a decoder. The encoder tries to project the input data, x, into a hidden layer, h. The decoder tries to reconstruct the input from the hidden layer h. The weights accompanying such a network are trained by minimizing the reconstruction error that is, the error between the reconstructed input, , from the decoder and the original input. If the input is continuous, then the sum of squares of the reconstruction error is minimized, in order to learn the weights of the autoencoder.

If we represent the encoder by a function, fW (x), and the decoder by fU (x), where W and U are the weight matrices associated with the encoder and the decoder, then the following is the case:



The reconstruction error, C, over the training set, xi, i = 1, 2, 3, ...m, can be expressed as follows:


The autoencoder optimal weights, , can be learned by minimizing the cost function from (3), as follows:


Autoencoders are used for a variety of purposes, such as learning the latent representation of data, noise reduction, and feature detection. Noise reduction autoencoders take the noisy version of the actual input as their input. They try to construct the actual input that acts as a label for the reconstruction. Similarly, autoencoders can be used as generative models. One such class of autoencoders that can work as generative models is called variational autoencoders. Currently, variational autoencoders and GANs are very popular as generative models for image processing.