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Intelligent Projects Using Python

By : Pattanayak

5 (3)

Intelligent Projects Using Python

5 (3)

By: 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.

Preface

Preface

Who this book is for

What this book covers

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Free Chapter

Foundations of Artificial Intelligence Based Systems

Foundations of Artificial Intelligence Based Systems

Neural networks

Neural activation units

The backpropagation method of training neural networks

Convolutional neural networks

Recurrent neural networks (RNNs)

Generative adversarial networks

Reinforcement learning

Transfer learning

Restricted Boltzmann machines

Autoencoders

Summary

Transfer Learning

Transfer Learning

Technical requirements

Introduction to transfer learning

Transfer learning and detecting diabetic retinopathy

The diabetic retinopathy dataset

Formulating the loss function

Taking class imbalances into account

Preprocessing the images

Additional data generation using affine transformation

Network architecture

The optimizer and initial learning rate

Cross-validation

Model checkpoints based on validation log loss

Python implementation of the training process

Results from the categorical classification

Inference at testing time

Performing regression instead of categorical classification

Using the keras sequential utils as generator

Summary

Neural Machine Translation

Neural Machine Translation

Technical requirements

Rule-based machine translation

Statistical machine-learning systems

Neural machine translation

Implementing a sequence-to-sequence neural translation machine

Summary

Style Transfer in Fashion Industry using GANs

Style Transfer in Fashion Industry using GANs

Technical requirements

DiscoGAN

CycleGAN

Learning to generate natural handbags from sketched outlines

Preprocess the Images

The generators of the DiscoGAN

The discriminators of the DiscoGAN

Building the network and defining the cost functions

Building the training process

Important parameter values for GAN training

Invoking the training

Monitoring the generator and the discriminator loss

Sample images generated by DiscoGAN

Summary

Video Captioning Application

Video Captioning Application

Technical requirements

CNNs and LSTMs in video captioning

A sequence-to-sequence video-captioning system

Data for the video-captioning system

Processing video images to create CNN features

Processing the labelled captions of the video

Building the train and test dataset

Building the model

Creating a word vocabulary for the captions

Training the model

Training results

Inference with unseen test videos

Summary

The Intelligent Recommender System

The Intelligent Recommender System

Technical requirements

What is a recommender system?

Latent factorization-based recommendation system

Deep learning for latent factor collaborative filtering

SVD++

Restricted Boltzmann machines for recommendation

Contrastive divergence

Collaborative filtering using RBMs

Collaborative filtering implementation using RBM

Inference using the trained RBM

Summary

Mobile App for Movie Review Sentiment Analysis

Mobile App for Movie Review Sentiment Analysis

Technical requirements

Building an Android mobile app using TensorFlow mobile

Movie review rating in an Android app

Preprocessing the movie review text

Building the model

Training the model

Freezing the model to a protobuf format

Creating a word-to-token dictionary for inference

App interface page design

The core logic of the Android app

Testing the mobile app

Summary

Conversational AI Chatbots for Customer Service

Conversational AI Chatbots for Customer Service

Technical requirements

Chatbot architecture

A sequence-to-sequence model using an LSTM

Building a sequence-to-sequence model

Customer support on Twitter

Summary

Autonomous Self-Driving Car Through Reinforcement Learning

Autonomous Self-Driving Car Through Reinforcement Learning

Technical requirements

Markov decision process

Learning the Q value function

Deep Q learning

Formulating the cost function

Double deep Q learning

Implementing an autonomous self-driving car

Discretizing actions for deep Q learning

Implementing the Double Deep Q network

Designing the agent

The environment for the self-driving car

Putting it all together

Results from the training

Summary

CAPTCHA from a Deep-Learning Perspective

CAPTCHA from a Deep-Learning Perspective

Technical requirements

Breaking CAPTCHAs with deep learning

CAPTCHA generation through adversarial learning

Summary

Other Books You May Enjoy

Other Books You May Enjoy

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Building an Android mobile app using TensorFlow mobile

In this project, we will be using TensorFlow's mobile capabilities to optimize a trained model as a protocol buffer object. We will then integrate the model with an Android app, the logic of which will be written in Java. We need to carry out the following steps:

Build a model in TensorFlow and train it with the relevant data.
Once the model performs satisfactorily on the validation dataset, convert the TensorFlow model to the optimized protobuf object (for example, optimized_model.pb).

Download Android Studio and its prerequisites. Develop the core application logic in Java and the interfacing pages using XML.
Integrate the TensorFlow trained model protobuf object and its associated dependencies in the assets folder within the project.
Build the project and run it.

The implementation of this Android app is illustrated...

Visually different images

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Tech Concepts

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Programming languages

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