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Intelligent Projects Using Python

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Learn
  • Build an intelligent machine translation system using seq-2-seq neural translation machines
  • Create AI applications using GAN and deploy smart mobile apps using TensorFlow
  • Translate videos into text using CNN and RNN
  • Implement smart AI Chatbots, and integrate and extend them in several domains
  • Create smart reinforcement, learning-based applications using Q-Learning
  • Break and generate CAPTCHA using Deep Learning and Adversarial Learning
About

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.

Features
  • A go-to guide to help you master AI algorithms and concepts
  • 8 real-world projects tackling different challenges in healthcare, e-commerce, and surveillance
  • Use TensorFlow, Keras, and other Python libraries to implement smart AI applications
Page Count 342
Course Length 10 hours 15 minutes
ISBN9781788996921
Date Of Publication 31 Jan 2019
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
Technical requirements
Chatbot architecture
A sequence-to-sequence model using an LSTM
Building a sequence-to-sequence model 
Customer support on Twitter 
Summary

Authors

Santanu Pattanayak

Santanu Pattanayak works as a Staff Machine Learning Specialist at Qualcomm Corp R&D and is an author of the deep learning book Pro Deep Learning with TensorFlow - A Mathematical Approach to Advanced Artificial Intelligence in Python. He has around 12 years of work experience and has worked at GE, Capgemini, and IBM before joining Qualcomm. He graduated with a degree in electrical engineering from Jadavpur University, Kolkata and is an avid math enthusiast. Santanu is currently pursuing a master's degree in data science from Indian Institute of Technology (IIT), Hyderabad. He also participates in Kaggle competitions in his spare time where he ranks in top 500. Currently, he resides in Bangalore with his wife.