Santanu Pattanayak - Intelligent Projects Using Python

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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.

Manohar Swamynathan is a data science practitioner and an avid programmer, with over 14 years of experience in various data-science-related areas, including data warehousing, BI, analytical tool development, ad hoc analysis, predictive modeling, consulting, formulating strategy, and executing analytics program. He's had a career covering the life cycle of data across different domains, such as US mortgage banking, retail/e-commerce, insurance, and Industrial IoT. He has a bachelor's degree with a specialization in physics, mathematics, and computers, and a master's degree in project management.

He has written Mastering Machine Learning With Python In Six Steps, and has also been a technical review of books on Python and R.

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Python Artificial Intelligence Projects will help you to build smart and practical AI-based systems leveraging deep learning and reinforcement learning. The projects illustrated in this book cover a wide range of domain problems related to healthcare, e-commerce, expert systems, surveillance fashion industry, mobile-based applications, and self-driving cars using techniques such as convolutional neural networks, deep reinforcement learning, LSTM-based RNNs, restricted Boltzmann machines, generative adversarial networks, machine translation, and transfer learning. The theoretical aspects of building the intelligent applications illustrated in this book will enable the reader to extend the projects in interesting ways and get them up to speed in building impactful AI applications. By the end of this book, you will be skilled enough to build your own smart models to tackle any kind of problem without any hassle.

This book is intended for data scientists, machine learning professionals, and deep learning practitioners who are ready to extend their knowledge of AI. If you want to build real-life smart systems that play a crucial role in every complex domain, then this book is what you need.

, Foundations of Artificial Intelligence Based Systems , covers the basics of how to build smart artificial systems using machine learning, deep learning, and reinforcement learning. We will be discussing various artificial neural networks, including CNNs for image processing purposes and RNNs for natural language processing purposes.

, Transfer Learning , covers how to use transfer learning to detect diabetic retinopathy conditions in the human eye, and to determine the retinopathy's severity. We will explore CNNs and learn how to train a model with CNN that is capable of detecting diabetic retinopathy in fundus images of the human eye.