Applied Deep Learning

Deep Learning has become increasingly important due to the growing need to process and make sense of vast amounts of data in various fields. If you want to gain a deeper understanding of the techniques and implementations of deep learning, then this book is for you.

The book presents you with a thorough introduction to AI and Machine learning, starting from the basics and progressing to a comprehensive coverage of Deep Learning with Python. You will be introduced to the intuition of Neural Networks and how to design and train them effectively. Moving on, you will learn how to use Convolutional Neural Networks for image recognition and other visual tasks. The book then focuses on localization and object detection, which are crucial tasks in many applications, including self-driving cars and robotics. You will also learn how to use Deep Learning algorithms to identify and locate objects in images and videos. In addition, you will gain knowledge on how to create and train Recurrent Neural Networks (RNNs), as well as explore more advanced variations of RNNs. Lastly, you will learn about Generative Adversarial Networks (GAN), which are used for tasks like image generation and style transfer.

KEY FEATURES

* Learn how to design your own neural network efficiently.
* Learn how to build and train Recurrent Neural Networks (RNNs).
* Understand how encoding and decoding work in Deep Neural Networks.

WHAT YOU WILL LEARN

* Learn how to work efficiently with various Convolutional models.
* Learn how to utilize the You Only Look Once (YOLO) framework for object detection and localization.
* Understand how to use Recurrent Neural Networks for Sequence Learning.
* Learn how to solve the vanishing gradient problem with LSTM.
* Distinguish between fake and real images using various Generative Adversarial Networks.

WHO THIS BOOK IS FOR

This book is intended for both current and aspiring Data Science and AI professionals, as well as students of engineering, computer applications, and masters programs interested in Deep learning.

1. Basics of Artificial Intelligence and Machine Learning
2. Introduction to Deep Learning with Python
3. Intuition of Neural Networks
4. Convolutional Neural Networks
5. Localization and Object Detection
6. Sequence Modeling in Neural Networks and Recurrent Neural Networks (RNN)
7. Gated Recurrent Unit, Long Short-Term Memory, and Siamese Networks
8. Generative Adversarial Networks

Dr. Rajkumar Tekchandani (B.Tech, M.Tech, Ph.D, CSE) is working as an Assistant Professor in the Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala (Pb.), India. He has previously worked in the Department of Computer Science and Engineering at Dr. B.R Ambedkar National Institute of Technology, Jalandhar, Punjab. He has fourteen years of academic experience in Computer Science and Engineering. He has published numerous technical research papers in top-cited journals and conferences with a current h-index of 12. He is supervising research scholars leading to Ph.D. and guided M.E./M.Tech (18). His broad research areas are Deep Learning, Machine Learning, Cognitive Science, Natural Language Processing, and Software Code Clone Detection.

Dr. Neeraj Kumar (SMIEEE) (2019, 2020, 2021 highly-cited researcher from WoS) is working as a Dean DCT and Full-time professor in the Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala (Pb.), India. He is also an adjunct professor at various organizations in India and abroad. He has published more than 600 technical research papers (DBLP Link) in top-cited journals and conferences which are cited more than 40,000 times from well- known researchers across the globe with a current h-index of 110 (Google Scholar Link). He was named a highly cited researcher in 2019, 2020, and 2021 in the Web of Science (WoS) list. He has guided many research scholars leading to Ph.D. (16) and M.E./M.Tech (24). His research is funded by various competitive agencies across the globe. His broad research areas are Green computing and Network management, IoT, Big Data Analytics, Deep learning, and cyber-security.