Orhan Gazi Yalçın - Applied Neural Networks with Tensorflow 2: Pi Oriented Deep Learning with Python

Orhan Gazi Yaln

Applied Neural Networks with TensorFlow 2

API Oriented Deep Learning with Python

1st ed.

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I dedicate this book to my overcurious dad, Lutfiwho kept sneaking into the study room to see how far I was into the bookand to my mom, Aye, for always supporting and encouraging me.

I would also like to thank my friend, Enes, for encouraging me to write this book in the first place.

Finally, I would like to thank my sister and brother, Merve and Krat, and all my friends who supported me throughout the whole processall the waytill the last word.

This book was written during a global lockdown due to the Covid-19 pandemic, which created a new normal that I have never experienced before. Writing a book in the middle of a global crisis was a very intense experience, and I was uncertain about taking this responsibility for a long time. Thanks to my family and friends, I was able to complete the book even earlier than scheduled. Now I am glad that I accepted Aarons invitation, who guided me throughout the whole process. Thank you very much for reaching out to me in the first place and making it possible to have this book written.

I would like to thank Jessica Vakili for coordinating the entire project and for being there whenever I needed. I would also like to thank Vishwesh Ravi Shrimali for reviewing every single line of the book and providing me with all the valuable comments, which helped to improve the quality of the book tremendously.

Being surrounded with people who all have a positive attitude made this experience very fruitful, and I am looking forward to working with them in the future. Thank you all very much!

In this book, we dive into the realms of deep learning (DL) and cover several deep learning concepts along with several case studies. These case studies range from image recognition to recommender systems, from art generation to object clustering. Deep learning is part of a broader family of machine learning (ML) methods based on artificial neural networks (ANNs) with representation learning. These neural networks mimic the human brain cells, or neurons, for algorithmic learning, and their learning speed is much faster than human learning speed. Several deep learning methods offer solutions to different types of machine learning problems: (i) supervised learning, (ii) unsupervised learning, (iii) semi-supervised learning, and (iv) reinforcement learning.