Programming with Tensorflow: Solution for Edge Computing Applications

ISBN-13: 9783030570767 / Angielski / Twarda / 2021 / 190 str.

Kolla Bhanu Prakash; G. R. Kanagachidambaresan

Spis treści

1) Introduction to Tensorflow package 

2) Tensorflow Basics

3) Visualizations

4) Regression

5) Neural Network

6) CONVOLUTIONAL NEURAL NETWORK

7) RECURRENT NEURAL NETWORK

8) Application of Machine Learning and Deep Learning

9) Chatbot

10) PYTORCH

11) Pattern Recognition and Machine Learning

12) Programming Tensor Flow with Single Board Computers

O autorze

Dr. Kolla Bhanu Prakash is working as Professor and Research Group Head for Artificial Intelligence and Data Science Research Group in CSE DEPARTMENT, K L University, VIJAYAWADA, Andhra Pradesh, India. He received his M.Sc and M.Phil in Physics from Acharya Nagarjuna University, Guntur, India, M.E and Ph.D in Computer Science & Engineering from Sathyabama University,Chennai, India. Dr. Kolla Bhanu Prakash has 14+ years of experience working in academia, research, teaching, academic administration. His current research interests include Deep Learning, IOT, Big Data Analytics and Natural Language Processing. He was the recipient of Best Speaker award during M.Sc. He has authored over 52+ research papers in various national and international journals and conferences. His publications are indexed in Scopus, Web of Science, DBLP and Google scholar. He is IEEE – Senior Member, Fellow – ISRD, received Best Teacher Award - K.L.University, Reviewer - IEEE Access Journal, Treasurer - ACM Amaravathi Chapter, Life Member – ISTE, Reviewer and TPC member in National and International Conferences, IJRAR RMS MEMBER, International Journal of Research and Analytical Reviews, 5 Patents published, Reviewer – IEEE Access, Wireless Networks Journal, Applied Soft Computing Journal, International Journal of Advanced Computer Science and Applications (IJACSA), Senior Member - theIRED [UACEE] and MEMBER - EXPERTS OF ACADEMIC EXCELLENCE RESEARCH CENTRE. He is currently editing 6 International books with reputed publishers like Springer, Elsevier, Wiley, Degryuter and CRC press.

Dr. G. R. Kanagachidambaresan received his B.E degree in Electrical and Electronics Engineering from Anna University in 2010 and M.E Pervasive Computing Technologies in Anna University in 2012. He has completed his Ph.D. in Anna University Chennai in 2017. He is currently an Associate Professor, Department of CSE, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology. His main research interest includes IoT, Machine learning and Wireless Networks. He has authored several research articles in leading International journals. He is presently working with funding agencies like Indian Space Research Organisation and Department of Biotechnology on IoT projects. He is an Editor in Chief for Next Generation computing and communication Engineering series Wiley. He is also associate editor in Wireless Networks, Springer.

Blurb

This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs).

• Provides a practical end-to-end guide to TensorFlow, the leading open source software library for building and training neural networks;
• Pertains to a broad technical audience—from data scientists and engineers to students and researchers;
• Shows how to implement advanced techniques in deep learning and explore deep neural networks and layers of data abstraction.