Can I read Methods and Techniques in Deep Learning : Advancements in MmWave Radar Solutions on EtoBox?
Methods and Techniques in Deep Learning : Advancements in MmWave Radar Solutions by Avik Santra, Souvik Hazra, Lorenzo Servadei, Thomas Stadelmayer, Michael Stephan, Anand Dubey is a nonfiction available to read on EtoBox.
What is Methods and Techniques in Deep Learning : Advancements in MmWave Radar Solutions about?
Methods and Techniques in Deep Learning Introduces multiple state-of-the-art deep learning architectures for mmWave radar in a variety of advanced applications Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmWave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution. A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrates how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmWave radars. In-depth cha
Who reads Methods and Techniques in Deep Learning : Advancements in MmWave Radar Solutions?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Avik Santra, Souvik Hazra, Lorenzo Servadei, Thomas Stadelmayer, Michael Stephan, Anand Dubey
- Publisher
- Wiley-IEEE Press
- Published
- 2022
- Language
- EN
- ISBN
- 9781119910657
- Category
- nonfiction
- Subjects
- Engineering, Computer Science, Stem
