Zaccone / Jain / Karim Deep Learning with TensorFlow

Explore neural networks and build intelligent systems with Python
2. Auflage 2024
ISBN: 978-1-78883-183-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 484 Seiten

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow. Key Features - Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow
- Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide
- Gain real-world contextualization through some deep learning problems concerning research and application
Book DescriptionDeep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects. What you will learn - Apply deep machine intelligence and GPU computing with TensorFlow
- Access public datasets and use TensorFlow to load, process, and transform the data
- Discover how to use the high-level TensorFlow API to build more powerful applications
- Use deep learning for scalable object detection and mobile computing
- Train machines quickly to learn from data by exploring reinforcement learning techniques
- Explore active areas of deep learning research and applications
Who this book is forThe book is for people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.

Autoren/Hrsg.

Zaccone, Giancarlo

Jain, Vihan

Karim, Md. Rezaul

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik

Inhaltsverzeichnis

Table of Contents - Getting Started with Deep Learning
- A First Look at TensorFlow
- Feed-Forward Neural Networks with TensorFlow
- Convolutional Neural Networks
- Optimizing TensorFlow Autoencoders
- Recurrent Neural Networks
- Heterogeneous and Distributed Computing
- Advanced TensorFlow Programming
- Recommendation Systems using Factorization Machines
- Reinforcement Learning

Über Autor(innen)

Zaccone Giancarlo :

Giancarlo Zaccone has over fifteen years' experience of managing research projects in the scientific and industrial domains. He is a software and systems engineer at the European Space Agency (ESTEC), where he mainly deals with the cybersecurity of satellite navigation systems. Giancarlo holds a master's degree in physics and an advanced master's degree in scientific computing. Giancarlo has already authored the following titles, available from Packt: Python Parallel Programming Cookbook (First Edition), Getting Started with TensorFlow, Deep Learning with TensorFlow (First Edition), and Deep Learning with TensorFlow (Second Edition).Karim Md. Rezaul :

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.