Deep Learning with TensorFlow
Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
Idioma: inglés
Editorial: Packt Publishing Limited, GB, 2023
- Tapa blanda
- Nuevo
Librería: Rarewaves.com USA, London, London, Reino UnidoRarewaves.com USA
Vendedor de IberLibro desde 11 de junio de 2025
Condición: Nuevo
EUR 67,77
Cantidad disponible: Más de 20 disponibles
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Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guideAbout This Book. 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. Real-world contextualization through some deep learning problems concerning research and application Who This Book Is ForThe book is intended for a general audience of 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.What You Will Learn. Learn about machine learning landscapes along with the historical development and progress of deep learning. Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x. Access public datasets and utilize them using TensorFlow to load, process, and transform data. Use TensorFlow on real-world datasets, including images, text, and more. Learn how to evaluate the performance of your deep learning models. Using 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 applicationsIn DetailDeep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x.Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context.After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.Style and approachThis step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to imple.…
N° de ref. del artículo LU-9781786469786
- Título
- Deep Learning with TensorFlow
- Autor
- Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
- Editorial
- Packt Publishing Limited, GB
- Año de publicación
- 2023
- Estado
- New
- Encuadernación
- Digital
- Idioma
- inglés
- ISBN 10
- 1786469782
- ISBN 13
- 9781786469786
- Peso del artículo
- 553 gramos
- Dimensiones
- 19.05 x 1.85 x 23.5 cm
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide
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
- Real-world contextualization through some deep learning problems concerning research and application
Book Description:
Deep learning is the step that comes after machine learning, and has more advanced
implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x.
Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including
search, image recognition, and language processing. Additionally, you'll learn how
to analyze and improve the performance of deep learning models. This can be done by
comparing algorithms against benchmarks, along with machine intelligence, to learn
from the information and determine ideal behaviors within a specific context.
After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.
What You Will Learn:
- Learn about machine learning landscapes along with the historical development and progress of deep learning
- Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x
- Access public datasets and utilize them using TensorFlow to load, process, and transform data
- Use TensorFlow on real-world datasets, including images, text, and more
- Learn how to evaluate the performance of your deep learning models
- Using 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 for:
The book is intended for a general audience of 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.
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
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.
Ahmed Menshawy is a Research Engineer at the Trinity College Dublin, Ireland. He has more than 5 years of working experience in the area of ML and NLP. He holds an MSc in Advanced Computer Science. He started his Career as a Teaching Assistant at the Department of Computer Science, Helwan University, Cairo, Egypt. He taught several advanced ML and NLP courses such as ML, Image Processing, and so on. He was involved in implementing the state-of-the-art system for Arabic Text to Speech. He was the main ML specialist at the Industrial research and development lab at IST Networks, based in Egypt.
“Acerca de” puede pertenecer a otra edición de este título.
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