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.
