Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition

Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition
Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition by Giancarlo Zaccone, Md. Rezaul Karim
English | 2018 | ISBN: 1788831109 | 484 Pages | True PDF, EPUB | 1047 MB

Deep Learning with TensorFlow – Second Edition: Explore neural networks with Python
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow v1.7.
Deep 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 v1.7, 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 v1.7
  • 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