E-book details

Deep Learning with TensorFlow. Explore neural networks with Python

Deep Learning with TensorFlow. Explore neural networks with Python

Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy

Ebook
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.
  • 1. Getting Started with Deep Learning
  • 2. First look at Tensorflow: Session & Graphs
  • 3. Using TensorFlow on a Feed Forward Neural Network
  • 4. TensorFlow on a Convolutional Neural Network
  • 5. Optimizing TensorFlow Autoencoders
  • 6. Recurrent Neural Networks
  • 7. GPU Computing
  • 8. Advanced TensorFlow Programming
  • 9. Advanced Multimedia Programming with TensorFlow
  • 10. Reinforcement Learning
  • Title: Deep Learning with TensorFlow. Explore neural networks with Python
  • Author: Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
  • Original title: Deep Learning with TensorFlow. Explore neural networks with Python
  • ISBN: 9781786460127, 9781786460127
  • Date of issue: 2017-04-24
  • Format: Ebook
  • Item ID: e_15i0
  • Publisher: Packt Publishing