Deep Learning with TensorFlow by Packt
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.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
>>> import tensorflow as tf >>> hello = tf.constant("hello TensorFlow!") >>> sess=tf.Session()
All the examples have been implemented using Python version 2.7 on a Ubuntu Linux 64 bit including the TensorFlow library version 1.0.1. You will also need the following Python modules (preferably the latest version): Pip Bazel Matplotlib NumPy Pandas Preface . Only for Chapter 8, Advanced TensorFlow Programming and Chapter 9, Reinforcement Learning, you will need the following frameworks: Keras Pretty Tensor TFLearn OpenAI gym
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