- Paperback: 252 pages
- Publisher: O'Reilly Media; 1 edition (June 23, 2018)
- Language: English
- ISBN-10: 149199584X
- ISBN-13: 978-1491995846
- Product Dimensions: 7 x 0.8 x 9.2 inches
- Shipping Weight: 15.8 ounces (View shipping rates and policies)
- Average Customer Review: 4 customer reviews
- Amazon Best Sellers Rank: #152,915 in Books (See Top 100 in Books)
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Deep Learning Cookbook: Practical Recipes to Get Started Quickly 1st Edition
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From the Preface
What Do You Need to Know?
These days there is a wide choice of platforms, technologies, and programming languages for deep learning. In this book all the examples are in Python and most of the code relies on the excellent Keras framework. The example code is available on GitHub as a set of Python notebooks, one per chapter. So, having a working knowledge of the following will help:
Python -- Python 3 is preferred, but Python 2.7 should also work. We use a variety of helper libraries that all can easily be installed using pip. The code is generally straightforward so even a relative novice should be able to follow the action.
Keras -- The heavy lifting for machine learning is done almost completely by Keras. Keras is an abstraction over either TensorFlow or Theano, both deep learning frameworks. Keras makes it easy to define neural networks in a very readable way. All code is tested against TensorFlow but should also work with Theano.
NumPy, SciPy, scikit-learn -- These useful and extensive libraries are casually used in many recipes. Most of the time it should be clear what is happening from the context, but a quick read-up on them won’t hurt.
Jupyter Notebook -- Notebooks are a very nice way to share code; they allow for a mixture of code, output of code, and comments, all viewable in the browser.
Each chapter has a corresponding notebook that contains working code. The code in the book often leaves out details like imports, so it is a good idea to get the code from Git and launch a local notebook.
How This Book Is Structured
Chapter 1 provides in-depth information about how neural networks function, where to get data from, and how to preprocess that data to make it easier to consume. Chapter 2 is about getting stuck and what to do about it. Neural nets are notoriously hard to debug and the tips and tricks in this chapter on how to make them behave will come in handy when going through the more project-oriented recipes in the rest of the book. If you are impatient, you can skip this chapter and go back to it later when you do get stuck.
Chapters 3 through 15 are grouped around media, starting with text processing, followed by image processing, and finally music processing in Chapter 15. Each chapter describes one project split into various recipes. Typically a chapter will start with a data acquisition recipe, followed by a few recipes that build toward the goal of the chapter and a recipe on data visualization.
Chapter 16 is about using models in production. Running experiments in notebooks is great, but ultimately we want to share our results with actual users and get our models run on real servers or mobile devices. This chapter goes through the options.
About the Author
Douwe Osinga is an experienced Software Engineer, formerly with Google, and founder of three startups. He maintains a popular software project website, partly focused on machine learning(https://douweosinga.com/projects/machine_learning).
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Highly highly recommend if you want to bring your deep learning from 0 to 60 fast!