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Hands-On Neural Networks

You're reading from   Hands-On Neural Networks Learn how to build and train your first neural network model using Python

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788992596
Length 280 pages
Edition 1st Edition
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Authors (2):
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Leonardo De Marchi Leonardo De Marchi
Author Profile Icon Leonardo De Marchi
Leonardo De Marchi
Laura Mitchell Laura Mitchell
Author Profile Icon Laura Mitchell
Laura Mitchell
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Getting Started FREE CHAPTER
2. Getting Started with Supervised Learning 3. Neural Network Fundamentals 4. Section 2: Deep Learning Applications
5. Convolutional Neural Networks for Image Processing 6. Exploiting Text Embedding 7. Working with RNNs 8. Reusing Neural Networks with Transfer Learning 9. Section 3: Advanced Applications
10. Working with Generative Algorithms 11. Implementing Autoencoders 12. Deep Belief Networks 13. Reinforcement Learning 14. Whats Next? 15. Other Books You May Enjoy

Environment setup

There are only a few viable programming language options when creating ML software. The most popular ones are Python and R, but Scala is also quite popular. There are other languages, but the better ones in terms of use in ML are Julia, JavaScript, Java, and a few others. In this book, we will be using Python only. The motivation behind this choice is its widespread adoption, its simplicity of use, and the vast ecosystem of libraries that are possible to use.

In particular, we will be using Python 3.7 and a few of its following libraries:

  • numpy: For fast vectorized numerical computation
  • scipy: Built on top of numpy, with many mathematical functionalities
  • pandas: For data manipulation
  • scikit-learn: The main Python library for ML
  • tensorflow: The engine that powers our deep learning algorithms
  • keras: The library we are going to use to develop our deep learning...
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