This is the code repository for Hands-On Neural Networks with TensorFlow 2.0, published by Packt.
Understand TensorFlow, from static graph to eager execution, and design neural networks
This book is a guide to the TensorFlow (TF) framework, from the static graph architecture of TF 1.x to the eager execution and all the new features introduced in TF 2.0. Neural Networks applications are developed throughout the book with the aim of making the reader capable of developing neural networks-based solutions to real problems using TF 2.0
This book covers the following exciting features:
- Grasp machine learning and neural network techniques to solve challenging tasks
- Apply the new features of TF 2.0 to speed up development
- Use TensorFlow Datasets (tfds) and the tf.data API to build high-efficiency data input pipelines
- Perform transfer learning and fine-tuning with TensorFlow Hub
- Define and train networks to solve object detection and semantic segmentation problems
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example,
The code will look like the following:
writer = tf.summary.FileWriter("log/two_graphs/g1", g1)
writer = tf.summary.FileWriter("log/two_graphs/g2", g2)
writer.close()
Following is what you need for this book: If you're a developer who wants to get started with machine learning and TensorFlow, or a data scientist interested in developing neural network solutions in TF 2.0, this book is for you. Experienced machine learning engineers who want to master the new features of the TensorFlow framework will also find this book useful. Basic knowledge of calculus and a strong understanding of Python programming will help you grasp the topics covered in this book.
With the following software and hardware list you can run all code files present in the book (Chapter 3-10).
| Chapter | Software required | OS required |
|---|---|---|
| 3-10 | Python 3.6, Anaconda, TensorFlow 2.0 | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
- Page 10 (Figure caption): Venn diagram representing how a dataset should be divided no overlapping among the training, validation, and test sets is required should be Venn diagram representing how a dataset should be divided; no overlap among the training, validation, and test sets is required
- Page 67 (Paragraph 3, line 7): Convolution operations requires fewer parameters should be Convolution operations require fewer parameters
- Page 202 (Paragraph 6, line 1): TensorFlow Hub is a library we can browse while a looking for a pre-trained model that best fits our needs should be TensorFlow Hub is a library we can browse while looking for a pre-trained model that best fits our needs
Paolo Galeone is a computer engineer with strong practical experience. After getting his MSc degree, he joined the Computer Vision Laboratory at the University of Bologna, Italy, as a research fellow, where he improved his computer vision and machine learning knowledge working on a broad range of research topics. Currently, he leads the Computer Vision and Machine Learning laboratory at ZURU Tech, Italy. In 2019, Google recognized his expertise by awarding him the title of Google Developer Expert (GDE) in Machine Learning. As a GDE, he shares his passion for machine learning and the TensorFlow framework by blogging, speaking at conferences, contributing to open-source projects, and answering questions on Stack Overflow.
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