Neural Networks and deep learning: theory and applications¶
This is the online companion of the Applied Deep Learning 2nd Edition book published by APRESS by Umberto Michelucci with code editing by Michela Sperti.
The book will be published in 2022 by APRESS and will be available as a printed version and in an online form (PDF without any limitations) from the APRESS website. As soon as available the link will be posted here.
About the Book¶
You can find more information on the new book at this page.
Why a 2nd Edition¶
The first verision of Applied Deep Learning (ADL) has been published in 2018. At the time TensorFlow was still in its first version. Today (2022 as I write this) TensorFlow is in its 2.7 version and much has changed. Keras works differently, sessions have disappearead (you know what sessions are if you have been around when TensorFlow version was still 1.X) and developing neural networks have become much easier.
The code in the book needed a complete rehaul. With the help of Michela Sperti we have udpated the code in the book to TensorFlow 2.X compeltely and created this online companion website for you. You can find here a lot of complete examples that are discussed in detail in the book.
If you are intersted you can buy the first edition HERE. Although at this point I would suggest you wait for the 2nd edition to come out. That should happen in Spring 2022.
Some Numbers about the first edition¶
Some Numbers about the 1st Edition
The 1st edition of the book have been downloaded more than 90000 times
The 1st edition of the book has been cited in more than 100 international peer reviewed papers
The 1st edition of the book has been used in PATENT applications
The 1st edition of the book has been cited in papers in a very large number of fields: medicine, drug testing, geology, market analysis, sales forecasting, construction, physics, water treatment systems, cosmology, and many more.
How is this website built?¶
This book is built using the jupyter-book wonderful open source project. You can read this book using any modern browser (chrome and safari have been tested extensively but other browser should work too).
Download the material¶
You can read this book without dowloading any material. But if you prefer, you can download the material locally or open the code parts in Google Colab.
You may notenice that on the top right of the page you will see typically four icons (they may be less in some cases)
The small rocket will allow you to open the code parts in an external environment (typically google colab)
The square symbol will make the page full screen
the GitHub logo will open the repository where the files are (in case you want to download all the notebooks directly)
the download symbol will allow you to download the page you are reading in various formats.
Last Build¶
This book has been built last time on Saturday 29th January 2022.