Deep Learning Projects Using TensorFlow 2

Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications.
Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to deep fakes and so much more. It is crucial for aspiring data scientists or those who want to enter the field of AI to understand deep learning concepts.
The best way to learn is by doing. You'll develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. You'll also work with Neural Networks and other deep learning concepts. By the end of the book, you'll have a collection of unique projects that you can add to your GitHub profiles and expand on for professional application.
What You'll Learn

• Grasp the basic process of neural networks through projects, such as creating music

• Restore and colorize black and white images with deep learning processes Who This Book Is For Beginners new to TensorFlow and Python.

  Study diagrams, tables, flowcharts, and other such visual aids to interact visually with deep learning information Troubleshoot deep learning projects Work through deep learning projects line-by-line to understand the concepts and build no them

  Autorentext

  Vinita Silaparasetty is a Data Scientist at Trendwise Analytics. Deep Learning is a topic she's passionate about, and she has experience working on deep learning projects and experimenting with neural networks. She aspires to share her love for deep learning with beginners and make it simple and easy to understand, so as to ignite a similar passion in them.

  Zusammenfassung
  Work through engaging and practical deep learning projects using TensorFlow 2.0.

  Inhalt
  Chapter 1: Getting Started: Installation and Troubleshooting.- Chapter 2: Perceptrons.- Chapter 3: Neural Networks.- Chapter 4: Sentiment Analysist.- Chapter 5: Music Generation.- Chapter 6: Image Colorization.- Chapter 7: Image Deblurring.- Chapter 8: Image Manipulation.- Chapter 9: Neutral Network Collection.- Appendix: Portfolio Tips.