Deep Learning Projects Using TensorFlow 2
Neural Network Development with Python and Keras
(Sprache: Englisch)
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...
Deep learning is...
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Klappentext zu „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
Beginners new to TensorFlow and Python.
Inhaltsverzeichnis zu „Deep Learning Projects Using TensorFlow 2 “
Chapter 1- Perceptrons- Introduction to Perceptrons
- Working of a Perceptron
- Program to understand the working of a Perceptron
Chapter 2: Neural Networks
- Introduction to Neural Networks
- Types of Neural Networks
- How each neural network works
- Program to understand the working of Neural Networks
Chapter 3: Project 1- DJ Neuron
- About the Project: Creating Music Using Neural Networks
- Requirements
- Explanation of concepts used
- Architecture of the Neural Network
- Source code with line by line instructions
Chapter 4: Project 2- Artistic Neurons
- About the Project: Adding colour to black and white images
- Requirements
- Explanation of concepts used
- Architecture of the Neural Network
- Source code with line by line instructions
Chapter 5: Project 3- Go HD
- About the Project: Restoration of images for better quality
- Requirements
- Explanation of concepts used
- Architecture of the Neural Network
- Source code with line by line instructions
Chapter 6: Project 4- Voice Experiments
- About the Project: Voice Manipulation
- Requirements
- Explanation of concepts used
- Architecture of the Neural Network
- Source code with line by line instructions
Chapter 7: Project 5- Imposters
- About the Project: Fake Image Recognition
- Requirements
- Explanation of concepts used
- Architecture of the Neural Network
- Source code with line by line instructions
Chapter 8: Project 6 - Gaming is Fun
- About the Project: MI-agent training using Unity. Learn to create Artificially Intelligent Characters.
Requirements
Explanation of concepts used
Architecture of the Neural Network
Source code with line by line instructions
Autoren-Porträt von Vinita Silaparasetty
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.
Bibliographische Angaben
- Autor: Vinita Silaparasetty
- 2020, 1st ed., XXV, 421 Seiten, Masse: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1484258010
- ISBN-13: 9781484258019
Sprache:
Englisch
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