| Description |
1 online resource |
|
text file |
|
PDF |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
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. |
| Summary |
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. You will: Grasp the basic process of neural networks through projects, such as creating music Restore and colorize black and white images with deep learning processes. |
| Subject |
TensorFlow.
|
|
Machine learning.
|
|
Neural networks (Computer science)
|
|
Artificial intelligence.
|
|
Neural Networks, Computer |
|
Machine Learning |
|
Artificial Intelligence |
|
Apprentissage automatique. |
|
Réseaux neuronaux (Informatique) |
|
Intelligence artificielle. |
|
artificial intelligence. |
|
Artificial intelligence. |
|
Machine learning |
|
Neural networks (Computer science) |
| Other Form: |
Print version: Silaparasetty, Vinita. Deep learning projects using TensorFlow 2. [United States] : Apress, 2020 1484258010 9781484258019 (OCoLC)1137807921 |
| ISBN |
9781484258026 (electronic bk.) |
|
1484258029 (electronic bk.) |
| Standard No. |
10.1007/978-1-4842-5802-6 doi |
|
10.1007/978-1-4842-5 |
|
More Resources