Beginning Deep Learning with TensorFlow : Work with Keras, MNIST Data Sets, and Advanced Neural Networks /

Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learn...

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Bibliographic Details
Main Authors:	Long, Liangqu (Author), Zeng, Xiangming (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	Berkeley, CA : Apress : Imprint: Apress, 2022.
Edition:	1st ed. 2022.
Subjects:	Machine learning. Machine Learning. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Chapter 1: Introduction to Artificial Intelligence
Chapter 2. Regression
Chapter 3. Classification
Chapter 4. Basic Tensorflow
Chapter 5. Advanced Tensorflow
Chapter 6. Neural Network
Chapter 7. Backward Propagation Algorithm
Chapter 8. Keras Advanced API
Chapter 9. Overfitting
Chapter 10. Convolutional Neural Networks
Chapter 11. Recurrent Neural Network
Chapter 12. Autoencoder
Chapter 13. Generative Adversarial Network (GAN)
Chapter 14. Reinforcement Learning
Chapter 15. Custom Dataset.