Beginning Deep Learning with TensorFlow

Work with Keras, MNIST Data Sets, and Advanced Neural Networks

Liangqu Long, Xiangming Zeng

• Publisher: Apress
• Year: 2022
• ISBN:
  9781484279151
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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 learners.
You'll start with an introduction to AI, where you'll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you'll jump into simple classification programs for hand-writing analysis. Once you've tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you'll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs andRNNs.
Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! What You'll Learn

• Develop using deep learning algorithms
  
• Build deep learning models using TensorFlow 2
  
• Create classification systems and other, practical deep learning applications
  

Who This Book Is For
Students, programmers, and researchers with no experience in deep learning who want to build up their basic skillsets. Experienced machine learning programmers and engineers might also find value in updating their skills.

Table of contents

• Cover
• Front Matter
• 1. Introduction to Artificial Intelligence
• 2. Regression
• 3. Classification
• 4. Basic TensorFlow
• 5. Advanced TensorFlow
• 6. Neural Networks
• 7. Backward Propagation Algorithm
• 8. Keras Advanced API
• 9. Overfitting
• 10. Convolutional Neural Networks
• 11. Recurrent Neural Network
• 12. Autoencoder
• 13. Generative Adversarial Networks
• 14. Reinforcement Learning
• 15. Customized Dataset
• Back Matter