Beginning Deep Learning with TensorFlow

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 and RNNs.

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

Chapter 1: Introduction to Artificial Intelligence

Chapter 2: Regression

Chapter 3: Classification

Chapter 4: Basic TensorFlow

Chapter 5: Advanced TensorFlow

Chapter 6: Neural Networks

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 Networks

Chapter 14: Reinforcement Learning

Chapter 15: Customized Dataset

About the Authors

​Liangqu Long is a well-known deep learning educator and engineer in China. He is a successfully published author in the topic area with years of experience in teaching machine learning concepts. His two online video tutorial courses “Deep Learning with PyTorch” and “Deep Learning with TensorFlow 2” have received massive positive comments and allowed him to refine his deep learning teaching methods.

Xiangming Zeng is an experienced data scientist and machine learning practitioner. He has over ten years of experience using machine learning and deep learning models to solve real world problems in both academia and professionally. Xiangming is familiar with deep learning fundamentals and mainstream machine learning libraries such as Tensorflow and scikit-learn.