Nowadays, deep learning is the most popular and fastest growing area in machine learning. Deep learning has been surpassing traditional approaches for machine learning applications since 2012. This is the reason why a lot of deep learning architectures have been applied to a great number of fields, including computer vision. Common applications of deep learning include automatic speech recognition, image recognition, visual art processing, natural language processing, recommendation systems, bioinformatics, and image restoration. Most modern deep learning architectures are based on an artificial neural network, and the deep in deep learning refers to the number of layers of the architecture.
In this chapter, you will be introduced to deep learning by looking at the differences with traditional machine learning approaches, which were covered in the Chapter 10, Machine Learning with OpenCV . Additionally, you will see some of the common deep learning architectures applied to both image classification and object detection. Finally, two deep learning Python libraries (TensorFlow and Keras) will be introduced.
More specifically, the following topics will be tackled in this chapter:
- Deep learning overview for computer vision tasks
- Deep learning in OpenCV
- TensorFlow library
- Keras library
In this chapter, you will be introduced to the world of deep learning with OpenCV, and also with some deep learning Python libraries (TensorFlow and Keras). In Chapter 13, Mobile and Web Computer Vision with Python and OpenCV, you will learn how to create computer vision and deep learning web applications.