Deep Convolutional Networks
In this chapter, we continue our pragmatic exploration of the world of deep learning, analyzing deep convolutional networks. Deep convolutional networks represent the most accurate and best performing visual processing technique for almost any purpose. Results like the ones obtained in fields such as real-time image recognition, self-driving cars, and deep reinforcement learning have been possible thanks to the expressivity of this kind of network. Employing this technique together with all the elements discussed in the previous chapters makes it possible to achieve extraordinary results in the fields of video processing, decoding, segmentation, and generation.
In particular, in this chapter, we are going to discuss the following topics:
- Deep convolutional networks
- Convolutions, atrous convolutions
- Separable convolutions, and transpose convolutions
- Pooling and other support layers
At this point, we can start discussing...
