In this chapter, we explained what deep learning is and how it's related to deep neural networks. We discussed the different types of networks and how to train them. We also mentioned many real-world applications of deep learning and tried to analyze the reasons for its efficiency. Finally, we introduced three of the most popular deep learning libraries, namely, TensorFlow, Keras and PyTorch. We also implemented a couple of examples with Keras, but we hit a low accuracy ceiling when we tried to classify the CIFAR-10 dataset.
In the next chapter, we'll discuss how to improve these results with the help of convolutional networks – one of the most popular and effective deep network models. We'll talk about their structure, building blocks, and what makes them uniquely suited to computer vision tasks. To spark your interest, let's recall that convolutional...
