Summary

In this chapter, we learned the concept of transfer learning and how is it related to pre-trained networks, and we learned how to use a pre-trained deep learning network. We also learned how to use techniques such as feature extraction and fine-tuning for better use of image classification tasks. We used both the VGG16 and ResNet50 networks. First, we learned how to use an existing model and classify images, and then we learned the powerful technique of tweaking existing models and making it work according to our dataset. This technique for building our own ANN over an existing CNN is one of the most powerful techniques used in the industry.

In the next chapter, we will learn about sequential modeling and sequential memory by looking at some real-life cases with Google Assistant. Further to this, we will learn how sequential modeling is related to Recurrent Neural Networks (RNN). We will learn about the vanishing gradient problem in detail, and how using an LSTM is better than a simple...