In this chapter, we learned about sequential modeling and sequential memory by examining some real-life cases with Google Assistant. We further learned how sequential modeling is related to RNNs. We also learned how RNNs are different from traditional feedforward networks. We learned about the vanishing gradient problem in detail, and learned how using an LSTM is better than a simple RNN to overcome the vanishing gradient problem. We applied the learning to time series problems by predicting stock trends.
In this book, we learned the basics of machine learning and Python, while also gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. We understood the difference between machine and deep learning. We learned how to build a logistic regression model, first with scikit-learn, and then with Keras. We further explored Keras and its different models by creating prediction models for various real-world scenarios, such as disease prediction. Then...
