Summary

In this chapter, we learned how to make predictions using TensorFlow. We studied the MNIST dataset and classification of models using this dataset. We came across the elements of DNN models and the process of building the DNN. Later, we progressed to study regression and classification with DNNs. We classified handwritten digits and learned more about building models in TensorFlow. This brings us to the end of this book! We learned how to use ensemble algorithms to produce accurate predictions. We applied various techniques to combine and build better models. We learned how to perform cross-validation efficiently. We also implemented various techniques to solve current issues in the domain of predictive analysis. And, the best part, we used the DNN models we built to solve classification and regression problems. This book has helped us implement various machine learning...