Book Image

Hands-On Deep Learning for Images with TensorFlow

By : Will Ballard
Book Image

Hands-On Deep Learning for Images with TensorFlow

By: Will Ballard

Overview of this book

TensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.
Table of Contents (7 chapters)

Summary

In this chapter, we actually covered an awful lot of material. We saw the structure of the classical or dense neural network. We learned about activation and nonlinearity, and we learned about softmax. We then set up testing and training data and we learned how to construct the network with Dropout and Flatten. We also learned all about solvers, or how machine learning actually learns. We then explored hyperparameters, and finally, we fine-tuned our model with grid search.

In the next chapter, we'll take what we've learned and alter the structure of our network to build what is called a convolutional neural network (CNN).