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  • Book Overview & Buying Hands-On Deep Learning with TensorFlow
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Hands-On Deep Learning with TensorFlow

Hands-On Deep Learning with TensorFlow

By : Dan Van Boxel
2.8 (4)
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Hands-On Deep Learning with TensorFlow

Hands-On Deep Learning with TensorFlow

2.8 (4)
By: Dan Van Boxel

Overview of this book

Dan Van Boxel’s Deep Learning with TensorFlow is based on Dan’s best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With Dan’s guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.
Table of Contents (7 chapters)
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Summary

In this chapter, we walked through the convolutional layer on an example image. We tackled the practical aspects of understanding the convolutions. They can be convoluted but hopefully no longer confusing. We eventually applied this concept to a simple example in TensorFlow. We explored a common partner to convolutions, pooling layers. We explained the workings of max pooling layers, a common convolutional partner. Then, as we progressed, we put this into practice by adding a pooling layer to our example. We also practiced creating a max pooling layer in TensorFlow. We started adding convolutional neural nets to the font classification problem.

In the next chapter, we'll look at models with a time component, Recurrent Neural Networks (RNNs).

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Hands-On Deep Learning with TensorFlow
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