Book Image

Hands-On Deep Learning with TensorFlow

By : Dan Van Boxel
Book Image

Hands-On Deep Learning with TensorFlow

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 (12 chapters)

Convolutional layer motivation

In this section, we're going to walk through using a convolutional layer on an example image. We'll graphically see how convolution is just a sliding window. Further we'll learn how to extract multiple features from a window as well as accept multiple layers of input to a window.

In a classic dense layer of a neural network for a given neuron every input feature gets its own weight.

This is great if the input features are totally independent and measure different things, but what if there is structure among your features. The easiest example to imagine this happening is if your input features are pixels from an image. Some pixels are next to each other, others are far away.

For a task like image classification, and font classification especially, it often doesn't matter where a small scale feature occurs in an image. We can look for small scale features in a larger image by sliding a smaller window throughout the image, and this is key to using the same weight...