From the course: Deep Learning: Getting Started
Join today to access over 25,900 courses taught by industry experts.
The input layer
From the course: Deep Learning: Getting Started
The input layer
In this chapter, we are taking a deep dive into the architecture of an ANN to explore various layers, parameters, and functions that are used in the architecture. We start with the input layer. Let's first start with the concept of vectors. A vector is an ordered list of values. In deep learning, the input is usually represented as a vector of numeric values. A vector can be thought of as a tuple of one or more values. For example, a single input record may contain values such as age, income, or temperature. Vectors are usually defined using a NumPy array when building deep learning applications. This makes it easier to perform mathematical operations efficiently. A vector represents the feature variables or independent variables used for prediction and training. These values are then passed into the neural network as input data. Input data sets that are available from the real world for machine learning contain samples and features. Let's look at an example of a table that contains…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.
