How to solve Classification Problems in Deep Learning with Tensorflow & Keras?
Today, we will focus on how to solve Classification Problems in Deep Learning with Tensorflow & Keras.
When we design a model in Deep Neural Networks, we need to know how to select proper label encoding, Activation, and Loss functions, along with accuracy metrics according to the classification task at hand.
Thus, in this tutorial, we will first investigate the types of Classification Problems. Then, we will see the most frequently used label encodings in Keras. We will learn how to select Activation & Loss functions according to the given classification type and label encoding. Moreover, we will examine the details of accuracy metrics in TensorFlow / Keras.
At the end of the tutorial, I hope that we will have a good understanding of these concepts and their implementation in Keras.
Contents:
- types of Classification Problems,
- possible label encodings,
- Activation & Loss functions,
- accuracy metrics
Furthermore, we will also discuss how the target encoding can affect the selection of Activation & Loss functions.
If you would like to learn more about Deep Learning with practical coding examples, please subscribe to my YouTube Channel or follow my blog on Blogger. Do not forget to turn on Notifications so that you will be notified when new parts are uploaded.
You can access this Colab Notebook using the link given in the video description below.
If you are ready, let’s get started!
Photo by Deon Black on Unsplash
