How to solve Binary Classification Problems in Deep Learning with Tensorflow & Keras?
In this tutorial, we will focus on how to select Accuracy Metrics, Activation & Loss functions in Binary Classification Problems. First, we will review the types of Classification Problems, Activation & Loss functions, label encodings, and accuracy metrics. Furthermore, we will also discuss how the target encoding can affect the selection of Activation & Loss functions. Moreover, we will talk about how to select the accuracy metric correctly. Then, for each type of classification problem, we will apply several Activation & Loss functions and observe their effects on performance.
We will experiment with all the concepts by designing and evaluating a deep learning model by using Transfer Learning on horses and humans datasets. In the end, we will summarize the experiment results.
You can access the code at Colab and all the posts of the classification tutorial series at muratkarakaya.net. You can watch all these parts on YouTube in ENGLISH or TURKISH as well.
If you would like to follow up on Deep Learning tutorials, please subscribe to my YouTube Channel or follow my blog on muratkarakaya.net. Do not forget to turn on notifications so that you will be notified when new parts are uploaded.
If you are ready, let’s get started!
Photo by Mitya Ivanov on Unsplash
