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

 

How to solve Multi-Class Classification Problems in Deep Learning with Tensorflow & Keras?

This is the third part of the “How to solve Classification Problems in Keras?” series. If you have not gone over Part A and Part B, please review them before continuing with this tutorial. The link to all parts is provided below.

In this tutorial, we will focus on how to solve Multi-Class Classification Problems in Deep Learning with Tensorflow & Keras. First, we will download the MNIST dataset. In multi-class classification problems, we have two options to encode the true labels by using either:

• integer numbers, or
• one-hot vector

We will experiment with both encodings to observe the effect of the combinations of various last-layer activation functions and loss functions on a Keras CNN model’s performance. In both experiments, we will discuss the relationship between Activation & Loss functions, label encodings, and accuracy metrics in detail.

We will understand why sometimes we could get surprising results when using different parameter settings other than the generally recommended ones. As a result, we will gain insight into activation and loss functions and their interactions. In the end, we will summarize the experiment results in a cheat table.

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 Debby Hudson on Unsplash

 

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

Fundamentals of Classification by Deep Learning with Tensorflow & Keras

In this post, we will focus on fundamental concepts for solving Classification Problems by 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.

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. 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 Eilis Garvey on Unsplash

 

Multi-Class Text Classification with a GPT3 Transformer block: An End-to-End Example

Author: Murat Karakaya & Cansen Çağlayan
Date created: 05 Oct 2021
Last modified: 19 Oct 2021
Description: This tutorial has 2 parts as explained below. Part A: Data Analysis & Text Preprocessing and Part B: Text Classification. 

                                       Photo by Håkon Grimstad on Unsplash

 

How to Evaluate a Classifier Trained with an Imbalanced Dataset? Why Accuracy is not Enough?

Author: Murat Karakaya
Date created: 19 May 2020
Last modified: 09 Dec 2021
Description: In this tutorial series, we will discuss how to evaluate a classifier trained with an imbalanced dataset. We will see that accuracy metric is not enough to measure the performance of classifiers, especially, when you have an imbalanced dataset. Furthermore, we will implement 8 different classifier models and evaluate their success by comparing the various classification metric results. We will implement the solutions by Python and SciKit Learn library.

Accessible on:

• Murat Karakaya Akademi YouTube Channel in English or Turkish
• muratkarakaya.net
• Google Colab
• Kaggle
• Github
• Github pages
• Github Repo

Parts

I will deliver the content in 3 parts:

• Part A: Fundamentals, Metrics, Synthetic Dataset
• Part B: Dummy Classifiers, Accuracy, Precision, Recall, F1
• Part C: ROC, AUC, Worthless Test, Setting up threshold

Photo by Aziz Acharki on Unsplash