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A Brief Introduction to Neural Networks: A Classification Problem
A practical beginner guide to Neural Networks in Python
In a previous tutorial, I covered the basics of neural networks and provided a simple example of using them for a regression problem. I briefly outlined the general process for working with neural networks. In this tutorial, we will delve deeper and learn how to use neural networks for classification tasks. We will follow the same general pipeline as before. However, if you need more background information on neural networks, I recommend reviewing the previous tutorial where I also briefly discussed the concepts of neurons and multi-layer networks.
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Table of contents
· 1. Introduction
· 2. Problem understanding
· 3. Data preparation and preprocessing
∘ 3.1. Data description
∘ 3.2. Data Transformation
· 4. Model conception
∘ 4.1. A single unit output
∘ 4.2. One-hot output
∘ 4.3. Convolutional neural networks
· 5. Training
∘ 5.1. A single unit output
∘ 5.2. One-hot output
∘ 5.3. Convolution units
· 6. Validation
∘ 6.1 Making predictions
∘ 6.2. Learning curves
∘ 6.3. Evaluation on test set
∘ 6.4. Evaluation metrics
∘ 6.5. Display some data
· 7. Conclusion
