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A two-dimensional neural network, often referred to as a 2D neural network or a 2D convolutional neural network (CNN), is a type of artificial neural network architecture specifically designed for processing and analyzing two-dimensional data, such as images and video frames. It is an extension of the traditional one-dimensional neural networks used for tasks like text and sequence processing.

Text classification, also known as document classification or supervised text categorization, involves assigning predefined labels or categories to text documents based on their content. The goal is to train a model to recognize patterns and associations between the content of documents and the appropriate labels. To do this, you need a labeled dataset where each document is associated with its correct category or label.

Key Points:

• Supervised learning: Requires labeled training data.
• Documents are assigned to specific predefined categories.
• Ground truth labels are needed for training and evaluation.
• Common algorithms include Naive Bayes, Support Vector Machines (SVM), and deep learning approaches like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).
• Example use case: Categorizing emails as spam or not spam.

Convolutional neural networks (CNNs) [2-3] show a way to reduce the number of connections by having each hidden unit only be responsible for a small local neighborhood of visible units.

The basic CNN structure is:
         i) Convolution.
         ii) Pooling.
         iii) Convolution.
         iv) Pooling.
         v) Fully connected layer.
         vi) Output.

In the wafer pattern classification with WM-811K done by Yunseon Byun and Jun-Geol Baek, [1] each pixel in the images represented a die on wafer maps. After testing, the normal chip was represented as 1, and the defective chip was represented as 2. Although the shape of the wafer was a circle, the inputs of the convolutional neural network had to be square. Therefore, the empty pixel was represented as 0. The goal of the experiment was to classify the defective patterns.

Max-pooling is used for several reasons in convolutional neural networks: 

1.  Translation Invariance: 

   Max-pooling helps to make the representation of an object invariant to small translations. The network becomes less sensitive to the precise location of features. 

2.  Reducing Dimensionality: 

   By selecting only the maximum values, max-pooling reduces the spatial dimensions of the input, which can help reduce the computational complexity of subsequent layers and prevent overfitting. 

3.  Feature Selection: 

   The maximum value in each region represents the most activated feature, helping to retain important information and discard less relevant details.

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[1] Yunseon Byun and Jun-Geol Baek, Mixed Pattern Recognition Methodology on Wafer Maps with Pre-trained Convolutional Neural Networks, 2022.
[2] Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to ́document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.
[3] Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 609–616. ACM, 2009.