General Deep Learning Methods(ANN-CNN-RNN-LSTM)

In this section, we will use general deep learning methods (ANN-CNN-RNN-LSTM) on data sets.

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1. Artificial Neural Network(ANN)

📌 Neural networks, also known as artificial neural networks (ANN) or simulated neural networks (SNN), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

2. Convolution Neural Network(CNN)

📌 A convolutional neural network (CNN) is a type of artificial neural network used primarily for image recognition and processing, due to its ability to recognize patterns in images. A CNN is a powerful tool but requires millions of labelled data points for training.

3. Recurrent Neural Network(RNN)

📌 A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes can create a cycle, allowing output from some nodes to affect subsequent input to the same nodes. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable-length sequences of inputs.

4. Long Short Term Memory(LSTM)

📌 Long short-term memory (LSTM)[1] is an artificial neural network used in the fields of artificial intelligence and deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. Such a recurrent neural network (RNN) can process not only single data points (such as images) but also entire sequences of data (such as speech or video). This characteristic makes LSTM networks ideal for processing and predicting data.