Other types of Neural Networks

Neural networks have revolutionized the field of machine learning and AI, providing solutions to problems previously deemed challenging or even unsolvable. There are many neural network architectures, each tailored for specific types of tasks.

Feedforward Neural Networks (FNN) or Multi-layer Perceptrons (MLP)

This is a classic NN architecture, a direct extension of the single-layer perceptron to multiple layers. These are the foundational architectures upon which most other neural network types are built. It is the architecture that we have considered in this course.

Convolutional Neural Networks (CNN)

CNNs are especially powerful for tasks like image processing (problems such as image classification, image segmentation, etc.) because they're designed to automatically and adaptively learn spatial hierarchies of features.

They use convolutional layers to filter inputs for useful information. These convolutional layers can capture the spatial features of an image like edges, corners, textures, etc. While their main success has been in the field of image classification, they have other applications as well.

Recurrent Neural Networks (RNN)

RNNs have loops to allow information persistence. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs, making them extremely useful for time series or sequential data. They're broadly used for sequence prediction problems, like natural language processing or speech recognition.

Variants of RNNs

1. Long Short-Term Memory (LSTM): Overcomes the vanishing gradient problem of RNNs, making it easier to learn from long-term dependencies;
2. Gated Recurrent Units (GRU): A simpler and more efficient variant of LSTM. However, it learns complex patterns in the data worse than LSTM.

Libraries for Deep Learning

Training deep neural networks requires more than the classic machine learning library scikit-learn offers. The most commonly used libraries for working with deep neural networks are TensorFlow and PyTorch. Here are the main reasons why they are preferred for this task:

1. Performance and Scalability: TensorFlow and PyTorch are designed specifically for training models on large amounts of data and can run efficiently on graphics processing units (GPUs), which speeds up training;

2. Flexibility: Unlike scikit-learn, TensorFlow and PyTorch allow you to create arbitrary neural network architectures, including recurrent, convolutional, and transformer structures;

3. Automatic Differentiation: One of the key features of these libraries is the ability to automatically compute gradients, which is essential for optimizing weights in neural networks.