Classical Machine Learning and Deep Learning
Under the umbrella of Classical Machine Learning, we have techniques like regression, used for predicting continuous values like real estate prices; classification, which involves categorizing inputs like distinguishing spam emails; and clustering, which groups inputs based on patterns, like customer segmentation based on purchasing behavior.
Deep Learning, characterized by its use of layered neural networks, autonomously learns the best representation of data. This shift from manual feature extraction in Classical ML to autonomous learning in Deep Learning is significant. It allows Deep Learning to process complex data like images, audio, and text more effectively.
The versatility of Deep Learning is evident in the variety of neural network architectures available, each suited to different tasks. However, it’s not without challenges, including the need for large data sets, significant computational resources, and the opacity of its processes.
In Deep Learning architectures, we have Feedforward Neural Networks, or FNNs, which are the foundation of neural network technology, allowing data to travel in one direction from input to output. Convolutional Neural Networks, or CNNs, are great for visual tasks like image classification, while Recurrent Neural Networks, or RNNs, are designed for sequential data, useful in applications like speech recognition.
A relatively new addition to the Deep Learning family is Transformers, known for their self-attention mechanism, enabling them to understand relationships in input sequences, like words in a sentence. This architecture underpins the revolutionary Generative AI, including large language models like GPT and image generation models like DALL-E.