The deep learning architecture named Transformer first appeared in the literature 2017. You can check out the publication by clicking on this hyperlink: Attention Is All You Need — 2017 Conceived to overcome the limitations of the natural language processing models of the day, the Transformer architecture went from making text translation and next-word prediction more accurate, to inspiring the development of chatGPT (OpenAI). (GPT stands for Generative Pre-trained Transfo

In my previous blogpost, I explained what recurrent neural networks (RNNs) are. To read more about this topic, click on the following hyperlink: RECURRENT NEURAL NETWORKS RNNs are a specialized form of the classic feed forward network, which I explained in WHAT IS AN ARTIFICIAL NEURAL NETWORK? . Used to translate words or summarize text, RNNs ingest sequential inputs, such as the words in a sentence or paragraph ( Figure 1 ). Figure 1.  RNNs are feedforward networks with a hi

In this blogpost I talk to you about convolutional neural networks ( CNNs ), a specialized deep learning architecture used in computer vision. When processing images, CNNs outperform feedforward networks (FNNs) because they overcome the curse of dimensionality . FNNs assign an artificial neuron to each pixel in an image, and, because FNNs are densely interconnected, the number of neurons required to process a large image increases disproportionally (multidimensionally). This

ARTIFICIAL NEURAL NETWORKS Artificial neural networks , also known as deep learning networks, are a type of machine learning algorithm, which can extract exceedingly complex features from input data. Examples of input data features are the words in a sentence or the patterns in an image. In analogy to the huma n brain’s cognitive processes, deep learning networks acquire the ability to identify feature patterns ( learn )  by modulating the activation state of the artificial n

I provide in this post a high-level introduction to artificial neural networks in the context of MS-proteomics.