Single Layer Perceptron SLP

Let’s build a Single Layer Perceptron (SLP) with Python

This article aims to explore the world of perceptrons, focusing in particular on the Single Layer Perceptron (SLP), which, although it constitutes only a small fraction of the overall architecture of deep neural networks, provides a solid basis for understanding the fundamental mechanisms of Deep Learning. We will also introduce practical implementation examples in Python, illustrating how to build and visualize an SLP using libraries such as NumPy, NetworkX and Matplotlib.

Large Language Models

Large Language Models (LLM), what they are and how they work

Large Language Models (LLM) are artificial intelligence models that have demonstrated remarkable capabilities in the field of natural language. They mainly rely on complex architectures that allow them to capture linguistic relationships in texts effectively. These models are known for their enormous size (hence the term “Large”), with millions or billions of parameters, which allows them to store vast linguistic knowledge and adapt to a variety of tasks.

Deep Learning

Deep Learning

Deep learning is a computational technique that allows you to extract and transform data from sources such as human speech or image classification, using multiple layers of neural networks. Each of these layers takes its inputs from the previous layers and refines them, so progressively. The layers are trained by algorithms that minimize their errors and improve their accuracy. In this way the networks learn to perform specific tasks.

WaveNet and Text-To-Speech (TTS) machines can speak m

 WaveNet and Text-To-Speech (TTS) – machines can speak

   The progress of this last year regarding Deep Learning is truly exceptional. Many steps forward have been made in many fields of technology thanks to neural networks and among these there is the synthetic voice, or rather the Text-To-Speech (TTS) that is, that series of technologies able to simulate the human way of speaking by reading a text. Among the models realized, therefore, there is WaveNet, a highly innovative model that has revolutionized the way of doing Text-To-Speech making them jump really forward

2017 the year of Deep Learning frameworks

 2017 The year of Deep Learning frameworks

   2017 was a special year for Deep Learning. In addition to the great experimental results obtained thanks to the algorithms developed, the Deep Learning this year has seen its glory in the release of many frameworks. These are very useful tools for developing numerous projects. In the article you will see an overview of many new frameworks that have been proposed as excellent tools for the development of Deep Learning projects.