This website deals with scientific and technological topics on computation in general and pays particular attention to deep learning and therefore to neural networks, to which a special section is dedicated; there is also a specific section on quantum computing.
The technologies used for neural networks are TensorFlow and PyTorch with Python 3.x and Flux with Julia 1.x; as a rule, whenever possible, the code relating to case studies on neural networks is written for both technologies.
With regard to quantum computing, the technologies involved are usually three: QASM on IBM Quantum Computing, Qiskit always on IBM Quantum Computing and the Q# language of Microsoft on .NET Core; here too, whenever possible, the code relating to case studies on quantum computing is written with these three technologies.
On computation in general numerical calculation issues are addressed; also it addresses other issues like numerical solutions to complex mathematical problems, operational research algorithms and other scientific and technological problems on the border between mathematics and computer science.
Topics are presented in post form; the website is constantly updated but the publication of new posts does not follow any regular periodicity. The texts, the code and any other material present here are original; the code is normally published on GitHub under MIT license.
The code is not to be considered professional as it was not written to solve complex real-world cases, but was designed and written only for dissemination, study or experimentation purposes.
This website is closely related to the following systems:
- My web space on GitHub which hosts the code of the programs dealt in the posts.
- The FaceBook page Computational Mindset which informs, in English, about the new posts published on this website.
- The FaceBook page Mentalità Computazionale which informs, in Italian, about the new posts published on this website.
- The YouTube channel for multimedia content.
- My social media channels available on Link Tree