Sections

Neural Networks

Studies, experiments and examples about deep learning machine models based on different topologies of neural networks: multilayer perceptrons, convolutional and recurrent layers, long-short-term-memory cells. Applications of neural networks to fit mathematical objects, to analyze texts, images, sounds and videos, to search for recurrent patterns in numerical series. Code strictly original written in Python 3 with TensorFlow and/or PyTorch, working and freely available on GitHub.
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Quantum Computing

Studies, experiments and examples about programs written for quantum computers and correspondent simulators. Algorithms that uses combinations of quantum gates, qubit superposition, entanglement, measure collapse. Analysis of results got from the execution of programs on true quantum computers. Code strictly original written in most common languages for the quantum programming like QASM, Q# and Python con Qiskit, working and freely available on GitHub.
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Highlights

Forecast of a univariate equally spaced time series with TensorFlow

Forecast of a univariate equally spaced time series with TensorFlow

Forecast of univariate and equally spaced time series via various neural network taxonomies implemented with TensorFlow without writing code but only via command line. More...

Fitting with highly configurable multi layer perceptrons

Fitting with highly configurable multi layer perceptrons

Fitting with highly configurable multi layer perceptrons (MLP) of functions, curves and surfaces with TensorFlow and PyTorch. More...

'Time Series' dataset collection

'Time Series' dataset collection

Collection of synthetic datasets generated by applying functions to the values of a sequence representing time; there are datasets done with scalar and vectorial functions and/or univariate and multivariate functions. Datasets are in .csv format with header (each header column corresponds to a name of a variable) and in some case there is also the .arff format (for Weka). More...

Fitting functions with a configurable XGBoost regressor

Fitting functions with a configurable XGBoost regressor

The XGBoost algorithm, known for winning numerous Kaggle competitions, gives incredible results in the fitting of functions; the results are extremely exciting both in terms of error metrics and performance. More...

NOT and C-NOT quantum gates

NOT and C-NOT quantum gates

This post deals with the topic of quantum gates NOT (X-Pauli) and C-NOT (Controlled NOT) and the underlying quantum phenomena involved; also illustrates the use of these gates by a high level language and finally it shows the respective behaviours both in the case of pure states only and in the presence of superposition states. More...

Random Numbers Generation

Random Numbers Generation

This post shows how to exploit the unpredictability of the measure of a qubit in an superposition state to generate a random number. More...