A Virtual Assistant with Python and Natural Language Processing

23 Apr 2023

This post demonstrates how to create a simple virtual assistant using Python, Natural Language Processing, and speech recognition. We cover the basics of language modeling and text-to-speech conversion, providing an example of how to match user commands with predefined responses. The end result is a fun and practical project that showcases the power of NLP in programming.

Word Embedding

14 Dec 2021

Word embedding maps words and phrases to numerical vectors, reducing data dimensionality by converting high-dimensional words to a limited number. Preprocessing in NLP extracts important words and reduces dimensions using techniques such as stemming, lemmatization, and removal of stop words. Word2Vec neural network architecture calculates coordinates for each word, and vector combination can transform states to other states. Examples are provided in the link to the code.

Latent Semantic Analysis

14 Dec 2021

Latent Semantic Analysis (LSA) is a Topic Modeling technique that extracts topics from text by using Singular Value Decomposition (SVD) of the matrix. LSA reduces computational costs and noise by using only the words with higher semantic weight. It is implemented using Python and the Scikit-learn library.

Solving the traveling salesman problem using genetic algorithm

2 Dec 2021

In this implementation of the Genetic Algorithm (GA) for the Traveling Salesman Problem (TSP), the algorithm is used to find the shortest path that visits all cities in a given list. The implementation includes various functions such as Cross, Mutation, and Selection, and demonstrates the use of the Roulette Wheel Selection method to select parents for breeding. The results show that the GA can effectively solve the TSP and find optimal solutions.

Impact of Hysteresis System on Signals

6 Nov 2020

Learn about the concept of hysteresis and its effects on signals in various fields, including physics, chemistry, engineering, biology, and economics. This post includes a Python function for simulating hysteresis on different signal types and displays the input and output signals in separate charts. Check out the code in the link provided.