Tensorflow
This repository is dedicated to the field of Natural Language Processing (NLP) and encompasses various aspects of text analysis and machine learning. Specifically, it places a strong emphasis on the following key areas:
NLP (Natural Language Processing): Using resources, models, and tools tailored for processing and understanding human language, by creating tests to Predict the rest of a sentence or Sentiment Analysis of a word to focuse on determining the emotional tone or sentiment expressed in text. Wether it be a searched token in searchbar or even a news title!
LSTM (Long Short-Term Memory): LSTM is a type of recurrent neural network (RNN) known for its ability to handle sequential data effectively. Here, you can find LSTM models and code implementations, particularly useful for tasks involving time series data or sequential text analysis.
Binary-class and Multi-class Classification: Classification is a fundamental machine learning task where data is categorized into predefined classes or categories. In this repository, you'll discover tools and techniques for both binary-class (two-class) and multi-class (more than two classes) classification tasks. These methods can be applied to a wide range of classification problems.
CNN (Convolutional Neural Network): CNNs are primarily associated with computer vision tasks, but they can also be adapted for text and sequence data analysis. In this context, the repository provides resources and models that utilize CNNs to extract meaningful features from text or sequence data.