An RNN sentiment classifier for Yelp reviews
-
Updated
May 9, 2023 - Jupyter Notebook
An RNN sentiment classifier for Yelp reviews
A deep learning project that uses ice cream brands to make recommendation based on data.
In this model ,I have used the GLUE dataset in order to perform a sentiment analysis on the reviews as positive and negative. Different types of Neural Networks using TensorFlow was tried out such as CNN,GRU and bi-directional LSTMs. The accuracy of predictions of each of these models are visualised in the end
Usage of NN in order to classify the setiment of YouTube comments.
An Image Caption Generator which generates a caption describing the given image.
Seq2Seq attention based speech transcription system using pyramidal Bi-LSTMS .
Subset of Keras functionality implemented in pure numpy 🦝
End-to-end approach for recognizing people signatures
This project implements a sequence-to-sequence (Seq2Seq) model using an RNN-based approach for machine translation from Urdu to English. The model utilizes PyTorch and processes a dataset of parallel Urdu-English sentences.
A simple deep learning repository covering ANN, CNN, Transfer Learning, and Sequence Models (RNN, LSTM, GRU) with easy explanations of core concepts.
Piano music generation using recurrent neural network (RNN) model
Sentiment analysis model for beta testing as a possibility to improve project management cycle in IT
This project classifies stocks based on tick-by-tick order-book data, as part of the CFM challenge. The model uses GRU layers to process sequential market data and leverages feature engineering on prices, bid/ask sizes, and order types. It’s trained with cross-entropy loss using the Adam optimizer.
The Ikirundi Corpus Project aims to create a comprehensive collection of Kirundi language resources to support and facilitate a wide range of natural language processing (NLP) tasks.
[CS763 - IIT Bombay] All my submissions in the coursework (★)
This project involved analyzing Amazon user reviews to determine the sentiment expressed (positive, negative, or neutral) using Recurrent Neural Networks (RNNs). The project leveraged deep learning techniques for text classification, processing and transforming the raw text data into a format suitable for RNN input.
Forecasting data for cryptocurrency using streamlit web app.
An image captioning model that uses flickr8k dataset with Deep learning and NLP
Add a description, image, and links to the reccurent-neural-network topic page so that developers can more easily learn about it.
To associate your repository with the reccurent-neural-network topic, visit your repo's landing page and select "manage topics."