Documents and Reference Papers for our Final Year Project
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Updated
Jul 16, 2024 - Jupyter Notebook
Documents and Reference Papers for our Final Year Project
Forecast Algorithm Comparison in Python
AI-Powered Stock Price Prediction Using LSTM Networks.
LSTM Chatbot 🤖from scratch
Fake News Classification using Bidirectional LSTM
Crypto & Stock* price prediction with regression models.
GoogleNet Architecture is trained on CIFAR10 Dataset and LSTM Architecture is trained on IBM Stocks Dataset
This code trains a chatbot utilizing technologies such as Pandas and Pickle to load, preprocess, and vectorize datasets, ensuring they are ready for model training and evaluation, uses keras and Tokenizer to design and implement a deep learning model using LSTM
This project is a comprehensive collection of machine learning algorithms implemented from scratch, accompanied by detailed documentation on the underlying mathematics.
Дискорд бот для обнаружения нецензурной лексики в пользовательских сообщениях
BitPredictor - A cutting-edge machine learning-based solution for predicting cryptocurrency prices. Harnessing the power of advanced algorithms and data analysis techniques, this system aims to provide accurate and timely forecasts for Bitcoin and other cryptocurrencies.
Template Implementation of an RNN with LSTM units for stock price prediction
A demonstration application for LSTM based time series forecasting with NOAA temperature data. Dataset organized with MongoDB, model performance analyzed with SQL (MySQL), REST API created with FastAPI, and application containerized with Docker
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
Self-Created Tools to convert ONNX files (NCHW) to TensorFlow/TFLite/Keras format (NHWC). The purpose of this tool is to solve the massive Transpose extrapolation problem in onnx-tensorflow (onnx-tf). I don't need a Star, but give me a pull request.
The projects for the NLP course at the University of Isfahan.
This project is dedicated to implementing various machine learning algorithms from scratch to gain a deeper understanding of how they work.
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