Service Classification based on Service Description
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Updated
Oct 17, 2021 - Jupyter Notebook
Service Classification based on Service Description
Forecasting Bitcoin Prices via ARIMA, XGBoost, Prophet, and LSTM models in Python
This project aims to study the Image Colorization problem and implement a Convolutional Neural Network that is able to colorize black and white images using CIELAB color space.
Music generation using a Long Short-Term Memory (LSTM) neural network. The gennhausser project uses TensorFlow and music21 libraries to create a synthetic dataset, train an LSTM model, and generate music sequences.
Specialized LLM / LSTM models
Using Deep Learning to Categorize Music through Spectrogram Analysis
Create Music with Machine Learning!
A computer vision model for Indian Sign Language Recognition
The goal of this project is to accurately predict the future closing value of a given stock across a given period of time in the future.
LSTM and all other supporting modules are used to predict the next word based on the previous five words.
Repo for the Deep Learning Specialization offered by Coursera
The goal of the project is to predict chickenpox cases one year ahead based on known history. Methods used: ETS decomposition and SARIMA with statsmodels, LSTM with Keras, MINMAX scaling.
🚀 Unveiling Stock Market Insights with RNNs: A concise exploration of LSTM and GRU models for stock price prediction, featuring a research paper and Jupyter Notebook. 💹📈
deep learning: prediction de sentiment associé à un tweet
Сентиментальный анализ рынка акции
📈 Experimenting with possible closing price predicition using automated systems
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