Weather Forecasting with deep learning
$250-750 USD
Pagado a la entrega
We are seeking an experienced data scientist or machine learning engineer to help us develop a long-range weather prediction system using Long Short-Term Memory (LSTM) neural networks. The system will combine LSTM with the Adam optimization technique to improve the accuracy of weather forecasts beyond a week or two. Our goal is to create a prototype for a global system that can provide accurate and reliable global weather forecasts to support industries, emergency management, research, and scientific purposes. The system will be based on TensorFlow and Keras libraries and will be trained and tested on historical weather data from the past 10 years in any country where specific data is available..
Tasks:
Data collection and preprocessing: Acquire and preprocess historical weather data from the past 10 years in Israel.
Model development: Design and implement an LSTM neural network model with Adam optimization using TensorFlow and Keras libraries.
Model training and testing: Train and test the model on the preprocessed historical weather data.
Evaluation and optimization: Evaluate model performance and optimize hyperparameters for better accuracy.
Documentation: Document the development process, model architecture, and evaluation results.
Skills Required:
Strong experience in data science and machine learning
Proficiency in Python and its libraries (TensorFlow, Keras, Pandas, NumPy)
Experience with LSTM neural networks and Adam optimization
Knowledge of time series analysis and weather prediction
Strong problem-solving and analytical skills
Good communication and documentation skills
Nº del proyecto: #36497195
Sobre el proyecto
Adjudicado a:
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