Temperature Forecasting Using Deep Learning (LSTM) In Python смотреть онлайн

In the video, we demonstrate step-by-step instructions on how to build an LSTM model for temperature forecasting. Here's a breakdown of the main components covered in the video:

Data Loading: You start by loading the temperature dataset from a CSV file using the Pandas library.

Data Preprocessing: The data is normalized using the MinMaxScaler from the scikit-learn library to scale the temperature values between 0 and 1. This step is crucial for better training performance and convergence of the LSTM model.

Data Preparation: The normalized data is split into training and testing sets. The training data is further prepared by creating input-output sequences for the LSTM model using a sliding window approach.

LSTM Model Building: You create an LSTM model using the Keras library. The model architecture consists of an LSTM layer followed by a dense layer to output the predicted temperature values. The model is compiled using the Adam optimizer and mean squared error (MSE) loss function.

Model Training: The LSTM model is trained using the training data. You specify the number of epochs and batch size for training.

Prediction and Evaluation: After training, the model is used to make predictions on the training and testing data. The predictions are then inverse transformed to obtain the actual temperature values. The root mean squared error (RMSE) is calculated to evaluate the performance of the model on the training and testing sets.

Future Forecasting: Finally, you showcase how to use the trained LSTM model to forecast future temperature values. You initialize the input sequence with the most recent data and iteratively predict future values based on the previous predictions.

Throughout the video, we provide explanations and insights into the code and the concepts behind temperature forecasting using LSTM networks. The tutorial aims to guide viewers in implementing a deep learning-based solution for temperature forecasting in Python.

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