Master Stock Price Prediction with PyTorch & LSTM on NeuralNine
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Today on NeuralNine, the team delves into the thrilling world of stock price prediction using PyTorch and LSTM cells. These cells, akin to advanced recurrent neural network units, possess a unique ability to forget irrelevant data while storing crucial information for accurate predictions. While the video emphasizes that it's not financial advice, but a programming tutorial, it showcases the power of working with sequential data like stock prices. The team's focus lies in building and training a neural network to handle time series data efficiently.
In their signature style, the team navigates to Jupyter Lab, the playground for Python enthusiasts, to kickstart their stock price prediction project. Armed with essential packages like numpy, pandas, and torch, they set the stage for data processing and neural network training. The channel's host, with a touch of humor, highlights the importance of setting the device variable to GPU for enhanced performance, if available. The excitement builds as they download stock data using ticker symbols like AAPL for Apple, preparing to dive deep into the world of financial data analysis.
With a flair for drama, the team preprocesses the stock data using a standard scaler, transforming it into a format suitable for the neural network. They meticulously define the sequence length, crafting input-output pairs essential for training the model. The NeuralNine crew expertly splits the data into training and testing sets, laying the foundation for evaluating the model's predictive capabilities. As the video progresses, viewers are taken on a thrilling journey through data preparation, setting the stage for the neural network's grand entrance into the world of stock price prediction.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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