Mouneshgouda/Deep-learning
This repository contains Jupyter Notebooks showcasing different techniques for price prediction using Artificial Neural Networks (ANNs). Topics covered include early stopping, batch normalization, dropout regularization, and deep neural networks. These notebooks offer practical implementations and insights for improving model training
This project provides practical examples for predicting future prices using artificial neural networks. It demonstrates how to refine neural network models using techniques like early stopping, batch normalization, and dropout regularization. Traders, financial analysts, or anyone interested in forecasting market trends would find these notebooks valuable.
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Use this if you want to learn how to apply deep learning techniques to predict price movements and understand various model optimization strategies.
Not ideal if you are looking for a ready-to-use trading bot or a tool that doesn't require hands-on understanding of neural network architecture.
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Last pushed
Mar 18, 2024
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