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In this Deep Learning Project, you will learn how to optimally tune the hyperparameters (learning rate, epochs, dropout, early stopping) of a neural network model in PyTorch to improve model performance.
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Overview
Hyperparameters are a set of parameters whose value controls the learning process of the model. The performance of models can be greatly improved by tuning their hyperparameters. Tuning hyperparameters means you are trying to find out the set of optimal parameters, giving you better performance than the default hyperparameters of the model.
In the previous project of the series Learn How to Build Neural Networks from Scratch, we saw what Neural Networks are and how we can build a Neural Network for the classification model in Pytorch. In this project, we will tune the hyperparameters such as learning rate, epochs, dropout, Early stopping, checkpoints to improve the performance of our classification model.
Aim
To understand the hyperparameters of neural networks
To understand how to tune hyperparameters for improving model performance in PyTorch
Data Description
The dataset used in this project has information about the customer churn based on the various features. The dataset contains 2000 rows and 15 features to predict the churn.
Tech Stack
Language: Python
Libraries: Pandas, Pytorch,matplotlib,sci-kit learn, NumPy, imblearn
Approach
Data cleaning
Data preprocessing
Building a sequential neural network
Model training
Tuning hyperparameters
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