Aman Rajput
AmanRajput997
Machine Learning engineer specializing in Deep Learning and NLP. || I’m currently working on transformer-based models. || Python, Tensorflow, Pytorch.
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📝 Description This project aims to predict whether a credit card customer will churn (i.e., close their account). By identifying customers who are likely to churn, the bank can take proactive steps to retain them. This is a binary classification problem, and a neural network model is built using TensorFlow and Keras to make the predictions.
This project predicts the chances of a student's admission to a graduate school based on various academic factors. An Artificial Neural Network (ANN) is built using TensorFlow and Keras to perform this prediction.
This project demonstrates a multi-class classification task to predict handwritten digits using the well-known MNIST dataset. The prediction is accomplished by building and training an Artificial Neural Network (ANN) with the help of the TensorFlow and Keras libraries.
Repositories
17This README summarizes a collection of Jupyter notebooks that demonstrate core statistical concepts, data analysis techniques, and Python programming proficiency. Each notebook combines theoretical explanations with practical Python implementations to showcase hypothesis testing, probability distributions, and data transformations.
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This repository contains a series of Jupyter notebooks demonstrating the implementation and comparison of various Recurrent Neural Network (RNN) architectures—SimpleRNN, LSTM, and GRU—for a binary sentiment classification task on the IMDb movie review dataset.
📝 Description This project aims to predict whether a credit card customer will churn (i.e., close their account). By identifying customers who are likely to churn, the bank can take proactive steps to retain them. This is a binary classification problem, and a neural network model is built using TensorFlow and Keras to make the predictions.
A Convolutional Neural Network is a class of deep neural networks most commonly applied to analyzing visual imagery. They are inspired by the biological processes in the animal visual cortex. CNNs use special layers—convolutional and pooling layers—to automatically and adaptively learn spatial hierarchies of features from input images.
This repository contains a collection of Jupyter notebooks that demonstrate various techniques to enhance the performance of deep learning models. These notebooks provide practical examples and explanations of how to implement these techniques using Python and popular deep learning libraries.
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This project provides a practical comparison between Batch Gradient Descent (BGD) and Stochastic Gradient Descent (SGD), two fundamental optimization algorithms in machine learning and deep learning. Using a simple neural network built with TensorFlow/Keras, we analyze the performance of these algorithms on a binary classification task.
This project predicts the chances of a student's admission to a graduate school based on various academic factors. An Artificial Neural Network (ANN) is built using TensorFlow and Keras to perform this prediction.
This project demonstrates a multi-class classification task to predict handwritten digits using the well-known MNIST dataset. The prediction is accomplished by building and training an Artificial Neural Network (ANN) with the help of the TensorFlow and Keras libraries.
No description provided.
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