This project is about examining how the initialization of the biases impacts the learning behavior.
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Updated
Jul 4, 2023 - Jupyter Notebook
This project is about examining how the initialization of the biases impacts the learning behavior.
This is a repository with the assignments of IE678 Deep Learning course at University of Mannheim.
A collection of different kinds of neural networks classes.
Deep learning using pyTorch
FeedForward Neural Networks Library ifrom scratch implemented using CUDA and vc++, With simple example application for MNIST dataset implementation with 97.82% Accuracy
This repository contains code and dataset used in the final project of the NLP course in Iran University of Science and Technology
This mini-project utilizes a Feedforward Neural Network to accurately identify digits from a dataset comprising tens of thousands of handwritten images, demonstrating the model's capability in pattern recognition.
Deep Learning on NLP in Pytorch using a Greek dataset with tweets regarding the elections .
Fully connected neural network for solving the mnist problem 784
Implement GAN (Generative Adversarial Network) on MNIST dataset. Vary the hyperparameters and analyze the corresponding results.
Used patterns in Indian names that models could learn, modelling those using n-gram models, then moved to neural n-gram and RNN models.
Estudo comparativo de técnicas de machine learning para previsão de valores de ações.
Fixed-volume neighborhood classifier with binary feedback
OCR technology to predict labels from handwritten text images.
Design and train two different neural network models for image the inpainting task
Milwaukee Bucks Game Outcome Prediction using Tensorflow
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