Evaluated the word vectors learned from both nce and cross entropy loss functions using word analogy tests
-
Updated
Nov 9, 2018 - Python
Evaluated the word vectors learned from both nce and cross entropy loss functions using word analogy tests
Word2Vec implementation using tensorflow
Built a custom adam scheduler using gradient clipping, LR scheduling, momentum updates, with two different loss functions
Implementation of a Fully Connected Neural Network, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) from Scratch, using NumPy.
Classification of Ionosphere dataset using pytorch neural networks.
In the project, the aim is to generate new song lyrics based on the artist’s previously released song’s context and style. We have chosen a Kaggle dataset of over 57,000 songs, having over 650 artists. The dataset contains artist name, song name, a link of the song for reference & lyrics of that song. We tend to create an RNN character-level la…
Фреймворк глубоко обучения на Numpy, написанный с целью изучения того, как все работает под "капотом".
Neural Networks from scratch (Inspired by Michael Nielsen book: Neural Nets and Deep Learning)
Breast Cancer Classification with Logistic Regression
Comparison of common loss functions in PyTorch using MNIST dataset
Multiclass Classification using Softmax from scratch without any famous library like Tensorflow, Pytorch, etc.
This repository contains code for the PhD thesis: "A Study of Self-training Variants for Semi-supervised Image Classification" and publications.
A classifier to differentiate between Cat and Non-Cat Images
Neural Network to predict which wearable is shown from the Fashion MNIST dataset using a single hidden layer
Code for the Paper : NBC-Softmax : Darkweb Author fingerprinting and migration tracking (https://arxiv.org/abs/2212.08184)
Decision Tree Implementation from Scratch
Pytorch Implementations of Common modules, blocks and losses for CNNs specifically for segmentation models
Implementation of key concepts of neuralnetwork via numpy
A PyTorch implementation of U-Net for aerial imagery semantic segmentation.
Add a description, image, and links to the cross-entropy-loss topic page so that developers can more easily learn about it.
To associate your repository with the cross-entropy-loss topic, visit your repo's landing page and select "manage topics."