Binary and Categorical Focal loss implementation in Keras.
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Updated
Nov 21, 2022 - Python
Binary and Categorical Focal loss implementation in Keras.
Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"
A PyTorch implementation of U-Net for aerial imagery semantic segmentation.
Implementation of key concepts of neuralnetwork via numpy
Pytorch Implementations of Common modules, blocks and losses for CNNs specifically for segmentation models
Decision Tree Implementation from Scratch
Code for the Paper : NBC-Softmax : Darkweb Author fingerprinting and migration tracking (https://arxiv.org/abs/2212.08184)
Neural Network to predict which wearable is shown from the Fashion MNIST dataset using a single hidden layer
A classifier to differentiate between Cat and Non-Cat Images
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.
Neural Networks from scratch (Inspired by Michael Nielsen book: Neural Nets and Deep Learning)
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".
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
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