Notes on implementation part of Cross Entropy Loss

Discover the main building blocks of neural networks and understand the three main neural network architectures. Explore the process of solving a regression data problem

[ISBI2020] Kappa loss for skin lesion segmentation in fully convolutional network

Here you will find R Codes and bibliography related to Loss Models Theory.

Machine learning projects using Kaggle datasets

This repo serves as a showcase for the implementation of various cost functions commonly used in the measurement of efficient weights and biases during the critical phase of backward propagation in neural networks.

AI | ML | DL Fully Covered for Basic

📄 Official implementation regarding the paper "Genetic Programming Operators into Artificial Machine Learning Losses".

Tópicos en Inteligencia Artificial

RULSTM Loss Functions Code, Used to aid in experimentation with architecture testing

Easy Reference on Common Loss Function Definitions

Notes about triplet loss

"This program trains a model using 'SVM' or 'Softmax' and predicts the input data. Loss history and predicted tags are displayed as results."

RULSTM Dissertation Research, Architecture used for RULSTM experimentation, mainly with loss functions, sequence completion pretraining and anticipation times.

Welcome to the Deep Learning Notes repository! This collection of notes is designed to provide a deep understanding, intuition, and real-world implications of deep learning. Whether you're a beginner or preparing for exams and interviews, these comprehensive and colorful notes will be your go-to readme resource.

All loss function and metrics related to segmentation model in one repo.

Implementation of "Safe Deep Learning-Based Global Path Planning Using a Fast Collision-Free Path Generator". We present a global path planning method in this project which is based on an LSTM model that predicts safe paths for the desired start and goal points in an environment with polygonal obstacles, using a new loss function (MSE-NER).

All codes of my master thesis conducted under computational learning theory