There are 1 repository under sigmoid topic.
校招、秋招、春招、实习好项目!带你从零实现一个高性能的深度学习推理库,支持大模型 llama2 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step
Covid-19 detection in chest x-ray images using Convolution Neural Network.
Deep Learning
This program implements logistic regression from scratch using the gradient descent algorithm in Python to predict whether customers will purchase a new car based on their age and salary.
Compare vanishing gradient problem case by case.
Simple multi layer perceptron application using feed forward back propagation algorithm
Test project for neural networks - Handwritten digit recognition on MNIST dataset
A classical XOR neural network using pytorch
A neural network (NN) having two hidden layers is implemented, besides the input and output layers. The code gives choise to the user to use sigmoid, tanh orrelu as the activation function. Prediction accuracy is computed at the end.
A repository with Twitter data and tweet classifiers using several machine learning approaches to measure the accuracy and performance of the approaches on classifying tweets.🐧
Neural Network from scratch without any machine learning libraries
ml5 (friendly machine learning for the web) SharePoint Framework (SPFx) extension
This is an ongoing project intended to make it easier to use neural network creation, genetic algorithms, and other data science and machine learning skills.
"The 'Activation Functions' project repository contains implementations of various activation functions commonly used in neural networks. "
Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. To find a local minimum of a function using gradient descent, we take steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point. But if we instead take steps proportional to the positive of the gradient, we approach a local maximum of that function; the procedure is then known as gradient ascent.
Standard logistic function.
Logit function.
Create an iterator which evaluates the standard logistic function for each iterated value.
Create an iterator which evaluates the logit function for each iterated value.
(Sarthak Tayal, Annette Dao, Nora Michniewicz, Breanna Carez) Seizure Seeker is a browser-based tool that has trained data from EEG datasets to process EEG recordings to identify areas of seizure events/elevated neural activity. With one-second windows, it establishes a quiet baseline to detect seizure events above YOUR/an auto chosen threshold.
Implementation of an ANN for recognisement of the Iris plant-family
a neural network create to study the deep learning
Implementing a logistic regression program to predict whether a patient has heart disease or not based on some features.
SystemVerilog implementations of fundamental neural network structures, designed for synthesis on FPGAs.
Lightweight neural network library written in ANSI-C supporting prediction and backpropagation for Convolutional- and Fully Connected neural networks
Generic L-layer 'straight in Python' fully connected Neural Network implementation using numpy.
We introduce two novel hybrid activation functions: S3 (Sigmoid-Softsign) and its improved version S4 (Smoothed S3)
End-to-end binary classification pipeline for clinical risk prediction using PyTorch. Includes preprocessing, imbalanced data handling with SMOTE, optimizer benchmarking (SGD vs Adam), and performance visualization (AUC-ROC, Confusion Matrix). Designed to align with healthcare AI applications and model evaluation best practices.
NTUEE IC Design 23Fall HW4
This is a compact working example of a perceptron with sigmoid function in python.
Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting