Multi-language Analyze text in 26 Cantonal Swiss German, Italian, German, Chinese (simplified), French, Italian. pply natural language understanding (NLU) to their applications with features including sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis.

MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20

Official repository of CVPRW2022 paper, ElasticFace: Elastic Margin Loss for Deep Face Recognition

【武汉大学遥感学院】空间智能感知与服务课设 | 基于Softmax的多波段遥感影像分类

Plots how the logit values that are passed into the softmax function change over time as the model is trained.

A CNN approach to automatically assess bouldering routes difficulty levels

A repository for hosting some of the popular machine learning algorithm implementations.

These Codes are written as part of Neural Networks and Deep learning course at UCLA.

Implementing deep learning algorithms from scratch

No description provided.

Multiclass Classification using Softmax from scratch without any famous library like Tensorflow, Pytorch, etc.

A deep learning model that recognizes hand gestures for alphabets. Trained using tensorflow, with activation function : RELU and Softmax (for multi-class classification).

handwritten digit recognition in real time

It reads handwritten numbers given an input of pixel values. A supervised learning, gradient descent, mini-batching, softmax-output-activation neural network that is meant to be trained on the MNIST dataset (dataset not included in this repository).

About some methods in Deep Learning using TensorFlow

Neural Network from Scratch with Python

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

This is the code for "predict MNIST datasets using pure Tensorflow and Keras, a shallow learning model" By M.Junaid Fiaz

Push features to OSM taked from satellite images.

This project demonstrates the implementation of a Softmax classifier from scratch, featuring both naive (loop-based) and vectorized versions for educational and performance comparison purposes. The implementation is based on CIFAR 10 dataset.

Some deep learning projects using TensorFlow

Simple Neural Network that uses multiclass classification to predict the price category of your phone given information about the hardware of the phone

This Repo provides a comprehensive exploration of machine learning techniques for handwritten digit recognition, progressing from simple linear models to sophisticated deep neural networks. we will implement and compare logistic regression, softmax regression, and custom neural networks using PyTorch.