The Pytorch Implementation of L-Softmax
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Updated
Aug 27, 2018 - Python
The Pytorch Implementation of L-Softmax
Machine learning algorithms in Dart programming language
My solutions for Assignments of CS231n: Convolutional Neural Networks for Visual Recognition
Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.
Plots how the logit values that are passed into the softmax function change over time as the model is trained.
Classifying fruit types using a deep learning method, namely Convolutional Neural Network (CNN/ConvNet), which is a type of artificial neural network that is generally used in image recognition and processing. And carry out the process of improvement mode with transfer learning.
This is a naive implementaion of softmax classifier with cross entropy loss functioon
Read and process CIFAR10 dataset, implement SVM and Softmax classifiers, train , and also tune up hyper parameters.
MITx - MicroMasters Program on Statistics and Data Science - Machine Learning with Python - Second Project
Jupyter notebook implementing an efficient machine learning method to classify flowers from the Iris data set.
Just exploring Deep Learning
Deep Learning breast histology microscopy image recognition using Convolutional Neural Networks
Applied Softmax Classifier on Cifar10 Dataset
Repository contains neural network for classification using softmax as an activation function
Neural Network to predict which wearable is shown from the Fashion MNIST dataset using a single hidden layer
Classifying the following 5 types of flowers: Rose, Daisy, Dandelion, Sunflower and Tulip
Compared 3 Machine learning algorithms namely Softmax classification, K nearest neighbours and Multilayer Perceptron using F-1 scoring on Breast Cancer Wisconsin dataset. Used Features based on digitized image of a fine needle aspirate (FNA) of a breast mass. Used Scikit SKLearn to Implement the 3 models.
"This program trains a model using 'SVM' or 'Softmax' and predicts the input data. Loss history and predicted tags are displayed as results."
Implementation of Deep Learning algorithm from scratch
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