Implementation of Deep Learning algorithm from scratch
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Updated
Apr 21, 2018 - Jupyter Notebook
Implementation of Deep Learning algorithm from scratch
Jupyter notebook implementing an efficient machine learning method to classify flowers from the Iris data set.
MITx - MicroMasters Program on Statistics and Data Science - Machine Learning with Python - Second Project
In this project, I implement a softmax classifier and a K-nearest-neighbor algorithm from scratch and train them. I do not use any DL library, only classic math libraries are required (numpy, math, mathplotlib...).
Applying a softmax based neural network to predict customer category
"This program trains a model using 'SVM' or 'Softmax' and predicts the input data. Loss history and predicted tags are displayed as results."
Image Classification pipeline for CIFAR-10 dataset based on K-NN, Svm, Softmax and 2-layer Neural Net Classifiers
Classify an email as a ham or a spam.
Just exploring Deep Learning
MNIST Handwritten Digits Classification using Deep Learning with accuracy of 0.9944
KNN, SVM, Neural network for image classification
Code Snippets for Sentiment Analysis Related Operations
Algorithms for logistic regression, including regularization, soft-max loss and classifier
Introduction to neural networks
rede neural totalmente conectada, utilizando mini-batch gradient descent e softmax para classificação no dataset MNIST
Classifier Iris dataset with softmax from scratch
My attempt to implement a generic deep learning platform using Python
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