CIFAR 10 image dataset
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Updated
Feb 15, 2019 - Python
CIFAR 10 image dataset
Deep Learning
Compare vanishing gradient problem case by case.
Simple multi layer perceptron application using feed forward back propagation algorithm
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.
A classical XOR neural network using pytorch
Neural Network from scratch without any machine learning libraries
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 implementation of a Neural Network in vanilla python that trains on the MNIST handwritten digit classifiction problem.
Implementing a logistic regression program to predict whether a patient has heart disease or not based on some features.
This is a compact working example of a perceptron with sigmoid function in python.
Generic L-layer 'straight in Python' fully connected Neural Network implementation using numpy.
Comparison of common activation functions on MNIST dataset using PyTorch.
"The 'Activation Functions' project repository contains implementations of various activation functions commonly used in neural networks. "
Introductory level artificial neural network
Mini-learn is a miniature version of tensor-flow which I made with ONLY NUMPY to play with perceptrons. You can use this project like you use tflearn. Go to https://github.com/Satyaki0924/boston-housing-with-minilearn to see it's usage.
Developed Neural Network (NN) having one hidden layer, two hidden layers and four hidden layers, besides the input and output layers. Tested with Sigmoid, tanh and ReLu activation function. Used Scikit learn for pre-processing data.
to maintain activation functions used in machine learning
MNIST classifier api
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