AIND Jupyter Notebook to predict student admissions using Keras Neural Networks
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Updated
Jun 19, 2017 - Jupyter Notebook
AIND Jupyter Notebook to predict student admissions using Keras Neural Networks
Artificial Intelligence using the sigmoid function.
creating a binary classifier that is capable of predicting whether applicants will come out successful after receiving funding by an investor.
Introductory level artificial neural network
Advance Machine Learning (CSL 712) Course Lab Assignments
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.
Using the features in the provided dataset, creating a binary classifier that can predict whether applicants will be successful if funded by Alphabet Soup.
Machine Learning Utility Functions
to maintain activation functions used in machine learning
This repository contain both Collaborative based movie recommendation system as well as Content based movie recommendation system
Artificial Natural Network Perceptron (Forward Pass and Back Propagation). Weights and Bias. Forward Pass: Net Input Function, Activation Function (Sigmoid). Threshold. Back Propagation: Binary Cross Entropy Loss, Computing Gradients/ Slopes/ Derivatives, Gradient Descent Step, Epoch.
Neural Networks and Deep Learning Models
Neural networks experiments with JavaScript. Allow the use of Sigmoid, Softplus, and Hyperbolic Tangent neurons.
MNIST classifier api
Data Science Project: Comparing 3 Deep Learning Methods (CNN, LSTM, and Transfer Learning).
Add a description, image, and links to the sigmoid topic page so that developers can more easily learn about it.
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