In our previous post, we saw how to develop a full deep learning model from scratch in F#, and we trained it to detect Cat Vs NonCat images. In this post, we will see how to integrate a train model in a .Net application. More specifically, a Xamarin Forms cross platform app that allow user
Build a DeepLearning algorithm from scratch in F# 03 – Building A Deep Neural Network
Build a DeepLearning algorithm from scratch in F# 03 – Building A Deep Neural Network Welcome to the third post of my “Build a DeepLearning algorithm from scratch in F#” serie. In the first article, we described how to implement a logistic regression with a single neuron. In the second article, we’ve seen how to
Build a DeepLearning algorithm from scratch in F# 02 – Planar data classification with one hidden layer
Welcome to the second post of our “Build a DeepLearning algorithm from scratch in F#” serie. In our previous article, we described how to implement a logistic regression with a neural network mindset. In that scneario, the input layer is connected directly to the output layer. In today’s post, we’ll build our first neural network
Build a DeepLearning algorithm from scratch in F# 01 – Logistic Regression with a Neural Network mindset
Welcome to the first article of a serie dedicated to learning how to build neural networks from scratch using the F# language. We will base our articles on the original work of Andrew NG tutorials, availabe on Coursera, but using F# instead of Python. Today we will build a logistic regression classifier to recognize cats.