Displaying 1 to 8 from 8 results

tensorflow_cookbook - Code for Tensorflow Machine Learning Cookbook

  •    Jupyter

This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

kmeans-clusterer - k-means clustering in Ruby

  •    Ruby

k-means clustering in Ruby. Uses NArray under the hood for fast calculations. Jump to the examples directory to see this in action.

ImageSeg-KMeans - 💠 Image Segmentation using K-Means

  •    Python

The program reads in an image, segments it using K-Means clustering and outputs the segmented image. It is worth playing with the number of iterations, low numbers will run quicker.




tensorbag - Collection of tensorflow notebooks tutorials for implementing the most important Deep Learning algorithms

  •    Jupyter

Tensorbag is a collection of tensorflow tutorial on different Deep Learning and Machine Learning algorithms. The tutorials are organised as jupyter notebooks and require tensorflow >= 1.5. There is a subset of notebooks identified with the tag [quiz] that directly ask to the reader to complete part of the code. In the same folder there is always a complementary notebook with the complete solution.

KMeans_elbow - Code for determining optimal number of clusters for K-means algorithm using the 'elbow criterion'

  •    Jupyter

"The idea behind k-Means Clustering is to take a bunch of data and determine if there are any natural clusters (groups of related objects) within the data. The k-Means algorithm is a so-called unsupervised learning algorithm. We don't know in advance what patterns exist in the data -- it has no formal classification to it -- but we would like to see if we can divide the data into groups somehow.

MLKit - A simple machine learning framework written in Swift 🤖

  •    Swift

MLKit is a simple machine learning framework written in Swift. Currently MLKit features machine learning algorithms that deal with the topic of regression, but the framework will expand over time with topics such as classification, clustering, recommender systems, and deep learning. The vision and goal of this framework is to provide developers with a toolkit to create products that can learn from data. MLKit is a side project of mine in order to make it easier for developers to implement machine learning algorithms on the go, and to familiarlize myself with machine learning concepts. This project is under active development and is not ready for use in commercial or personal projects.