Search Results for "recommendation"

The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state...

ML-For-Beginners is a structured, project-driven curriculum that teaches foundational machine learning concepts with approachable math and lots of code. Organized as a multi-week course, it mixes short lectures with labs in notebooks so learners practice regression, classification, clustering, and recommendation techniques on real datasets. Each lesson aims to connect the algorithm to a relatable scenario, reinforcing intuition before diving into parameters, metrics, and trade-offs...

Tria O.S. detects hard & soft specs of the PC where you run it, you can load this info or specify it manually to let the program inform you what would be the best operating system for a specific PC, with the specified hardware, and then see the difference by adding RAM, a SSD hard drive, changing the graphics card ... etc. REQUIREMENTS: For Linux, you need GAMBAS 3.3 or later, so you will have to install the gambas3 package before installing. 90's EDITION needs 160 MB (192 MB on...

En Dictionary is Free, Efficiency Dictionary and a English Learning Tool. Help you to improve your English knowledge using many functionality. (See bellow) Award for Invention of the En Dictioanry ICTA - e-Swabhimani 2012 SLIC - Dhasis 2012 Visit to More Details, http://namalyaya.blogspot.com/2014/01/free-download-english-learning-tool-en-dictionary-2014.html Easy All of Your Language Problem With The En Dictionary ICTA Recommendation. http://tinyurl.com/be5tcze "Real-time...

... the interaction with final users. Functionalities provided by this API can be classified into three categories: A) Technical Localisation: methods in this category support the identification of Learning Stories and Learning Activities that may be implemented in a school according to the technological resources available there. B) Resource Planning: collects methods targeted to support the location and recommendation of resources useful to implement a Learning Story / Learning Activity. C) Validation:

This project has the objective of defining a recommendation layer that will communicate with several LMS like .LRN, Moodle, etc.