For the past five years, the hottest thing in artificial intelligence has been a branch known as deep learning. The grandly named statistical technique, put simply, gives computers a way to learn by processing massive amounts of data. Thanks to deep learning, computers can easily identify faces and recognize spoken words, making other forms of humanlike intelligence suddenly seem within reach. Companies like Google, Facebook and Microsoft have poured money into deep learning. And the technology's perception and pattern-matching abilities are being applied to improve progress in fields such as drug discovery and self-driving cars.
PyData London 2017 Description This talk will demonstrate how to harness a deep-learning framework such as Tensorflow, together with the usual suspects such as Pandas and Numpy, to implement recommendation models for news and classified ads. Abstract Recommender systems are used across the digital industry to model users' preferences and increase engagement. Popularised by the seminal Netflix prize, collaborative filtering techniques such as matrix factorisation are still widely used, with modern variants using a mix of meta-data and interaction data in order to deal with new users and items. We will demonstrate how to implement a variety of models using Tensorflow, from simple bi-linear models expressed as shallow neural nets to the latest deep incarnations of Amazon DSSTNE and Youtube neural networks. We will also use TensorBoard and particularly the embedding projector to visualise the latent space for items and metadata.
Have you ever wondered how apps like Netflix or Spotify decide which movie or songs you're likely to prefer watching or listening to? Seems like magic, doesn't it? For instance, a lot of data is being mined and multiple complicated algorithms are developed by data science professionals in an attempt to make predictions more accurate. It is not magic but "machine learning." Machine learning is what allows the system to determine the movies and songs most relevant to your liking.
This article is extended version of the presentation I gave at General AI Challenge kick off meetup organized by GoodAI and MLMU. First of all, I have to explain what a meta-learning is good for. The term meta-learning has many definitions and meanings. As you can see the meta-learning is relevant for the first objective of the challenge which is developing AI with gradual learning capabilities. One of the main direction in meta-learning is having a meta level system utilizing a Knowledge Repository.
Online healthcare communities provide users with various healthcare interventions to promote healthy behavior and improve adherence. When faced with too many intervention choices, however, individuals may find it difficult to decide which option to take, especially when they lack the experience or knowledge to evaluate different options. The choice overload issue may negatively affect users' engagement in health management. In this study, we take a design-science perspective to propose a recommendation framework that helps users to select healthcare interventions. Taking into account that users' health behaviors can be highly dynamic and diverse, we propose a multi-armed bandit (MAB)-driven recommendation framework, which enables us to adaptively learn users' preference variations while promoting recommendation diversity in the meantime. To better adapt an MAB to the healthcare context, we synthesize two innovative model components based on prominent health theories. The first component is a deep-learning-based feature engineering procedure, which is designed to learn crucial recommendation contexts in regard to users' sequential health histories, health-management experiences, preferences, and intrinsic attributes of healthcare interventions. The second component is a diversity constraint, which structurally diversifies recommendations in different dimensions to provide users with well-rounded support. We apply our approach to an online weight management context and evaluate it rigorously through a series of experiments. Our results demonstrate that each of the design components is effective and that our recommendation design outperforms a wide range of state-of-the-art recommendation systems. Our study contributes to the research on the application of business intelligence and has implications for multiple stakeholders, including online healthcare platforms, policymakers, and users.