One of the challenging aspects of applying machine learning is the need to identify the algorithms that will perform best for a given dataset. This process can be difficult, time consuming and often requires a great deal of domain knowledge. We present Sommelier, an expert system for recommending the machine learning algorithms that should be applied on a previously unseen dataset. Sommelier is based on word embedding representations of the domain knowledge extracted from a large corpus of academic publications. When presented with a new dataset and its problem description, Sommelier leverages a recommendation model trained on the word embedding representation to provide a ranked list of the most relevant algorithms to be used on the dataset. We demonstrate Sommelier's effectiveness by conducting an extensive evaluation on 121 publicly available datasets and 53 classification algorithms. The top algorithms recommended for each dataset by Sommelier were able to achieve on average 97.7% of the optimal accuracy of all surveyed algorithms.
Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept of social trust has attracted the attention of information processors/data scientists and information consumers/business firms. One of the main reasons for acquiring the value of Social Big Data (SBD) is to provide frameworks and methodologies using which the credibility of OSNs users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring the credibility of SBD. Given the exposed environment's settings and fewer limitations related to OSNs, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. Hence, this paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.
The following topics are covered in this Artificial Intelligence Tutorial: (01:08) History Of AI (03:20) What Is AI? (04:07) Stages Of Artificial Intelligence (06:45) Types Of Artificial Intelligence (09:16) Domains Of Artificial Intelligence Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Check out the entire Machine Learning Playlist: https://bit.ly/2NG9tK4 It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning. The Master's Program Covers Topics LIke: Python Programming PySpark HDFS Spark SQL Machine Learning Techniques and Artificial Intelligence Types Tokenization Named Entity Recognition Lemmatization Supervised Algorithms Unsupervised Algorithms Tensor Flow Deep learning Keras Neural Networks Bayesian and Markov's Models Inference Decision Making Bandit Algorithms Bellman Equation Policy Gradient Methods. However, as a goodwill gesture, Edureka offers a complimentary self-paced course in your LMS on SQL Essentials to brush up on your SQL Skills.
Sentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified sentence should also be logically entailed by its input sentence. In this work, we first present a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model, and then improve its entailment and paraphrasing capabilities via multi-task learning with related auxiliary tasks of entailment and paraphrase generation. Moreover, we propose a novel 'multi-level' layered soft sharing approach where each auxiliary task shares different (higher versus lower) level layers of the sentence simplification model, depending on the task's semantic versus lexico-syntactic nature. We also introduce a novel multi-armed bandit based training approach that dynamically learns how to effectively switch across tasks during multi-task learning. Experiments on multiple popular datasets demonstrate that our model outperforms competitive simplification systems in SARI and FKGL automatic metrics, and human evaluation. Further, we present several ablation analyses on alternative layer sharing methods, soft versus hard sharing, dynamic multi-armed bandit sampling approaches, and our model's learned entailment and paraphrasing skills.
In 2016 it got exponential growth over 2012 and 2017 figures till Sept equally critical is delivering this performance with reduced silicon (all grey & thin areas) area and industry power consumption. Machine learning helps in reducing the required efforts bandwidth between the buyer, seller and manufacturers such bandwidth reductions also reduce the cost and required time. In eCommerce AI based technologies like Big Data, Machine Learning, Neural Networks, Data Science, Bots and Deep Learning (mainly for secured online payments) are currently buzzwords. To safe guard the business from anti social elements deep learning helps in fraud detection, prevention, velocity measure and makes better business decisions with deep understanding of entity resolution (avoid multiple accounts of same person), Image recognition and understanding, Concept extraction, sentiment and trend analysis makes buyers life easy to choose and buy.