dataset


What is Deep Learning? - QuantStart

#artificialintelligence

Almost a year ago QuantStart discussed deep learning and introduced the Theano library via a logistic regression example. Given the recent results of the QuantStart 2017 Content Survey it was decided that an up to date beginner-friendly article was needed to introduce deep learning from first principles.


Artificial Intelligence Could Help Diagnose Tuberculosis

#artificialintelligence

Artificial intelligence models may be the new tool to help screen and evaluate efforts in tuberculosis-prevalent areas that often are plagued by limited access to radiologists.


Handling imbalanced dataset in supervised learning using family of SMOTE algorithm.

#artificialintelligence

The algorithm adaptively updates the distribution and there are no assumptions made for the underlying distribution of the data. The algorithm uses Euclidean distance for KNN Algorithm. The key difference between ADASYN and SMOTE is that the former uses a density distribution, as a criterion to automatically decide the number of synthetic samples that must be generated for each minority sample by adaptively changing the weights of the different minority samples to compensate for the skewed distributions. The latter generates the same number of synthetic samples for each original minority sample.


So your company wants to do AI? – Eder Santana – Medium

#artificialintelligence

Machine Learning, and more so Deep Learning, is so popular now that it is being referred as AI itself. Gladly, your startup just got funded or your new team budget was approved! Now you will be doing Deep Learning as well. You already had fun with Keras, Imagenet, etc.


Hybrid content-based and collaborative filtering recommendations with {ordinal} logistic regression (1): Feature engineering

@machinelearnbot

I will use {ordinal} clm() (and other cool R packages such as {text2vec} as well) here to develop a hybrid content-based, collaborative filtering, and (obivously) model-based approach to solve the recommendation problem on the MovieLens 100K dataset in R. All R code used in this project can be obtained from the respective GitHub repository; the chunks of code present in the body of the post illustrate the essential steps only. The MovieLens 100K dataset can be obtained from the GroupLens research laboratory of the Department of Computer Science and Engineering at the University of Minnesota. The first part of the study introduces the new approach and refers to the feature engineering steps that are performed by the OrdinalRecommenders_1.R script (found on GitHub). The second part, to be published soon, relies on the R code in OrdinalRecommenders_3.R and presents the model training, cross-validation, and analyses steps. The OrdinalRecommenders_2.R script encompasses some tireless for-looping in R (a bad habbit indeed) across the dataset only in order to place the information from the dataset in the format needed for the modeling phase. The study aims at (a) the demonstration of the improvement in predicted ratings for recommending on a well-known dataset, and (b) attempts to shedd light on the importance of various types of information in the work of recommendation engines. Consequently, the code is not suited for use in production; additional optimizations are straightforward, simple, and necessary as well.



Tutorial: Using Amazon ML to Predict Responses to a Marketing Offer - Amazon Machine Learning

#artificialintelligence

With Amazon Machine Learning (Amazon ML), you can build and train predictive models and host your applications in a scalable cloud solution. In this tutorial, we show you how to use the Amazon ML console to create a datasource, build a machine learning (ML) model, and use the model to generate predictions that you can use in your applications.


The Democratization of Machine Learning: What It Means for Tech Innovation 04-15

#artificialintelligence

This is where public cloud services such as Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure and others come in. All of these changes mean that the world of machine learning is no longer restricted to university labs and corporate research centers that have access to massive training data and computing infrastructure. This is where public cloud services such as Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure and others come in. All of these changes mean that the world of machine learning is no longer restricted to university labs and corporate research centers that have access to massive training data and computing infrastructure.


Data enabled products are defining the future of data science

#artificialintelligence

If you want to know how to build great data science-enabled product, you needn't look beyond LinkedIn, now Microsoft-owned. The Mountain View headquartered professional networking company has over 433 million members across 200 countries, allowing data scientists access to structured datasets that spawned cutting-edge data-driven products, most notably "People You May Know"; "Social Graph Visualizations"; " Matching" and "Collaborative Filtering" that drove the company to success.


junyanz/pytorch-CycleGAN-and-pix2pix

#artificialintelligence

This is our ongoing PyTorch implementation for both unpaired and paired image-to-image translation.