Digests » 87
There is a growing interest in the use of deep generative models for sampling high-dimensional data; examples include high-resolution natural images, long-form text generation, designing pharmaceutical drugs, and creating new materials at the molecular level. Training these models is, however, an arduous task.
Finding the right dataset while researching for machine learning or data science projects is a quite difficult task. And, to build accurate models, you need a huge amount of data. But don’t worry, there are many researchers, organizations, and individuals who have shared their work and we can use their datasets in our projects. In this article, we will discuss more than 70 machine learning datasets that you can use to build your next data science project.
Decision tree machine learning algorithm can be used to solve not only regression but also classification problems. This algorithm creates a tree like conditional control statements to create its model hence it is named as decision tree. In this post we will be implementing a simple decision tree classification model using python and sklearn.
Currently, AI is one of the fastest-growing technology in the current job market, the demand for AI professionals outpaces the current skilled AI engineers.
A model-agnostic visual debugging tool for machine learning from Uber. As a visual analytics tool, Manifold allows ML practitioners to look beyond overall summary metrics to detect which subset of data a model is inaccurately predicting. Manifold also explains the potential cause of poor model performance by surfacing the feature distribution difference between better and worse-performing subsets of data.