Neural Networks - Recurrent
As we collect more and more data as a society, we need more powerful tools to process it. This is where neural networks excel as they are able to handle high-dimensional data and complex processing with relatively little human intervention. While classic machine learning (ML) methods have been able to conquer classification and regression tasks such as filtering spam emails and predicting the price of an item, they do not perform well on natural language processing or image classification tasks. In essence, neural networks are being used to perform the complex tasks we ask our brains to do every day. For this reason, it is not surprising that the “under-the-hood” efforts of neural networks parallel (at a basic level) the processes of the neurons in our brain.
Neural Networks – General Overview
Neural networks are quickly becoming the go-to algorithm for most machine learning tasks and are heralded as the main factor shaping the future of technology. This rise in popularity is mainly due to the increase in computing power since the concept of neural networks was first presented (circa 1960’s) and the exponential increase in the amount of data generated each day. The increased computing power has enabled the neural network algorithms to show off their power and yield impressive results on today’s massive datasets in a way that previous machine learning algorithms simply cannot.
Ensemble Methods: Putting it All Together
Ensemble methods are one of the most effective classes of machine learning algorithms available to data scientists for supervised learning. As a testament to their power, examples of these algorithms such as Random Forests, Gradient Boosted Models (GBM), or custom stacks of various types of models (meta-ensembles) are routinely seen in the winning solutions for Kaggle competitions and the KDD Cup. But what exactly is an ensemble method? How do they work? And what makes them so effective? Understanding the answers to these questions can help data scientists identify situations where ensemble methods would excel and aid in their communication of the results.