As everyone knows Machine learning studies computer algorithms for learning to do stuff. We might, for instance, be interested in learning to complete a task, or to make accurate predictions, or to behave intelligently. The learning that is being done is always based on some sort of observations or data, such as examples…direct experience, or instruction. So in general, machine learning is about learning to do better in the future based on what was experienced in the past. Machine learning is being used in a lot of real-world applications for various purpose. In this article, we will see various applications of Machine Learning!
Tackle feature selection in R: explore the Boruta algorithm, a wrapper built around the Random Forest classification algorithm, and its implementation!
Extreme Gradient Boosting is among the hottest libraries in supervised machine learning these days. It supports various objective functions, including regression, classification, and ranking. It has gained much popularity and attention recently as it was the algorithm of choice for many winning teams of a number of machine learning competitions. Previously I showed how to do Extreme Gradient Boosting with R and in this post, I will show how to do it with Python.
In 2015, our early attempts to visualize how neural networks understand images led to psychedelic images. Soon after, we open sourced our code as DeepDream and it grew into a small art movement producing all sorts of amazing things. But we also continued the original line of research behind DeepDream, trying to address one of the most exciting questions in Deep Learning: how do neural networks do what they do?
AI is taking hold of a wider audience of businesses and individuals. Deep learning-enabled capabilities such as natural language processing (NLP), computer vision, and speech recognition have become a game changer for many industries. Image and voice interaction with our computing devices is now commonplace, and this is changing how we live. Fueling the growth of deep learning is the availability of open source frameworks and libraries that allow developers of all skill levels to build deep learning models quickly and easily. One framework making waves in the deep learning community is Apache MXNet, which reached 1.0 at the end of 2017. O’Reilly has been working with AWS and a great bunch of authors to develop a deeply technical library of content for developers who want to use MXNet along with its Gluon API. However, we aren’t the only ones who’ve been putting out great content—here are some of the best resources for your deep learning needs.