Deep learning is not as complex a concept that non-science people often happen to decipher. Scientific evolution over the years have reached a stage where a lot of explorations and defined research work needs the assistance of artificial intelligence. Since machines are usually fed with a particular set of algorithms to understand and react to various tasks within a matter of seconds, working with them broadens the scope of scientific breakthroughs resulting in the invention of techniques and procedures that make human life simpler and enriching. However, in order to work with machines, it is important for them to understand and recognize things just the way the human brain does. For example, we may recognize an apple through its shape and colour.
The enormous and raging wave of change that has hit our world in the last decade, has got some of us thinking and others reveling in their glory. The internet and evolving technological practices have increased possibilities. Man and machine collaboration has got us introduced to automated virtual work and communication systems everywhere in the world. Deep Learning has given birth to several real-life applications that have lessened human control and involvement in several spheres of life. The immense popularity of the Deep Learning UseCases blog was enough encouragement to look at more such UseCases.
This page contains a curated list of examples, tutorials, blogs about XGBoost usecases. It is inspired by awesome-MXNet, awesome-php and awesome-machine-learning. Please send a pull request if you find things that belongs to here. This is a list of short codes introducing different functionalities of xgboost packages. Most of examples in this section are based on CLI or python version.
Machine Learning and Deep Learning models combined with easy to build open source techniques can help improve the diagnosis of the life-threatening Malaria disease. The objective of this usecase is to develop a model that can predict the probability of a human cell to be infected with Malaria parasite from an exploratory data analysis performed on a vast dataset containing images of infected and uninfected human cells. We leveraged deep learning models like CNN because of its effectiveness in providing solutions related to Computer Vision tasks. Using a CNN model we have been successful to predict both the categories and validate our approach to the future unseen data. To know how Qualetics gives an effective solution, download the full usecase.
So, you've heard the dazzling sales pitch on deep learning and are wondering whether it actually works in production. The top question companies have is on whether the promised land of perennial business benefits is a reality. In a previous article, we saw a simple business introduction to deep learning, a technology that seems to have a swashbuckling solution to every problem. But, what happens when the rubber hits the road? A good gauge of an innovation's maturity level is by understanding how it fares on the ground, long past the sales pitches.