Deep Learning Neural Network with R

Deep learning is one of the most powerful machine learning techniques, and R is a popular programming language for data science. This “Deep Learning Neural Network with R” course will teach you how to build and train deep learning models in R using real-world projects.

You’ll start by learning the basics of neural networks, including single-layer and multi-layer networks. Using the Keras library, you’ll then learn how to implement these networks in R.

Once you understand the basics well, you’ll embark on two hands-on projects. In the first project, you’ll build a single-layer neural network to predict crop yields in agriculture. In the second project, you’ll build a multi-layer neural network to predict war deaths.

As you progress, the curriculum seamlessly transitions you into the realm of multi-layer neural networks, tackling the complexities of war-related death data. This step-up solidifies your grasp of neural networks and enhances your ability to handle intricate datasets, extract meaningful information, and craft informed predictions. The projects are designed to simulate world scenarios, ensuring you are well-prepared to apply your newfound knowledge to valuable applications.

By the end of this “Deep Learning Neural Network with R” course, you will have mastered the art and science of neural networks, armed with the confidence and skills to analyze diverse datasets and make predictions that can drive decisions. The knowledge and valuable skills you gain will add value to your professional repertoire and open doors to numerous opportunities in the fast-evolving world of data analysis. This is your opportunity to step into the future and make a mark in the world of deep learning neural networks.