Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve a wide range of problems in different areas of AI and machine learning. The advantage of neural network is that it is adaptive in nature. It learns from the information provided, i.e. trains itself from the data, which has a known outcome and optimizes its weights for a better prediction in situations with unknown outcome.
R provides this machine learning environment under a strong programming platform, which not only provides the supporting computation paradigm but also offers enormous flexibility on related data processing. The open source version of R and the supporting neural network packages are very easy to install and also simple to learn. Machine learning is widely used in many areas, ranging from the diagnosis of diseases to weather forecasting. You can also experiment with any novel example, which you feel can be interesting to solve using a neural network.
This comprehensive 3–in–1 course is a step–by–step guide to understanding Neural Networks with R; throughout the course, practical, real–world examples help you get acquainted with the various concepts of Neural Networks. Develop a strong background in neural networks with R, to implement them in your applications. Learn how to build and train neural network models to solve complex problems. Implement solutions from scratch, covering real–world case studies to illustrate the power of neural network models.
Specification: R: Artificial Neural Nets in R – Beginner to Expert!: 3-in-1