This book is for current and aspiring machine learning and artificial intelligence practitioners looking to implement solutions to real-world predictive analytical problems in agricultural and allied fields.
As for prerequisite knowledge, basic familiarity with regression analysis typically presented in applied disciplines, knowledge of probability, mathematics and neural networks.
The book is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. This book represents our attempt to make Neural Network approachable, teaching you the concepts, the context, and the application of Neural Network and Deep Learning.
A Neural Network is predecessors of the deep learning methods that we focus on in this book. In this book the real data set is used for underrating the practical approach and application of Neural Networks. In this book, we want to show how to build Neural Network solutions for rainfall prediction, and how to choose the best prediction model. With the knowledge in this book, you can train the neural network to make the best predictions. We have also provided some various methods of rainfall predictions methods. The applications of machine learning, artificial intelligence and neural network are endless and, with the amount of data available today, mostly limited by your imagination.
You will get the most out of the book if you are somewhat familiar with Deep Learning and Neural Networks.
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