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Are you thinking of learning more about Artificial Neural Network?This book has been written in layman's terms as an introduction to neural networks and their algorithms. Each algorithm is explained very easily for more understanding.
Several Visual Illustrations and ExamplesInstead of tough math formulas, this book contains several graphs and images which detail all algorithms and their applications in all area of the real life.
Why this book is different ?An Artificial Neural Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human (animal) brain processes information. It includes a large number of connected processing units called neurons that work together to process information. They also generate meaningful results from it. In this book, we will take you through the complete introduction to Artificial Neural Network, Artificial Neural Network Structure, layers of ANN, Applications, Algorithms, Tools and technology, Practical implementations and the benefits and limitations of ANN.
This book takes a different approach that is based on providing simple examples of how ANN algorithms work, and building on those examples step by step to encompass the more complicated parts of the algorithms.
Target UsersThe book designed for a variety of target audiences. The most suitable users would include:
Beginners who want to approach ANN, but are too afraid of complex math to start
- Newbies in computer science techniques and ANN
- Professionals in data science and social sciences
- Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
- Students and academicians, especially those focusing on neural networks and deep learning
What’s inside this book?
- What is Artificial Neural Network?
- Why Neural Networks?
- Major Variants of Artificial Neural Network
- Tools and Technologies
- Practical implementations
- Major NN projects
- Open sources resources
- Issues and Challenges
- Applications of ANN
- Deep Learning: What & Why?
- Our Future with Deep Learning Applied
- The Long-Term Vision of Deep Learning
- Glossary of Some Useful Terms in Neural Networks