Foundation of Artificial Neural Networks
Basics of ANN, McCulloch Pitts Model, Perceptron, BackPropagation Model, Associative Network and Unsupervised model
Foundation of Artificial Neural Networks free download
Basics of ANN, McCulloch Pitts Model, Perceptron, BackPropagation Model, Associative Network and Unsupervised model
This course serves as an insightful exploration into the Basics of Artificial Neural Networks (ANN) and key models that have played pivotal roles in shaping the field of neural network research and applications. Covering foundational concepts from the McCulloch Pitts Model to advanced algorithms like Backpropagation, Associative Networks, and Unsupervised Models, participants will gain a comprehensive understanding of the principles driving modern artificial intelligence.
Introduction to Artificial Neural Networks (ANN):
Overview of Biological Neural Networks and inspiration behind developing Artificial Neural Networks
McCulloch Pitts Model:
In-depth examination of the McCulloch Pitts Model as a pioneering concept in neural network architecture. Understanding the basic principles that laid the groundwork for subsequent developments.
Perceptron:
Exploration of the Perceptron model as a fundamental building block of neural networks.
Insight into how Perceptrons process information and make binary decisions.
BackPropagation Model:
Detailed study of the Backpropagation algorithm as a crucial element in training neural networks.
Analysis of error backpropagation and its role in optimizing the performance of neural networks.
Associative Network:
Introduction to Associative Networks and the significance of connections between elements.
Application of associative memory for pattern recognition and retrieval.
Unsupervised Models:
Comprehensive coverage of Unsupervised Learning in neural networks.
Exploration of self-organizing maps, clustering, and other unsupervised techniques.
This course is tailored for aspiring data scientists, machine learning enthusiasts, and professionals seeking to enhance their understanding of neural networks. Additionally, students and researchers interested in staying abreast of the latest developments in artificial intelligence will find this course invaluable. Embark on this educational journey to acquire a solid foundation in neural networks and gain the knowledge and skills necessary to navigate the dynamic landscape of artificial intelligence.
