Bank Defaulters Using ANN

Explore the fundamentals of Artificial Neural Networks (ANN) and their application in predicting bank defaulters in this comprehensive video tutorial. Delve into the brain-inspired data-processing paradigm of ANN, learning its architecture, weights, biases, activation functions, and back propagation. Discover the power of Keras, a user-friendly Python neural network library, and witness practical demonstrations using real-world examples. Master key concepts such as ANN architecture, loss functions, gradient descent, and Keras basic ANN architecture through hands-on demos. Gain valuable insights into dataset overview, model framework, and ANN application in credit data analysis. By the end of this 1-hour 18-minute tutorial, acquire the skills to implement ANNs for financial risk assessment and credit scoring.

Syllabus

Course Introduction.
Objective of the course.
Architecture of ANN.
Weights , Biasis and Activation Functions.
Activation Function.
Loss Functions in Neural Networks.
Back Propagation in Neural Networks.
Gradient Descent.
Keras_Basic_ANN_Architecture - Demo.
Dataset Overview and Model Framework.
ANN_Application_Credit_Data - Demo.