Bank Defaulters Using ANN
Offered By: Great Learning via YouTube
Course Description
Overview
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.
Taught by
Great Learning
Related Courses
Neural Networks for Machine LearningUniversity of Toronto via Coursera Good Brain, Bad Brain: Basics
University of Birmingham via FutureLearn Statistical Learning with R
Stanford University via edX Machine Learning 1—Supervised Learning
Brown University via Udacity Fundamentals of Neuroscience, Part 2: Neurons and Networks
Harvard University via edX