Course Description

This tutorial course teaches you how to apply Artificial Neural Networks ANN and Deep Neural Networks DNN using PyTorch to solve core supply chain, procurement, and finance optimization problems. You will learn how to build deep learning models that can optimize product pricing, forecast supplier reliability, detect purchase order fraud, analyze customer purchase patterns, and predict late payments.

Using the real world datasets, this tutorial course covers end to end implementation from data preprocessing, feature engineering and model design, to model training, evaluation, and business interpretation. You will also learn to create ANN, DNN based solutions for price quotation analysis, credit memo utilization prediction, dynamic credit limit adjustment, and vendor evaluation scoring. This tutorial course goal is to convert raw procurement, ERP, and finance data into actionable predictions that improve business decision making and cost control.

This tutorial course primarily focuses on:

ANN for price optimization & customer purchase behavior

DNN for supplier reliability & vendor performance scoring

Purchase order fraud & vendor risk prediction with deep learning

Credit memo & credit limit prediction models

Full PyTorch workflows on real business datasets

By the end of this course, You will be able to

Build ANN, DNN models in PyTorch for business prediction

Optimize product prices using neural networks

Predict supplier reliability and vendor performance

Detect purchase order fraud using deep learning

Analyze customer purchase behavior patterns

Predict price quotation success probability

Forecast late payments & perform credit risk analysis

Automate vendor evaluation scorecard using DNN

You will learn in this tutorial course

How to preprocess procurement, vendor & pricing datasets

How to build, train & evaluate ANN and DNN models using PyTorch

How to convert predictions into actionable business insights

How to deploy AI models for pricing, vendor risk & financial planning