Deep Learning for Banking (with R)

Introduction

Understanding the Fundamentals of Artificial Intelligence and Machine Learning

Understanding Deep Learning

• Overview of the Basic Concepts of Deep Learning
• Differentiating Between Machine Learning and Deep Learning
• Overview of Applications for Deep Learning

Overview of Neural Networks

• What are Neural Networks
• Neural Networks vs Regression Models
• Understanding Mathematical Foundations and Learning Mechanisms
• Constructing an Artificial Neural Network
• Understanding Neural Nodes and Connections
• Working with Neurons, Layers, and Input and Output Data
• Understanding Single Layer Perceptrons
• Differences Between Supervised and Unsupervised Learning
• Learning Feedforward and Feedback Neural Networks
• Understanding Forward Propagation and Back Propagation
• Understanding Long Short-Term Memory (LSTM)
• Exploring Recurrent Neural Networks in Practice
• Exploring Convolutional Neural Networks in practice
• Improving the Way Neural Networks Learn

Overview of Deep Learning Techniques Used in Banking

• Neural Networks
• Natural Language Processing
• Image Recognition
• Speech Recognition
• Sentimental Analysis

Exploring Deep Learning Case Studies for Banking

• Anti-Money Laundering Programs
• Know-Your-Customer (KYC) Checks
• Sanctions List Monitoring
• Billing Fraud Oversight
• Risk Management
• Fraud Detection
• Product and Customer Segmentation
• Performance Evaluation
• General Compliance Functions

Understanding the Benefits of Deep Learning for Banking

Exploring the Different Deep Learning Packages for R

Deep Learning in R with Keras and RStudio

• Overview of the Keras Package for R
• Installing the Keras Package for R
• Loading the Data
  • Using Built-in Datasets
  • Using Data from Files
  • Using Dummy Data
• Exploring the Data
• Preprocessing the Data
  • Cleaning the Data
  • Normalizing the Data
  • Splitting the Data into Training and Test Sets
• Implementing One Hot Encoding (OHE)
• Defining the Architecture of Your Model
• Compiling and Fitting Your Model to the Data
• Training Your Model
• Visualizing the Model Training History
• Using Your Model to Predict Labels of New Data
• Evaluating Your Model
• Fine-Tuning Your Model
• Saving and Exporting Your Model

Hands-on: Building a Deep Learning Credit Risk Model Using R

Extending your Company's Capabilities

• Developing Models in the Cloud
• Using GPUs to Accelerate Deep Learning
• Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis

Summary and Conclusion