Course Outline

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

Requirements

  • Basic experience with R programming
  • General familiarity with financial and banking concepts
  • Basic familiarity with statistics and mathematical concepts
 28 Hours

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