## Duration

28 hours (usually 4 days including breaks)

## Requirements

- Experience with Python programming
- General familiarity with financial and banking concepts
- Basic familiarity with statistics and mathematical concepts

## Overview

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.

By the end of this training, participants will be able to:

- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use Python, Keras, and TensorFlow to create deep learning models for banking
- Build their own deep learning credit risk model using Python

**Audience**

- Developers
- Data scientists

**Format of the course**

- Part lecture, part discussion, exercises and heavy hands-on practice

## 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 Libraries for Python

- TensorFlow
- Keras

Setting Up Python with the TensorFlow for Deep Learning

- Installing the TensorFlow Python API
- Testing the TensorFlow Installation
- Setting Up TensorFlow for Development
- Training Your First TensorFlow Neural Net Model

Setting Up Python with Keras for Deep Learning

Building Simple Deep Learning Models with Keras

- Creating a Keras Model
- Understanding Your Data
- Specifying Your Deep Learning Model
- Compiling Your Model
- Fitting Your Model
- Working with Your Classification Data
- Working with Classification Models
- Using Your Models

Working with TensorFlow for Deep Learning for Banking

- Preparing the Data
- Downloading the Data
- Preparing Training Data
- Preparing Test Data
- Scaling Inputs
- Using Placeholders and Variables

- Specifying the Network Architecture
- Using the Cost Function
- Using the Optimizer
- Using Initializers
- Fitting the Neural Network
- Building the Graph
- Inference
- Loss
- Training

- Training the Model
- The Graph
- The Session
- Train Loop

- Evaluating the Model
- Building the Eval Graph
- Evaluating with Eval Output

- Training Models at Scale
- Visualizing and Evaluating Models with TensorBoard

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

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