Deep Learning for Banking (with Python) Training Course

Course Code

dlforbankingwithpython

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

Testimonials

★★★★★
★★★★★

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