Deep Learning for Banking (with Python)

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