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          Deep Learning for Banking (with R)培訓

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