Deep Learning For Banking (with Python)

Langue : UKRéférence : DLBDurée : 4 jours
Lieu : BertrangePrix : A définir
Date non disponible actuellement :

Cette formation est organisée uniquement à la demande d'un client et sera adaptée à ses besoins spécifiques.
Le coût de celle-ci sera donc déterminé par un devis personnalisé (avec This email address is being protected from spambots. You need JavaScript enabled to view it. ou This email address is being protected from spambots. You need JavaScript enabled to view it. au 53 28 20 1).

Contenu :

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. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R 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 R to create deep learning models for banking
- Build their own deep learning credit risk model using R

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 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

Pré-requis :

Requirements:
- Basic experience with R programming
- General familiarity with financial and banking concepts
- Basic familiarity with statistics and mathematical concepts

Audience:
- Developers
- Data scientists

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