## Duration

28 hours (usually 4 days including breaks)

## Requirements

- Experience with R programming
- General familiarity with finance 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. 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 finance using R as they step through the creation of a deep learning stock price prediction 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 finance
- Use R to create deep learning models for finance
- Build their own deep learning stock price prediction model using R

**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 Finance

- Neural Networks
- Natural Language Processing
- Image Recognition
- Speech Recognition
- Sentimental Analysis

Exploring Deep Learning Case Studies for Finance

- Pricing
- Portfolio Construction
- Risk Management
- High Frequency Trading
- Return Prediction

Understanding the Benefits of Deep Learning for Finance

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 Model for Stock Price Prediction 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