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 finance using Python 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 Python, Keras, and TensorFlow to create deep learning models for finance

Build their own deep learning stock price prediction 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 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 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 Finance

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 Model for Stock Price Prediction 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

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

Some experience with excel would help but not essential

Description

This is a beginner level course designed specifically for accounting and business professionals who have no prior knowledge of computer programming.

Python is gaining popularity in the accounting and finance field. Despite professional willingness to learn it, most courses available online are more centric to IT background professionals.

It is a 1 hour and 18 minutes short course for accountants, auditors, and other finance professionals to learn python taking small steps at a time. The aim is to avoid technical jargon and setup complexities so the accountants can get straight to learning rather than frustrating installing software and setting up environments.

Practice exercises use Google Colab Notebook, so you don’t need to install anything on your computer. Google Colab is a cloud-based software that allows you to write your code online. All you need is just a google account.

Class notebooks are attached to either download or linked to open in Google Colab directly.

What we cover in this course:

· Introduction to Programming

· Introduction to Python

· Syntax or Grammar of Programming

· Variables

· Data Formats (Structured Data, Unstructured Data etc.)

· Data Types (String, Numbers, Dates, Boolean etc.)

· Operators(>, =, /, ==, & etc.)

· Conditional Statements (if, else, etc.)

· Loops (For, While)

· Functions

· Data Structures in Python (Lists, Dictionaries, Data Frames )

After completing this course

1. You will have an understanding of computer programming

2. Various Data Structures

3. Write basic Python syntax

4. Familiarity with a notebook, so you know where to write your code.