Deep Learning for Finance (with R) Training Course

Course Code



28 hours (usually 4 days including breaks)


  • Experience with R programming
  • General familiarity with finance concepts
  • Basic familiarity with statistics and mathematical concepts


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


  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice

Course Outline


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
    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

Bookings, Prices and Enquiries

Guaranteed to run even with a single delegate!

Private Classroom

From £5000

Private Remote

From £4400 (84)

Public Classroom

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