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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
Requirements
- Experience with R programming
- General familiarity with finance concepts
- Basic familiarity with statistics and mathematical concepts
28 Hours
