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
Testimonials
the group activity
Innovative Investors & Financing Co., Inc.; Innovative Investors & Financing Co., Inc.
Credit Risk Management for Consumer Lending Course
CAMPARI / TRUST FORMULA
VIRNA INTAL - Innovative Investors & Financing Co., Inc.
Credit Risk Management for Consumer Lending Course
I like the application cases wherein every topic, she has exercises to apply what we have learned.
CRISTINA MEDINA - Innovative Investors & Financing Co., Inc.
Credit Risk Management for Consumer Lending Course
The whole day was just great and relaxed which really helped. All the printouts really helped.
Louise Mackrell
Corporate Governance Course
The lecturer is very knowledgeable and can substantiate theories with his own personal experiences.
Harry Estipona
Financial Markets Course
real exemples of the trainer
Joana Gomes
Compliance and the Management of Compliance Risk Course
She was very personable and presented a fluid delivery of the course material.
Karli Buckle
Corporate Governance Course
Vajitha was engaging, very knowledgeable, clear and prepared
Cécile McNeil
Corporate Governance Course
Trainer, Richard was very good