DAT 310 - Applied Deep Learning

Course Description

What may be natural for you may not be so easy for Artificial Intelligence. Acquire the necessary deep learning knowledge and skills required to work on an AI-related project.

Develop the mathematical foundations and key concepts of deep learning which will allow you to build and train deep neural networks. Use popular frameworks such as Tensorflow and PyTorch in real-world use cases within Computer Vision and Natural Language Processing (NLP).

This course is delivered in collaboration with WeCloudData.

Course Details

Learning Outcomes

By the completion of this course, successful students will be able to:

• Explain deep neural network algorithms, the mathematics concepts, and optimization techniques
• Evaluate three use cases of deep learning techniques in structured data analysis, computer vision, and natural language processing (NLP)
• Apply deep learning model training, tuning, and optimization using popular open-source tools such as Tensorflow/Keras or PyTorch for computer vision tasks
• Apply Transformer techniques such as BERT and GPT-3 for common NLP tasks

Topics

• Math Foundations for Deep Neural Networks
• Introduction to Neural Networks
• Introduction to Deep Learning Frameworks: Tensorflow or PyTorch
• Specialized Topic: Computer Vision
• Specialized Topic: Natural Language Processing
• GPUs and Model Tunings
• Build your first deep learning application

Who is this course for?

This course is designed for:

• Data and business analytics professionals who wish to learn about the latest AI tools to stay ahead of the curve
• IT and Engineering Professionals looking to receive hands-on training in ML and AI
• Recent graduates and academics in Computer Science

Notes

Software Requirements

To complete the lab activities, you will require:

A Google Colab account (Gmail)

Applies Towards the Following Program(s)

• Deep Learning and Scalable Machine Learning : Required