Fundamentals of Deep Learning

Learn the fundamental techniques and tools required to train a deep learning model.

Businesses worldwide are using artificial intelligence to solve their greatest challenges. Healthcare professionals use AI to enable more accurate, faster diagnoses in patients. Retail businesses use it to offer personalized customer shopping experiences. Automakers use it to make personal vehicles, shared mobility, and delivery services safer and more efficient. Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software

Learning Objectives

1. Learn the fundamental techniques and tools required to train a deep learning model
2. Gain experience with common deep learning data types and model architectures
3. Enhance datasets through data augmentation to improve model accuracy
4. Leverage transfer learning between models to achieve efficient results with less data and computation
5. Build confidence to take on your own project with a modern deep learning framework

Topics Covered

1. PyTorch
2. Convolutional Neural Networks (CNNS)
3. Data Augmentation
4. Transfer Learning
5. Natural Language Processing