This course offers a comprehensive introduction to deep learning, a key technology driving advancements in industries such as healthcare, retail, and automotive. Deep learning utilizes multi-layered neural networks to solve complex tasks such as image recognition, language translation, and speech processing. The course aims to equip students with the foundational skills needed to train and deploy deep learning models, using modern tools like PyTorch. With practical applications ranging from object detection to personalized experiences, you’ll learn how to apply AI to real-world problems.
Throughout the course, you’ll explore important deep learning concepts such as Convolutional Neural Networks (CNNs), data augmentation, and transfer learning. These techniques are essential for improving model accuracy and efficiency, especially when working with large, complex datasets. The curriculum also covers the use of pre-trained models such as LLMs, which enable faster model training by leveraging existing knowledge. Additionally, you’ll explore advanced topics like recurrent neural networks (RNNs) and natural language processing (NLP), which are crucial for sequential data tasks and text-based applications.
By the end of the course, you’ll apply your knowledge to a final project, where you’ll build an object classification model using computer vision techniques. You’ll enhance model performance through transfer learning and data augmentation, gaining valuable experience in optimizing models with limited data. The course will also guide you through setting up your own AI development environment, preparing you to take on deep learning projects independently. Whether you’re new to AI or looking to expand your skill set, this course provides a solid foundation for anyone interested in the rapidly evolving field of deep learning.
At the end of the workshop, participants can obtain an official certificate from Deep Learning Institute from NVIDIA.
Agenda
Workshop: Fundamentals of Deep Learning (CNN, RNN, LLM) – Nov 29, 2024 – Nov 29, 2024.
Daily Program: Friday, November 29, 2024
Session: Introduction (– Meet the instructor. – Get familiar with your GPU-accelerated
interactive JupyterLab environment.)
Time and Place: (9:00 AM – 9:30 AM)
Session: The Mechanics of Deep Learning (• Train your first computer vision model to learn
the process of training. • Introduce convolutional neural networks to improve accuracy
of predictions in vision applications. • Apply data augmentation to enhance a dataset
and improve model generalization.)
Time and Place: (9:30 AM – 12:30 PM)
Break: Lunch break
Time and Place: (Nov 29, 2024 – Nov 29, 2024)
Session: Pre-trained Models and Large Language Models (Leverage pre-trained models to
solve deep learning challenges quickly. Train recurrent neural networks on sequential
data: • Integrate a pre-trained image classification model to create an automatic doggy
door. • Leverage transfer learning to create a personalized doggy door that only lets in
your dog. • Use a Large Language Model (LLM) to answer questions based on provided
text.)
Time and Place: (1:30 PM – 3:00 PM)
Break: Coffee break
Time and Place: (Nov 29, 2024 – Nov 29, 2024)
Session: Final Project: Object Classification (Apply computer vision to create a model
that distinguishes between fresh and rotten fruit: • Create and train a model that
interprets color images. • Build a data generator to make the most out of small
datasets. • Improve training speed by combining transfer learning and feature
extraction. • Discuss advanced neural network architectures and recent areas of research
where students can further improve their skills.)
Time and Place: (3:15 PM – 4:15 PM)
Session: Final Review (– Complete the assessment to earn a certificate. – Review key
learnings and wrap up questions. – Take the workshop survey.)
Time and Place: (4:15 PM – 4:45 PM)