Continual Learning

Learn Continual Learning Techniques from Scratch Using PyTorch


What you'll learn
Understand Deep Learning and Neural Networks: Start with core concepts, laying the foundation for advanced deep learning techniques.
Implement Neural Networks Without Libraries: Build neural networks from scratch, deepening your understanding of their core mechanics.
Build Neural Networks with PyTorch: Master the construction of neural networks using PyTorch, a leading deep learning framework.
Learn Continual Learning Techniques: Explore UER, SER, LwF, and EWC, and understand their application in adaptive AI systems.
Implement Continual Learning in PyTorch: Apply continual learning algorithms from scratch using PyTorch, preparing for real-world AI challenges.
Leverage deep learning in resource-constrained environments.

Requirements
Python Programming Language

Description
Unlock the potential of
continual learning
—a cutting-edge approach that allows machine learning models to adapt and learn from new data over time without forgetting previous knowledge. In this comprehensive course, you will gain both a strong theoretical foundation and hands-on experience in implementing
continual learning
techniques using
PyTorch
, one of the most widely used deep learning frameworks.
This course begins by introducing the core concepts of deep learning and neural networks, ensuring a solid understanding of how models learn and evolve. From there, you will dive into key
continual learning
strategies such as
Experience Replay (ER), Knowledge Distillation (KD),
and
Elastic Weight Consolidation (EWC)
. Each technique will be explored in detail, along with practical coding sessions where you'll build these methods from scratch using PyTorch.
By the end of the course, you will:
Master the fundamentals of deep learning
and explore its application in continual learning.
Implement continual learning techniques
from scratch, including experience replay, Elastic Weight Consolidation (EWC), and knowledge distillation.
Understand regularization and normalization methods
to prevent overfitting and manage shifting data patterns over time.
Build and train custom neural networks
that can incrementally learn new tasks without forgetting previous ones.
Apply continual learning algorithms
to both regression and classification problems, preparing you for real-world applications.
Leverage deep learning in resource-constrained environments
.
This course is designed for machine learning enthusiasts, developers, and researchers who want to take their deep learning skills to the next level and become proficient in
continual learning
. Whether you're familiar with PyTorch or new to it, this course will guide you through every step, making it accessible and rewarding for learners at various levels.
Who this course is for:
Continual Learning Enthusiasts

More Info