This course provides a comprehensive guide to using the Julia programming language for deep learning applications. Learn to build, train, and deploy neural networks with Julia's efficient libraries, focusing on practical implementation and performance.
Prerequisites Julia basics, ML concepts
Level:
Julia's Deep Learning Ecosystem
Understand the components and advantages of using Julia for deep learning tasks.
Flux.jl Proficiency
Develop skills to build various neural network architectures using Flux.jl.
Automatic Differentiation
Grasp and apply automatic differentiation techniques in Julia for model training.
Data Handling for Deep Learning
Learn to prepare and manage datasets for deep learning models within Julia.
Model Training and Optimization
Implement training loops, select appropriate optimizers, and evaluate model performance.
GPU Acceleration
Utilize Julia's capabilities for accelerating deep learning computations on GPUs.