Deep learning via rust
deep learning via rust
Modules
7
Lessons
39
Course created by Mikias Abebe
Module 1
Linear Algebra and Tensors in Rust
Master the mathematical foundations of machine learning by exploring multi-dimensional arrays and tensor operations using Rust's ndarray crate.
Module 2
Neural Network Foundations
Build the core components of a neural network, from single neurons to fully connected layers and activation functions, natively in Rust.
Module 3
Backpropagation and Computational Graphs
Dive deep into the mechanics of learning by implementing an automatic differentiation (autograd) system and the backpropagation algorithm.
Module 4
Architecting Deep Learning Models
Leverage Rust's advanced type system, traits, and memory management to build a robust and extensible deep learning architecture.
Module 5
Optimization and the Training Loop
Learn how to update model weights efficiently by implementing optimizers and structuring a complete training loop with data batching.
Module 6
The Rust Deep Learning Ecosystem and Acceleration
Transition from building from scratch to utilizing industry-standard Rust machine learning frameworks and GPU acceleration.
Module 7
Final Assessment
A comprehensive evaluation covering tensor manipulation, autograd mechanisms, model architecture, and the Rust deep learning ecosystem.