Welcome to the MojoCodeCollection repository! This repository contains a series of programs and experiments written in Mojo, designed to explore various programming concepts, optimizations, and machine learning models. Below is an overview of the content and purpose of each file in this repository.
- These files contain basic Mojo programs that cover fundamental programming concepts, including:
- Variables
- Conditional logic
- Iterations
- Basic data handling
- This Jupyter Notebook explains the usage of functions, constructors, and structures in Mojo. It includes examples and explanations to help understand how to implement and use these features in Mojo programming.
- decorators_usage: Demonstrates the usage of Mojo decorators such as
@valueand@register. - python_libs_usage: Shows how to utilize Python libraries like
randomandpandaswithin Mojo. - simd_vector_usage: Illustrates the usage of SIMD (Single Instruction, Multiple Data) vector operations in Mojo for enhanced performance.
- 41.mojo: Examines performance optimization techniques in Mojo for the matrix transpose problem.
- 42.mojo: Focuses on the performance improvements achieved through parallelization in matrix addition and subtraction.
- 43.mojo: Implements a multi-layer perceptron (MLP) model, showcasing machine learning capabilities in Mojo.
- 51.mojo: Demonstrates Mojo programming performance with optimization techniques by generating a calendar for the years 2010-2030.
- 52.mojo: Implements a multi-layer perceptron (MLP) model in Mojo with three inputs and at least three nodes in the hidden layer, designed for the implementation of universal logical gates.
- Clone the Repository:
git clone https://github.com/starplatinummaster/MojoCodeCollection.git
- Refer the official documentation of MOJO: https://docs.modular.com/mojo/manual/