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Mojo programming for beginners from fundamentals to advanced
0 students
Last updated
Jan 2025
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Overview
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Course content
Sections:
18
•
Activities:
0
•
Resources:
57
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Section 1
01 Introduction
001 Introduction
002 What is Mojo
Section 2
02 Setting up the developement environment
001 Write first mojo program in mojo playground
002 Install Mojo in Windows WSL
003 Install Mojo in Windows wsl updated
004 installing docker on windows part one
005 Installing Mojo incDocker Part 2
006 Updated Mojo Installation in Docker
Section 3
03 Python Object and SIMD
001 PythonObject in Mojo
002 SIMD Datatype in Mojo
Section 4
04 Basic Data Types in Mojo
001 Int Data Type
002 Float Data Type
003 Bool Data Type
004 String StringLiteral String Ref
005 Slice In Strings
006 ListLiteral
007 Tuple
008 DynamicVector and InlineFixedVector
Section 5
05 var, let, alias and Conditional Statements
001 var let alias
002 Conditional Statements
003 for and while loop with continue and break
Section 6
06 Functions in Mojo
001 def and fn functions
Section 7
07 Structures in Mojo
001 struct in Mojo
Section 8
08 Memory Management in Mojo
001 inout owned borrowed
Section 9
09 Decorators in Mojo
001 register_passable
002 unroll
003 always_inline
004 parameter
005 noncapturing
Section 10
10 Mojo 0.6 and Traits
001 Updating Mojo to version 06
002 Traits in Mojo
003 Traits Inheritance
004 Traits and Lifecycle Methods
005 Built in traits in Mojo
Section 11
11 Basic ML and Matrix Operations
001 Scarlar Vector Matix and Tensor
002 SIMD splat join add mul
003 vectorize and parallelize tensor row mean
004 Efficient way to create initialise access and print square matrices
005 Square matrices multiplication with a scalar in a vectorize way
Section 12
12 Factorial
001 Factorial of a number
Section 13
13 Mandelbrot
001 Mandelbrot set theory
002 Mandelbrot implementation in mojo
Section 14
14 Juliaset
001 Juliaset theory
002 Juliaset Implementation in Mojo
Section 15
15 Multi Layer Perceptron
001 MLP theory part 1
002 MLP part 2
003 MLP one dimentional array
004 MLP 2 dimensional array
005 MLP create neural network struct
006 MLP feed forward method
007 MLP MSE and derivation sigmoid activation function
009 Final main method
Section 16
16 Error and File handling
001 Error handling part 1
002 Error handling part 2
003 File Handling
Section 17
17 Mojo CLI
001 Mojo CLI Commands
Section 18
18 Conclusion
001 Conclusion
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Mojo programming for beginners from fundamentals to advanced
Course modified date:
20 Jan 2025
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