Introduction to Julia Programming: Machine-Learning Models and AI
Course 1267
3 DAY COURSE

As machine learning and artificial intelligence algorithms grow more sophisticated, the need for a high-performance development environment grows greater and greater. Julia is a programming language designed to feel like a comfortable scripting environment, like Python, but able to deliver the high performance of fully compiled languages like C and Fortran.

In this course, we introduce the fundamentals of coding in Julia, always with an eye towards programming techniques currently finding application in cutting-edge machine learning and artificial intelligence.

Introduction to Julia Programming: Machine-Learning Models and AI Benefits

• In this Julia programming training, you will learn how to:

  • Craft efficient code in the high-performance programming language, Julia
  • Create machine-learning models in Julia
  • Understand the vector and matrix methods common to all neural network models
  • Interact with other AI platforms, like PyTorch and TensorFlow

• Prerequisites

  Attendees must have programming experience.

Julia Machine-Learning Models Training Outline

Chapter 1 – Introduction and Overview

What is Julia?

LLVM

Installing and Using Julia

The Julia REPL

• semicolon works as in MATLAB

Julia IDEs

• Installing the Julia kernel for Jupyter notebooks
• VS Code

Hands-On Exercise 1.1

Chapter 2 – Fundamentals of the Julia Language

Variables and Types in Julia

• Integers
  • No overflow checking
• Floats
• Strings
  • Characters versus strings
  • Strings are assumed to be UTF-8
  • print
  • println
  • formatted printing
• Dates

Using Latex Symbols

Best Practices for Datatypes

Best practice:

• Ensure the compiler can correctly deduce type

Hands-On Exercise 2.1

• Julia DataFrames
• Interoperating with Pandas DataFrames

Julia Operators and Functions

Functions and operators

• pipe operator
• Function composition
• Tuple arguments are immutable
• Array arguments are mutable
• Variable number of arguments
• Broadcasting a function
• Anonymous functions

Contents - Multiple Dispatch

Multiple Dispatch

• Function Signatures

Hands-On Exercise 2.2

• Julia Macros

Hands-On Exercise 2.3

Chapter 3 – Julia Arrays

Arrays

• Julia matrices are in column-major order
• Linear and Cartesian indexes
• EachIndex operator
• Arrays with custom indices

Hands-On Exercise 3.1

• Applications of Matrices
• Special Array and Matrix types
• Introduction to Matrices in Artificial Intelligence

Hands-On Exercise 3.2

• Introductory numerical analysis
• Matrices – Norms and Conditioning
• Differential Equations

Hands-On Exercise 3.3

Chapter 4 – Input and Output

FileIO Package

Standard File Types

Implementing Loaders and Saves

Hands-On Exercise 4.1

• Graphics Output
• Plotting from the Julia REPL
• Plotting in Julia Notebooks

Hands-On Exercise 4.2

Chapter 5 – Putting machine learning theory into practice

Statistical modeling

Machine Learning

Hands-On Exercise 5.1

Chapter 6 – Neural Networks with Julia

Neural Network Basics in Julia

Hands-On Exercise 6.1

Advanced Neural Network Libraries in Julia

Performance Tuning for Neural Networks

Quantization of Neural Networks

Hands-On Exercise 6.2

Chapter 7 – Debugging, Profiling, and High-Performance Julia

The Julia Debugger

High-Performance Julia

Principles of high-performance programming

Profiling Julia code

Hands-On Exercise 7.1

• Parallel Processing
• Multithreading
• Multiprocessing
• Distributed processing

Hands-On Exercise 7.2

Chapter 8 – Interoperating with other Artificial Intelligence Platforms

Julia with TensorFlow and PyTorch

ONNX

Creating a computer vision system

Picking a model from the “zoo”

ResNet

Hands-On Exercise 8.1

Chapter 9 – Course Summary