Introduction to Julia Programming for Artificial Intelligence Training

Level: Intermediate

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.

Key Features of this Julia Programming Training:

  • After-course instructor coaching included
  • Learning Tree end-of-course exam included

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 neutral network models
  • Interact with other AI platforms, like PyTorch and TensorFlow

Choose the Training Solution That Best Fits Your Individual Needs or Organizational Goals


In Class & Live, Online Training

  • 3-day instructor-led training course
  • After-course instructor coaching included
  • Learning Tree end-of-course exam included
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Standard $2745 CAD

Government $2415 CAD




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In Class & Live, Online Training

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Note: This course runs for 3 Days

  • May 11 - 13 9:00 AM - 4:30 PM EDT Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare) Reserve Your Seat

  • Aug 10 - 12 9:00 AM - 4:30 PM EDT Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare) Reserve Your Seat

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Important Julia Programming Training Information

  • Prerequisites

    Attendees must have programming experience.
  • Exam Information

    Attendees will have the opportunity to take the Learning Tree exam upon completion.

Julia Programming 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 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

Team Training

Julia Programming Training FAQs

  • I am new to programming, what experience do I need going into this course?

    This course introduces Julia but assumes the student has experience with some programming language such as Python, C#, or Java.
  • I am a manager trying to develop better knowledge of the Julia programming language, is this the right course for me?

    This course explores some of the more technical aspects of neural networks and is probably not suitable for managers and non-technical students.
  • I am a developer. Can I take this class?

    Yes! This course is designed for developers, and programmers, who wish to delve deeper into neural networks and AI.
  • I’m a developer who wishes to apply existing neural network architectures. Is this class suitable for me?

    Not likely. Developers wishing only to apply existing neural network architectures might be better served by a course in PyTorch or TensorFlow.
Herndon, VA / Online (AnyWare)
Herndon, VA / Online (AnyWare)
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