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Introduction to Julia Programming: Machine-Learning Models and AI

Introduction to Julia Programming: Machine-Learning Models and AI

Course 1267

Duration: 3 days
Labs: Yes
Language: English
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.

Julia Machine-Learning Models Training Delivery Methods

In-Person
Live-Online

Julia Machine-Learning Models Training Course Information

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

Get This Course $2,650

3-day instructor-led training course
After-course instructor coaching included
Learning Tree end-of-course exam included

#1267

Feb 7 - 9 9:00 AM - 4:30 PM EST

Herndon, VA or Virtual
May 22 - 24 9:00 AM - 4:30 PM EDT

Herndon, VA or Virtual
Aug 14 - 16 9:00 AM - 4:30 PM EDT

Herndon, VA or Virtual

3-day instructor-led training course
After-course instructor coaching included
Learning Tree end-of-course exam included

#1267

Feb 7 - 9 9:00 AM - 4:30 PM EST

Herndon, VA or Virtual
May 22 - 24 9:00 AM - 4:30 PM EDT

Herndon, VA or Virtual
Aug 14 - 16 9:00 AM - 4:30 PM EDT

Herndon, VA or Virtual

Bring this or any training to your organization
Full-scale program development
Delivered when, where, and how you want it
Blended learning models
Tailored content
Expert team coaching

#1267

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Julia Machine-Learning Models Training FAQs

This course introduces Julia but assumes the student has experience with some programming language such as Python, C#, or Java.

This course explores some of the more technical aspects of neural networks and is probably not suitable for managers and non-technical students.

Yes! This course is designed for developers, and programmers, who wish to delve deeper into neural networks and AI.

Not likely. Developers wishing only to apply existing neural network architectures might be better served by a course in PyTorch or TensorFlow.

Introduction to Julia Programming: Machine-Learning Models and AI

Julia Machine-Learning Models Training Delivery Methods

Julia Machine-Learning Models Training Course Information

Julia Machine-Learning Models Training Outline

Chapter 1 – Introduction and Overview

Chapter 2 – Fundamentals of the Julia Language

Chapter 3 – Julia Arrays

Chapter 4 – Input and Output

Chapter 5 – Putting machine learning theory into practice

Chapter 6 – Neural Networks with Julia

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

Chapter 8 – Interoperating with other Artificial Intelligence Platforms

Chapter 9 – Course Summary

Julia Machine-Learning Models Training FAQs

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

I am a manager trying to develop better knowledge of the Julia programming language, is this the right course for me?

I am a developer. Can I take this class?

I’m a developer who wishes to apply existing neural network architectures. Is this class suitable for me?

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