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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

Quantisation 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 23 730 kr

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

Include dates with afternoon start times

#1267

aug 15 - 17 9:00 - 16:30 CEST

Stockholm or Virtual
aug 15 - 17 10:00 - 18:00 CEST

London or Virtual
aug 16 - 18 15:00 - 22:30 CEST

Herndon, VA or Virtual
nov 7 - 9 9:00 - 16:30 CET

Stockholm or Virtual
nov 7 - 9 10:00 - 18:00 CET

London or Virtual
nov 15 - 17 15:00 - 22:30 CET

Herndon, VA or Virtual
feb 6 - 8 9:00 - 16:30 CET

Stockholm or Virtual
feb 6 - 8 10:00 - 18:00 CET

London or Virtual
feb 7 - 9 15:00 - 22:30 CET

Herndon, VA or Virtual
maj 22 - 24 15:00 - 22:30 CEST

Herndon, VA or Virtual

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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.