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.
Attendees must have programming experience.
Attendees will have the opportunity to take the 4thdacad exam upon completion.
Introduction to Julia Programming for Artificial Intelligence Training Delivery Methods
After-course instructor coaching included
4thdacad end-of-course exam included
Introduction to Julia Programming for Artificial Intelligence Training Course Benefits
Craft efficient code in the high-performance programming language, JuliaCreate machine-learning models in JuliaUnderstand the vector and matrix methods common to all neutral network modelsInteract with other AI platforms, like PyTorch and TensorFlow
Julia Programming Training Outline
Chapter 1 – Introduction and Overview
What is Julia?
Installing and Using Julia
The Julia REPL
semicolon works as in MATLAB
Installing the Julia kernel for Jupyter notebooks
Hands-On Exercise 1.1
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
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