e-learning
Neural networks using Python
Abstract
About This Material
This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.
Questions this will address
- to do
Learning Objectives
- Initializing model with a single layer (code)
- Loss function
- Model as equation
- How model parameters are learned
- Training steps (code)
- Predictions and save+load models
- Initializing model with multiple layers (code)
- Forward step
- Concept of backprop and epochs
- Training (code)
Licence: Creative Commons Attribution 4.0 International
Keywords: Statistics and machine learning, ai-ml, elixir, jupyter-notebook, work-in-progress
Target audience: Students
Resource type: e-learning
Version: 1
Status: Draft
Prerequisites:
- Foundational Aspects of Machine Learning
- Foundational Aspects of Machine Learning using Python
- Introduction to Galaxy Analyses
- Introduction to Python
- Python - Warm-up for statistics and machine learning
Learning objectives:
- Initializing model with a single layer (code)
- Loss function
- Model as equation
- How model parameters are learned
- Training steps (code)
- Predictions and save+load models
- Initializing model with multiple layers (code)
- Forward step
- Concept of backprop and epochs
- Training (code)
Date modified: 2025-03-11
Date published: 2025-03-11
Scientific topics: Statistics and probability
External resources:
Activity log
