Specialised Technical Course
Advanced Deep Learning and Neural Networks
Develop the ability to assess deep learning experiments through neural network design, data preparation, training behaviour and evidence of model performance.
Course Overview
Examines deep learning model design, training and evaluation. Participants review neural network choices, data preparation and the interpretation of model performance and limitations.
Who This Course Is For
- Machine learning engineers developing models
- Data scientists reviewing neural networks
- Software engineers supporting model evaluation
- Technical specialists assessing industrial AI
What Participants Will Learn
Compare neural network choices against objectives
Assess data preparation and evaluation boundaries
Interpret training behaviour and experiment evidence
Evaluate model performance against stated requirements
Explain model limitations and further checks
Key topics
- Network architecture
- Data preparation
- Training behaviour
- Model evaluation
Course Modules
Back to Course OverviewDeep Learning Experiment Context
Deep Learning Experiment Context
Module overview
Review how network design, data preparation, training and evaluation connect within a deep learning experiment.
Key topics
Network architecture · Data preparation · Training behaviour · Model evaluation
Learning outcome
Identify the decisions and evidence required to assess a deep learning experiment.
Learning format
Technical explanation · worked examples · model exercise
Data Preparation and Evaluation Boundaries
Data Preparation and Evaluation Boundaries
Module overview
Review input preparation and the separation of data used for learning and evaluation.
Key topics
Input data · Preparation choices · Evaluation boundaries · Data limitations
Learning outcome
Explain data preparation choices and identify limitations affecting evaluation.
Learning format
Technical explanation · worked examples · model exercise
Neural Network Architecture Choices
Neural Network Architecture Choices
Module overview
Compare network design choices against the task, available data and requirements for interpreting results.
Key topics
Network architecture · Task requirements · Model complexity · Design assumptions
Learning outcome
Justify a network design choice and identify assumptions requiring evaluation.
Learning format
Technical explanation · worked examples · model exercise
Training Behaviour and Experiment Records
Training Behaviour and Experiment Records
Module overview
Review training behaviour and organise the records needed to compare model experiments.
Key topics
Training behaviour · Experiment settings · Performance trends · Comparison records
Learning outcome
Interpret training evidence and define records needed for a meaningful experiment comparison.
Learning format
Technical explanation · worked examples · model exercise
Model Evaluation and Limitations
Model Evaluation and Limitations
Module overview
Assess model outputs against the stated task and identify evidence gaps affecting conclusions about performance.
Key topics
Model evaluation · Output interpretation · Error review · Model limitations
Learning outcome
Explain evaluation results and identify limitations needing further investigation.
Learning format
Technical explanation · worked examples · model exercise
Deep Learning Experiment Review
Deep Learning Experiment Review
Module overview
Assess a complete experiment and present findings about design, data, training and evaluation.
Key topics
Model exercise · Experiment evidence · Evaluation review · Limitation reporting
Learning outcome
Participants can assess a deep learning experiment and explain its evaluation results.
Learning format
Lectures, model exercises and technical review
Programme Information
Course duration, location, practical components, training-centre information and applicable certification arrangements depend on the selected programme.
Learning format: Lectures, model exercises and technical review
Request the Complete Course Information