Deep Learning Models and Architectures Course

Name: Deep Learning Models and Architectures Foundation Course
Availability: InStock

Compare neural network families, interpret training evidence, and select suitable deep learning architectures for defined tasks.

Deep Learning Models and Architectures Course

At a glance

Duration: 5 days
Format: Classroom
Cities: Cairo, Abu Dhabi, Phuket, Milan, Bali, Trabzon and more
Next session: 19 – 23 October 2026, Cairo
Average fee: 5,800 €

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Deep Learning Models and Architectures Foundation Course

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Overview

Deep Learning Models and Architectures Foundation Course is a five-day foundation course for analysts, junior data scientists, technical professionals, and managers, who leave with a Deep Learning Architecture Selection Canvas. Participants examine neural network components, training and validation, feedforward, convolutional, sequence, and transformer models, transfer learning, evaluation, overfitting, and responsible use through guided demonstrations. The course links data types and tasks to suitable model families. Agile Leaders Training Center provides training in deep learning models and architectures.

Who Should Attend

Data teams responsible for exploring model options for business problems
Technical teams responsible for discussing deep learning requirements
Analytics teams responsible for evaluating model results and limitations
Project teams responsible for selecting feasible AI approaches
Managers responsible for reviewing deep learning proposals and risks

The course assumes participants can interpret basic data and charts and leaves out advanced mathematics, production deployment, API development, and model coding labs.

Departments and Industries

The course supports foundation-level deep learning decisions across data, technology, and business functions.

Data analytics, data science, information technology, and innovation
Operations, quality, research, and product development
Healthcare, financial services, and professional services
Manufacturing, logistics, retail, and telecommunications
Education, media, and customer service

Learning Objectives

By the end of this course, participants will be able to:

Analyze neural network layers, activations, loss, and training flow
Compare feedforward, convolutional, sequence, and transformer architectures
Apply architecture selection criteria to data and tasks
Evaluate validation results, loss curves, and performance metrics
Diagnose overfitting, bias, and data-quality limitations
Build a deep learning architecture selection canvas

Course Agenda

Day 1: Neural Network Foundations

Input, Hidden, and Output Layer Map
Weight, Bias, and Activation Function Diagram
Forward Pass and Prediction Flow
Loss Function and Learning Objective Card
Gradient-Based Training Concept Demonstration

Day 2: Training and Validation

Training, Validation, and Test Data Split
Epoch, Batch, and Learning Rate Control Sheet
Loss Curve Interpretation Guide
Underfitting and Overfitting Diagnostic
Regularization and Early-Stopping Decision

Day 3: Architecture Families

Feedforward Network Use-Case Matrix
Convolutional Neural Network Feature Map
Recurrent and Sequence Model Flow
Transformer Attention Concept Diagram
Architecture Family Comparison Table

Day 4: Applications and Evaluation

Image, Text, Sequence, and Tabular Task Map
Transfer Learning Suitability Checklist
Classification and Regression Metric Card
Baseline-to-Model Performance Comparison
Bias, Explainability, and Human Review Check

Day 5: Architecture Selection Practice

Suggested Exercise: Map Data Type to Model Family
Suggested Exercise: Diagnose Training Curves
Suggested Exercise: Compare Model Evaluation Evidence
Suggested Exercise: Review Bias and Use Boundaries
Capstone Exercise: Deep Learning Architecture Selection Canvas

Practical Exercises

The course uses suggested activities to turn architecture concepts into reviewable selection decisions.

Suggested activity: trace inputs, layers, activations, predictions, loss, and training flow in a guided model demonstration
Suggested activity: interpret learning curves and distinguish underfitting, overfitting, and unstable training
Suggested activity: compare feedforward, convolutional, sequence, transformer, and transfer-learning options
Suggested activity: select an architecture using task, data, metrics, limitations, bias, and human-review criteria

FAQs

Who suits deep learning models and architectures foundation training?

Deep learning foundation training suits analysts, junior data scientists, technical professionals, project teams, and managers who interpret data and need to compare model families without an advanced coding course.

How does deep learning architecture training differ from general machine learning training?

Deep learning architecture training concentrates on layered neural networks, training behavior, and model families such as convolutional, sequence, and transformer architectures. General machine learning training covers a wider range of statistical and algorithmic methods.

Which deep learning architecture suits image, text, or sequence data?

Architecture suitability depends on the task, data structure, volume, labels, baseline, constraints, and evaluation evidence. Convolutional, sequence, transformer, and feedforward models each provide different inductive structures and tradeoffs.

How are deep learning models evaluated?

Deep learning models are evaluated with task-relevant metrics, validation and test data, learning curves, baseline comparisons, error analysis, bias checks, and human review of practical consequences.

What causes overfitting in deep learning models?

Overfitting occurs when a model fits training patterns more closely than it generalizes to unseen data. Data quality, model complexity, training duration, and regularization choices all affect the pattern.

Conclusion

Participants take back a Deep Learning Architecture Selection Canvas connecting the task, data, model family, training behavior, evaluation evidence, limitations, bias, and review needs. It makes architecture discussions clearer and more consistent. It supports informed decisions about when a model family is suitable and what evidence remains necessary.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 61-76 of 76 events

Location	Dates	Duration	Mode	Price
Johannesburg	Week 29, 2027 25 – 29 July 2027	5 Days	Onsite	€4,500
Amsterdam	Week 31, 2027 2 – 6 August 2027	5 Days	Onsite	€5,700
Langkawi	Week 31, 2027 8 – 12 August 2027	5 Days	Onsite	€6,000
Zoom	Week 32, 2027 9 – 13 August 2027	5 Days	Online	€1,500
Toronto	Week 32, 2027 15 – 19 August 2027	5 Days	Onsite	€12,000
Vienna	Week 34, 2027 23 – 27 August 2027	5 Days	Onsite	€5,700
Montreux	Week 34, 2027 23 – 27 August 2027	5 Days	Onsite	€7,500
San Diego	Week 35, 2027 30 August – 3 September 2027	5 Days	Onsite	€14,000
Casablanca	Week 36, 2027 6 – 10 September 2027	5 Days	Onsite	€4,100
Geneva	Week 36, 2027 12 – 16 September 2027	5 Days	Onsite	€6,200
Paris	Week 37, 2027 13 – 17 September 2027	5 Days	Onsite	€5,700
Cape town	Week 37, 2027 19 – 23 September 2027	5 Days	Onsite	€4,500
Tokyo	Week 38, 2027 20 – 24 September 2027	5 Days	Onsite	€10,000
Al Jubail	Week 39, 2027 3 – 7 October 2027	5 Days	Onsite	€5,700
Barcelona	Week 40, 2027 4 – 8 October 2027	5 Days	Onsite	€5,700
Dubai	Week 41, 2027 11 – 15 October 2027	5 Days	Onsite	€4,500

Frequently asked questions

What does this course cover?

OverviewDeep Learning Models and Architectures Foundation Course is a five-day foundation course for analysts, junior data scientists, technical professionals, and managers, who leave with a Deep Learning Architecture Selection Canvas. Participants examine neural network components, training and validation, feedforward, convolutional, sequence, and transf…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

Deep Learning Models and Architectures Course