Deep Learning Fundamentals and Neural Network Design Course

Master neural-network design, optimisation, modern architectures, transfer learning and responsible deployment through practical deep-learning work.

Artificial Intelligence and Machine Learning Courses Confirmed

Deep Learning Fundamentals and Neural Network Design Course

Training Outlines

Introduction

Deep learning enables powerful analysis of images, language, audio and complex data, but reliable results depend on sound architecture, representative data and disciplined training. Professionals must understand how neural networks learn, why training becomes unstable and when a simpler machine-learning method may provide a stronger operational solution.

This five-day fundamentals course provides a rigorous, practical foundation in neural-network design. Participants will build and diagnose neural models, compare major architectures, apply transfer learning and evaluate performance beyond headline accuracy. The course develops sufficient technical depth for informed design and implementation without assuming advanced research experience.

Objectives

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

Explain how neural networks learn representations.
Design suitable network inputs, outputs and losses.
Apply backpropagation and modern optimisation methods.
Diagnose unstable training and overfitting.
Compare convolutional, recurrent and transformer architectures.
Apply transfer learning and controlled fine-tuning.
Evaluate robustness, interpretation and efficiency.
Design a production-ready deep-learning workflow.

Course Content

Day 1: Neural-Network Design Foundations

Tensors, layers, weights and activations
Forward propagation and network outputs
Loss functions and task formulation
Depth, width and model capacity
Training, validation and test design
Feed-forward network laboratory

Day 2: Training and Optimisation

Backpropagation and gradient calculation
Mini-batch learning and optimisation
Learning rates and scheduling
Initialisation and normalisation
Regularisation and early stopping
Training-diagnosis laboratory

Day 3: Modern Neural Architectures

Convolutional networks for visual data
Recurrent and gated sequence models
Attention and transformer architecture
Embeddings and learned representations
Architecture-selection trade-offs
Comparative architecture laboratory

Day 4: Transfer Learning and Model Evidence

Pretrained models and feature extraction
Freezing and fine-tuning strategies
Data augmentation and label validity
Hyperparameter experimentation
Calibration and structured error analysis
Model-evidence review exercise

Day 5: Deployment and Responsible Design

Batch, real-time and edge inference
Latency and computational efficiency
Drift and performance monitoring
Robustness and failure controls
Privacy, fairness and human oversight
Capstone neural-solution design

Training Locations

This Deep Learning Fundamentals and Neural Network Design Course is available in multiple cities. Please select your preferred location from the list below

Durrës

Albania

Tirana

Albania

Andorra la Vella

Andorra

Escaldes-Engordany

Andorra

Innsbruck

Austria

Salzburg

Austria

Vienna

Austria

Gomel

Belarus

Minsk

Belarus

Antwerp

Belgium

Brussels

Belgium

Banja Luka

Bosnia and Herzegovina

Sarajevo

Bosnia and Herzegovina

Plovdiv

Bulgaria

Sofia

Bulgaria

Dubrovnik

Croatia

Split

Croatia

Zagreb

Croatia

Limassol

Cyprus

Nicosia

Cyprus

Brno

Czech Republic

Prague

Czech Republic

Aarhus

Denmark

Copenhagen

Denmark

Tallinn

Estonia

Tartu

Estonia

Helsinki

Finland

Tampere

Finland

Lyon

France

Marseille

France

Nice

France

Paris

France

Berlin

Germany

Frankfurt

Germany

Hamburg

Germany

Munich

Germany

Athens

Greece

Thessaloniki

Greece

Budapest

Hungary

Debrecen

Hungary

Akureyri

Iceland

Reykjavík

Iceland

Jakarta

Indonesia

Cork

Ireland

Dublin

Ireland

Florence

Italy

Milan

Italy

Naples

Italy

Rome

Italy

Osaka

Japan

Tokyo

Japan

Pristina

Kosovo

Prizren

Kosovo

Liepāja

Latvia

Riga

Latvia

Schaan

Liechtenstein

Vaduz

Liechtenstein

Kaunas

Lithuania

Vilnius

Lithuania

Esch-sur-Alzette

Luxembourg

Luxembourg City

Luxembourg

Kuala Lumpur

Malaysia

Malé

Maldives

St. Julian's

Malta

Valletta

Malta

Bălți

Moldova

Chișinău

Moldova

La Condamine

Monaco

Monte Carlo

Monaco

Budva

Montenegro

Podgorica

Montenegro

Amsterdam

Netherlands

Rotterdam

Netherlands

The Hague

Netherlands

Abuja

Nigeria

Lagos

Nigeria

Port Harcourt

Nigeria

Ohrid

North Macedonia

Skopje

North Macedonia

Bergen

Norway

Oslo

Norway

Gdańsk

Poland

Kraków

Poland

Warsaw

Poland

Faro

Portugal

Lisbon

Portugal

Porto

Portugal

Bucharest

Romania

Cluj-Napoca

Romania

City of San Marino

San Marino

Serravalle

San Marino

Belgrade

Serbia

Novi Sad

Serbia

Singapore

Bratislava

Slovakia

Košice

Slovakia

Bled

Slovenia

Ljubljana

Slovenia

Incheon

South Korea

Seoul

South Korea

Barcelona

Spain

Madrid

Spain

Valencia

Spain

Sri Jayawardenepura Kotte

Sri Lanka

Gothenburg

Sweden

Stockholm

Sweden

Bern

Switzerland

Geneva

Switzerland

Zurich

Switzerland

Kaohsiung

Taiwan

Taipei

Taiwan

Ankara

Turkey

Istanbul

Turkey

Kyiv

Ukraine

Lviv

Ukraine

Odesa

Ukraine

Dubai

United Arab Emirates

Birmingham

United Kingdom

Edinburgh

United Kingdom

London

United Kingdom

Manchester

United Kingdom

Rome (Vatican-adjacent)

Vatican City

Training Schedule

Below is the table of cities along with the respective dates for the upcoming training sessions of Deep Learning Fundamentals and Neural Network Design Course. Please review the schedule to find the most convenient option for you. You can also use the below search bar to type the city name and filter the results.