Technologies / Artificial Intelligence

Introduction to Deep Learning and Neural Networks for Engineers

The training provides a practical introduction to deep learning and neural networks from an engineering perspective. Participants learn the theoretical foundations and implementation of various neural network architectures through hands-on workshops. The program combines fundamental theory with intensive programming exercises. The classes are conducted in the form of workshops, where each participant independently implements and trains deep learning models.

Issues

Neural network architecture
Activation functions and back propagation
Optimization and regularization
Convolutional Networks (CNN)
Recurrent networks (RNNs)
Transformers and mechanisms of attention
Preparation of training data
Training and evaluation of models
Debugging neural networks
Implementation of models in production
Performance monitoring
Real-time model updates

Benefits

Upon completion of the training, the participant will be able to independently design and implement basic neural network architectures
The ability to effectively prepare data for training deep learning models
Will be able to train and optimize models for specific applications
Will master techniques for debugging and solving common problems in neural networks
Will learn to implement models in a production environment
Will gain practical experience in using popular deep learning frameworks
Will learn methods for monitoring and updating models in production
Will be able to assess the quality of models and interpret their results

Who is this training for?

Software engineers interested in AI

Developers looking to enter the field of deep learning

Data science specialists getting started with neural networks

Data analysts

Systems engineers

AI solution architects

Specialists in process automation

Scientists in technical fields

Prerequisites

Basic knowledge of linear algebra and differential calculus
Experience in Python programming
Understand the basics of machine learning
Knowledge of basic statistics

Training program

Activation functions and back propagation
Optimization and loss functions
Regularization and dropout
Deep network architectures
Convolutional Networks (CNN)
Recurrent networks (RNNs)
Transformers and mechanisms of attention

Practical implementation

Training models

Debugging neural networks

Performance optimization
Export and deployment of models
Monitoring in production
Update models

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

Who is the Introduction to Deep Learning and Neural Networks for Engineers training for?

This training is designed for professionals looking to develop skills in introduction to deep learning and neural networks for engineers. Required level: beginner.

How long is the Introduction to Deep Learning and Neural Networks for Engineers training?

The training lasts 3. Available in online or on-site format.

Will I receive a certificate?

Yes — every participant receives a completion certificate confirming acquired competencies. EITT holds ISO 9001 accreditation.

Can this training be conducted for a closed group?

Yes — we offer dedicated closed trainings for companies. We customize the program to your team's needs. Contact us for an individual quote.