Technologies / Programming

Using Microsoft Cognitive Toolkit (CNTK)

Advanced training on using CNTK tools in machine learning and neural networks. Participants learn practical library applications, deep learning model implementation techniques, and performance optimization. Workshops combine theory with intensive programming exercises.

Required participant preparation

Knowledge of machine learning
Python programming experience
Basic knowledge of linear algebra
Knowledge of neural networks

Benefits

Ability to implement models in CNTK
Knowledge of library architecture
Model optimization capability
Deep learning knowledge
Data processing ability
Knowledge of training techniques
Model deployment ability
Network debugging skills

Who is this training for?

Data Scientists

Machine learning engineers

AI programmers

Deep learning researchers

Data analysts

Artificial intelligence specialists

Python/C++ programmers

ML solution architects

Training program

Computational models
Tensor operations

Neural network implementation
Network architectures
Layers and activations

Optimizers
Transfer learning

Distributed processing
Performance optimization
Model deployment

Performance evaluation
Model deployment
System integration

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

Training Topics

Frequently asked questions

What are the prerequisites for this training?

The Using Microsoft Cognitive Toolkit (CNTK) training does not require specialized prior knowledge. Basic IT knowledge is sufficient.

What is the format and duration of this training?

The training lasts 4 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Data Scientists; Machine learning engineers; AI programmers.