Training: PyTorch
Level
AdvancedDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedTraining: PyTorch
The PyTorch training is an intensive two-day course, with 80% focused on hands-on workshops and 20% on theory. The course is designed to provide participants with both solid theoretical foundations and practical skills in using PyTorch – one of the most popular machine learning frameworks. During the training, participants will work with real datasets, build and train models, and deploy them in production environments.
What will you learn?
- How to install and configure PyTorch in your working environment
- How to build, train, and optimize machine learning models with PyTorch
- How to implement advanced neural networks such as CNNs and RNNs
- How to prepare and deploy PyTorch models in production environments
Required technical skills
- Basic knowledge of Python programming
- Basic knowledge of machine learning
- Ability to work in Jupyter Notebook or Google Colab environments
Who is this training for?
Developers and data engineers who want to expand their skills with PyTorch-
Data scientists who want to apply PyTorch in their projects
-
AI and ML enthusiasts eager to start working with PyTorch
Training Program
-
Day 1: Introduction to PyTorch and Machine Learning Fundamentals
-
Introduction to PyTorch
- History and development of PyTorch
- Architecture and main components
-
Installation and environment setup
- Installing PyTorch and dependencies
- Setting up the environment (Jupyter Notebook, Google Colab)
-
PyTorch fundamentals
- Operations on tensors, autograd, and computation graphs
- Building and running simple models
-
Workshop: Building your first model
- Implementing a linear model in PyTorch
- Training and evaluating the model on real data
-
Introduction to PyTorch
-
Day 2: Advanced Techniques and Practical Applications
-
Advanced models in PyTorch
- Neural networks and their architecture
- Implementing and training Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
-
Model optimization and fine-tuning
- Optimization and regularization techniques
- Fine-tuning pretrained models in PyTorch
-
Workshop: Building an image classification model
- Preparing and processing image data
- Implementing and training a CNN for image classification
-
Deploying PyTorch models
- Exporting models and preparing for deployment
- Practical aspects of deploying models in production environments
-
Advanced models in PyTorch
