PyTorch Training

Infoshare is the largest tech community in CEE and the organizer of the leading tech conference in Gdańsk. It connects startups, investors, corporations, and innovation enthusiasts. It promotes entrepreneurship, knowledge sharing, and networking. Through events, competitions, and programs, it supports the development of the tech ecosystem in Poland and the region.

The PyTorch training is an intensive two-day course that is 80% based on practical workshops and 20% on theory. The course is designed for participants to gain solid theoretical foundations and practical skills in using PyTorch – one of the most popular frameworks for machine learning. During the training, participants will have the opportunity to work with real data, build and train models, and deploy them in a production environment.

• Programmers and data engineers who want to expand their skills with PyTorch.
• Data scientists wishing to apply PyTorch in their projects.
• Artificial intelligence and machine learning enthusiasts looking to start working with PyTorch.
• For those who know the basics of programming in Python and have basic knowledge of machine learning.
• For those who can work in a Jupyter Notebook or Google Colab environment.

Participants will gain knowledge on how to install and configure PyTorch in their work environment. They will also learn how to build, train, and optimize machine learning models in PyTorch. We will teach you how to prepare and deploy PyTorch models in a production environment. You will learn methods for implementing advanced neural networks, such as CNN and RNN.

1. DAY 1: INTRODUCTION TO PYTORCH AND MACHINE LEARNING BASICS
    • introduction to PyTorch
     – history and development of PyTorch
     – architecture and main components
    • installation and configuration of the environment
     – installing PyTorch and necessary dependencies
     – configuring the work environment (Jupyter Notebook, Google Colab)
    • basics of PyTorch
     – tensor operations, autograd, and computational graphs
     – creating and running simple models
    • workshop: creating your first model
     – implementing a linear model in PyTorch
     – training and evaluating the model on real data

2. DAY 2: ADVANCED TECHNIQUES AND PRACTICAL APPLICATIONS
    • advanced models in PyTorch
     – neural networks and their architecture
     – implementing and training convolutional (CNN) and recurrent (RNN) networks
    • model optimization and fine-tuning
     – optimization and regularization techniques
     – fine-tuning pre-trained models in PyTorch
    • workshop: creating an image classification model
     – preparing and processing image data
     – implementing and training a CNN model for image classification
    • deploying PyTorch models
     – exporting models and preparing for deployment
     – practical aspects of deploying models in a production environment

16 h/2 days