Training: Introduction to Deep Learning with PyTorch
Level
BeginnerDuration
24h / 3 daysDate
Individually arrangedPrice
Individually arrangedTraining: Introduction to Deep Learning with PyTorch
The Introduction to Deep Learning with PyTorch course is an intensive 2–3 day program designed for participants who want to learn the practical foundations of deep learning using the popular PyTorch framework. The course combines theory (20%) with extensive hands-on exercises (80%), enabling participants to acquire essential skills in building, training, and testing neural network models from scratch. The training covers key topics such as network architecture, working with data, tensor operations, and training optimization.
What will you learn?
- How to build and train deep neural network models in PyTorch
- How to manage and effectively prepare data for training
- How to optimize and monitor the model training process
- How to apply transfer learning techniques and adapt models to new tasks
- How to prepare models for deployment and integration into real-world systems
- How to independently design and train deep learning models with PyTorch
Who is this training for?
Beginner programmers and data scientists who want to learn deep learning-
Individuals planning to build AI solutions using PyTorch
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Data analysts and engineers looking to expand their ML competencies
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IT specialists and researchers interested in training neural network models
Training Program
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Day 1: PyTorch Fundamentals and Introduction to Deep Learning
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Module 1: Introduction to PyTorch and Working with Tensors
- What is PyTorch and why is it so popular?
- PyTorch vs TensorFlow – quick comparison
- Key advantages of PyTorch
- Creating, operating, and manipulating tensors
- Using GPUs and optimizing hardware performance
- Simple data pipelines for training
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Module 2: Building and Training Simple Neural Networks
- Neural network architecture and key deep learning concepts
- Defining models in PyTorch – layers and activation functions
- Implementing training loops and optimization
- Parameter initialization and the concept of backpropagation
- Building your first network (layers, activations, forward pass)
- Hands-on: creating and training a classifier
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Day 2: Advanced Techniques and Data Handling
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Module 3: Data Management and Dataset Preparation
- Creating and loading datasets (Dataset, DataLoader)
- Data augmentation techniques and train/test splitting
- Data visualization and analysis
- Workshop: preparing a custom dataset for training
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Module 4: Model Optimization and Monitoring
- Choosing loss functions and optimizers
- Regularization, early stopping, and preventing overfitting
- Debugging models and diagnosing common pitfalls
- Introduction to TensorBoard and other visualization tools
- Saving and loading models
- Monitoring metrics and training results
- Preventing overfitting: dropout, early stopping, L2 regularization
- Model evaluation: validation sets, accuracy/F1-score metrics
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Day 3: Practical Projects and Transfer Learning
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Module 5: Transfer Learning and Fine-Tuning
- Using pretrained models in PyTorch for new tasks
- Model adaptation and training of final layers
- Example applications: image classification, NLP tasks
- Different loss functions (MSE, CrossEntropy) and optimizers (SGD, Adam)
- Hyperparameter tuning: learning rate, epochs, batch size
- Hands-on: adapting a pretrained model to a custom problem
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Module 6: Deployment and Model Scaling
- Preparing a model for production
- Basics of model integration with Python applications
- Introduction to optimization and inference acceleration tools
- Project workshop: building a mini AI application based on your own model
