This training enables participants to design, train, evaluate and improve AI models using PyTorch and Lightning. Participants gain a clear understanding of the full modeling lifecycle, from how models learn from data to how training decisions affect performance and reusability in real-world applications. They develop the ability to reason about modeling choices, including when to build models from scratch and when to adapt or fine-tune existing ones.

By the end of the training, participants are able to confidently train their own neural networks, understand every aspect of the training process, and apply these skills in professional and industrial AI settings.

PyTorch Introduction

Get familiar with PyTorch, its ecosystem, and workflow.

• What is PyTorch?
• Core Components: Tensors, Autograd, Modules
• PyTorch Workflow
• Ecosystem Overview
• Setting up the Development Environment

Working with PyTorch Tensors

Learn how to create, manipulate, and optimize tensors.

• Scalars, Arrays, and Matrices
• Datatypes and Attributes
• Manipulating Tensors
• Tensor Operations (matmul, etc.)
• Reshaping Tensors (reshape, squeeze, permute)
• Creating Views and Stacking
• Using the GPU
• Common Issues with Tensors

Training Our First Model

In this module, you will build and train a simple linear regression model. This will introduce you to the fundamental concepts of model training in PyTorch.

• Single Neuron Linear Regression
• Weights and Biases
• Loss Function
• Backpropagation
• Optimizers
• Learning Rate
• Epochs
• Building a Training Loop
• Visualizing the Model
• Making Predictions

Testing Our First Model

Learn the common strategies for testing machine learning models.

• Splitting Data into Training, Validation, and Test Sets
• Evaluation Functions
• Metrics: Accuracy, F1, Confusion Matrix
• Building a Test Loop
• Visualizing Training Processes
• Overfitting and Underfitting

Building a Classification Model

Create and train a classification model using PyTorch.

• Understanding Classification Problems
• Binary vs Multi-Label Classification
• Analyze Inputs and Outputs
• Turning Data into Tensors
• Setting up Sequential Layers
• Loss Functions for Classification
• Breaking Linearity
• Activation Functions: ReLU, Sigmoid
• From Probability to Classification
• Saving and Loading Models

Data Preprocessing

Before we can start working on more realistic problems. We need to understand how to prepare our data for effective machine learning.

• Data Normalization
• Categorical Encoding
• Handling Missing Data
• Data Augmentation
• Preprocessing Pipelines
• Batching and Shuffling

Datasets and DataLoaders

Learn how to manage and load data efficiently.

• What is a Dataset?
• What is a DataLoader?
• Built-in Datasets in PyTorch
• Creating a Custom Dataset
• Using DataLoader for Batching
• Shuffling and Parallel Loading
• Integration with Transforms
• Practrical Example with Multi-Label Classification

Lightning

Lightning helps you write cleaner, more scalable, and production-ready code by removing boilerplate from training loops. It automates repetitive tasks like checkpointing, logging, and distributed training, so you can focus on the model logic instead of infrastructure details.

• Benefits of Using Lightning
• Migrating our PyTorch Model
• LightningModule and LightningDataModule
• Trainer
• Callbacks
• Checkpointing
• Managing Configuration & Hyperparameters

Experiment Tracking and Visualization with W&B

Track experiments and visualize results using Weights & Biases.

• Why We Need Experiment Tracking
• Using W&B with PyTorch and Lightning
• Viewing Metrics, System Info, and Artifacts
• Optimizing Hyperparameters
• Deployment Options

Optimizing Our Model

Improve model performance through various strategies.

• Building a Base Model
• Improving Data
• Cross-Validation
• Improving Model Architecture
• Changing Training Strategy
• Hyperparameter Search
• Regularization

Exploring Convolutional Neural Networks

Learn CNN fundamentals and apply them to vision tasks.

• Understanding Computer Vision Problems
• Analyze Inputs and Outputs
• Basics of CNNs
• Convolutional and MaxPool Layers
• Using Hardware Acceleration
• Evaluating and Improving the Model

Feature Extraction

Instead of building a complete model yourself, you can use pre-trained models and tame them for your use case.

• Exploring Pre-trained Models
• Transfer Learning
• Freezing Parameters
• Adding a new Classification Layer

Transformer Architecture in a Nutshell

Before we can do anything with an LLM, we need to understand its building blocks.

• Tokenization and Embedding
• Positional Information
• Self-Attention Mechanism
• Overall Structure

Fine-Tuning an LLM with LoRA

Face it, you are not going to train an LLM from scratch. But you can fine-tune a pre-trained model for your specific use case. Learn parameter-efficient fine-tuning for large language models.

• Why We Need Parameter-Efficient Fine-Tuning
• The Core Idea of Low-Rank Adaptation
• Practical Benefits of Using LoRA
• Fine-Tuning a Small LLM Using LoRA

This course combines deep technical expertise with hands-on, instructor-led training, helping you transform knowledge into practical, real-world skills that last.

This 5-day course is intended for data scientists, machine learning engineers, and AI researchers who want hands-on experience in building and deploying neural networks using PyTorch and Lightning. Participants should have a basic understanding of Python programming and some familiarity with machine learning concepts.