PyTorch Neural Network Development Training Course

Course overview

Teams building AI-enabled products need more than a model that trains in a notebook. They need engineers who can structure PyTorch projects, select appropriate architectures, diagnose unstable training, measure performance against business-relevant metrics, and package models for reliable deployment. This course addresses the gap between introductory deep learning knowledge and the practical work of developing maintainable neural-network solutions for image, text, tabular, and sequence data. Participants learn to make defensible technical choices rather than relying on copied training scripts or opaque pre-trained models.

Across five days, participants develop neural networks with PyTorch using tensors, autograd, nn.Module, Dataset and DataLoader, custom training loops, and GPU acceleration. The course covers convolutional neural networks, transfer learning with torchvision models, sequence modelling, attention concepts, regularisation, hyperparameter tuning, experiment tracking, model evaluation, error analysis, checkpointing, and ONNX export. Participants also learn to identify data leakage, class imbalance, overfitting, training bottlenecks, and poor calibration before a model reaches users or production systems.

Instruction combines guided code walkthroughs with structured labs using realistic datasets and engineering constraints. Each participant builds a version-controlled PyTorch model-development project that includes data pipelines, a reusable model class, training and validation code, TensorBoard experiment logs, an evaluation report, saved checkpoints, and an exported ONNX model. The final workshop requires participants to justify their architecture, metrics, operating threshold, and deployment recommendation to a technical review panel.

The course is best suited to software engineers, data scientists, machine learning practitioners, and technical leads who already work with Python and need hands-on capability in PyTorch rather than a theory-only introduction to AI.

Course objectives

By the end of this course, participants will be able to:

• Build reusable PyTorch model classes with nn.Module, forward passes, parameters, and loss functions
• Create efficient Dataset and DataLoader pipelines with transforms, batching, shuffling, and validation splits
• Implement custom training and validation loops using autograd, optimizers, learning-rate schedules, and GPU devices
• Develop convolutional neural networks and transfer-learning workflows with torchvision pre-trained models
• Evaluate classification models using confusion matrices, precision, recall, F1 score, ROC-AUC, and threshold selection
• Diagnose overfitting, data leakage, class imbalance, and training instability through structured error analysis
• Track and compare experiments with TensorBoard metrics, learning curves, parameter settings, and model checkpoints
• Export a validated PyTorch model to ONNX and produce a deployment-ready model evaluation report

Benefits of attending

Target competencies

Who should attend

• Machine Learning Engineers — who need to build, test, and operationalise PyTorch models in product or platform teams
• Data Scientists — who need to move from exploratory notebooks to reproducible neural-network training workflows
• Software Engineers — who are integrating trained models into applications and need practical deep learning implementation skills
• AI Engineers — who need to select architectures, tune training runs, and evaluate model behaviour rigorously
• Computer Vision Engineers — who need to adapt torchvision models for image classification and inspection use cases
• Technical Leads — who review model-development approaches and need to assess performance, risk, and deployment readiness

Requirements and prerequisites

Participants should be comfortable writing and running Python functions, classes, loops, and data-processing code. They should understand NumPy-style arrays, basic pandas data handling, train/validation/test splits, and the purpose of supervised learning. Familiarity with linear algebra concepts such as vectors, matrices, and matrix multiplication is assumed, along with a basic understanding of gradients and loss functions. Participants should have used a Python environment such as JupyterLab or VS Code. Prior PyTorch experience is not required, and advanced calculus, research-level deep learning theory, or previous production deployment experience is not assumed.

Training methodology

The instructor demonstrates each PyTorch workflow in a live coding environment, then participants apply it in timed labs using supplied datasets and starter repositories. Short technical briefings explain the design rationale behind autograd, architecture selection, optimisation, and evaluation before hands-on implementation begins. Participants inspect failed training runs, compare competing experiments in TensorBoard, and review model decisions in small groups. Daily exercises add components to a single end-to-end project. The final session uses a technical design review format to turn each participant’s model evidence into a practical implementation plan.

Course outline

Day 1: PyTorch foundations and reproducible data pipelines

• PyTorch tensors, tensor shapes, broadcasting, indexing, and device placement
• Automatic differentiation with autograd and computational graphs
• The nn.Module structure, forward method, parameters, and buffers
• Loss functions for regression, binary classification, and multiclass classification
• Dataset and DataLoader construction for batched training
• Train, validation, and test partitioning with leakage controls
• Reproducibility through random seeds, configuration files, and project structure

Workshop: Build a reproducible PyTorch data pipeline and baseline classifier, producing a project repository with documented splits and a first validation result.

Day 2: Training loops, optimisation, and model diagnosis

• Custom training loops with forward pass, loss calculation, backward pass, and optimizer step
• SGD, Adam, AdamW, weight decay, and optimizer selection
• Learning-rate schedules and warm-up strategies
• GPU acceleration, CUDA device management, and memory-aware batching
• Regularisation with dropout, batch normalisation, and early stopping
• Checkpointing model weights, optimizer state, and training metadata
• TensorBoard logging for losses, metrics, parameter distributions, and learning curves

Workshop: Implement a GPU-enabled training loop with checkpoints and TensorBoard logging, then diagnose and improve an intentionally overfitting model.

Day 3: Computer vision and transfer learning with torchvision

• Image tensors, normalisation, augmentation, and torchvision transforms
• Convolutional layers, pooling, receptive fields, and feature maps
• CNN architecture choices for image classification tasks
• Pre-trained torchvision models and feature-extractor reuse
• Fine-tuning strategies for frozen and unfrozen network layers
• Class imbalance handling with weighted losses and sampling
• Visual error analysis using misclassified image batches

Workshop: Fine-tune a torchvision pre-trained image classifier and produce a comparison of baseline, feature-extraction, and full fine-tuning results.

Day 4: Sequence models, attention, and rigorous evaluation

• Sequential data preparation, padding, masking, and packed sequences
• Embedding layers for categorical and token-based inputs
• RNN, LSTM, and GRU model implementation patterns
• Attention mechanisms and transformer architecture fundamentals
• Classification metrics including precision, recall, F1 score, ROC-AUC, and PR-AUC
• Confusion-matrix analysis, calibration, and operating-threshold selection
• Hyperparameter search design and experiment comparison

Workshop: Train and evaluate a sequence classifier, producing an evaluation brief that recommends an operating threshold for a stated business cost scenario.

Day 5: Packaging, export, and production-oriented model decisions

• Inference mode, torch.no_grad, batching, and latency considerations
• Model serialization with state dictionaries and load-time validation
• ONNX export, dynamic axes, and export verification
• Input and output contracts for application integration
• Model versioning, experiment lineage, and reproducible release artefacts
• Data drift, performance monitoring, and retraining triggers
• Technical model review using architecture, metrics, risk, and deployment criteria

Workshop: Package the final PyTorch solution with checkpoints, TensorBoard evidence, an ONNX export, and a deployment recommendation for a technical review panel.

Tools & standards covered

PyTorch, torchvision, TensorBoard, ONNX

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions