Advanced Deep Learning for Data Science Training Course

Course overview

Data science teams are increasingly asked to build models for images, text, time series and recommendation problems where tabular machine-learning approaches plateau. The challenge is not simply training a neural network: it is choosing an architecture that fits the data and operational constraint, designing reliable experiments, diagnosing unstable training, controlling compute cost and proving that model improvements will hold outside a notebook. This course addresses the gap between basic deep learning familiarity and the ability to develop production-ready deep learning solutions that can be reviewed by technical peers and deployed with confidence.

Participants work through an advanced deep learning workflow using PyTorch, TensorFlow/Keras and MLflow. They implement and compare convolutional networks, sequence models, attention mechanisms, transformer-based transfer learning and autoencoders; tune optimisation with learning-rate schedules, regularisation and mixed-precision training; and evaluate models with error analysis, calibration, robustness checks and task-appropriate metrics. The course also covers dataset versioning principles, experiment tracking, model packaging and monitoring signals required after deployment.

Teaching combines focused instructor demonstrations with guided coding labs based on realistic data science cases: visual defect classification, customer-support text classification and multivariate demand forecasting. Each participant builds an end-to-end deep learning project repository containing a documented problem statement, reproducible training pipeline, tracked experiments, evaluation report, model card and deployment recommendation. The final day includes a technical review in which participants defend architecture, metric and operational decisions against business and engineering criteria.

The course is designed for experienced data professionals who already build machine-learning models and now need stronger capability with neural-network design, experimentation and operationalisation. Managers gain staff able to make informed decisions about when deep learning is justified, rather than treating it as a default solution.

Course objectives

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

• Design convolutional, recurrent and transformer architectures matched to image, text and time-series data.
• Implement reproducible training pipelines in PyTorch and TensorFlow/Keras using modular datasets, models and training loops.
• Optimise neural-network training with learning-rate schedules, weight decay, dropout, batch normalisation and mixed precision.
• Apply transfer learning and fine-tuning strategies to adapt pretrained vision and language models to limited labelled data.
• Diagnose overfitting, data leakage, class imbalance and training instability using learning curves, gradient inspection and error slices.
• Evaluate deep learning models with calibration, confusion analysis, robustness tests and business-aligned performance metrics.
• Track experiments, parameters, artefacts and model versions in MLflow to support reproducible model selection.
• Produce a deployable deep learning project package with a model card, inference interface and monitoring plan.

Benefits of attending

Target competencies

Who should attend

• Senior Data Scientists — who need to select, build and defend advanced neural-network solutions for business problems.
• Machine Learning Engineers — who productionise training and inference pipelines for deep learning models.
• AI Engineers — who need practical architecture, fine-tuning and model-evaluation methods beyond API-level use.
• Data Science Team Leads — who review modelling choices, experiment evidence and delivery risk across AI projects.
• Applied Research Scientists — who need disciplined experimentation and reproducible benchmarking for model development.
• Analytics Engineers — who support feature, data-quality and deployment workflows for neural-network applications.

Requirements and prerequisites

Participants should be comfortable writing and debugging Python code, working in Jupyter notebooks, and using NumPy, pandas and scikit-learn for supervised learning. Prior experience training at least one basic neural network is required, including familiarity with train/validation/test splits, loss functions, gradient descent, classification metrics and overfitting. Participants should also be able to use a command line and install Python packages in a virtual environment. Familiarity with PyTorch or TensorFlow is helpful but not mandatory; both are introduced through practical labs. No prior GPU administration, cloud engineering, advanced calculus or research publication experience is required.

Training methodology

The instructor alternates short technical briefings with live code walkthroughs and supervised labs in notebooks. Participants implement models, inspect training traces, compare experiments in MLflow and diagnose deliberately flawed datasets and training runs. Small groups review case-study decisions, including architecture selection, metric choice and deployment constraints. Daily exercises extend a single project repository rather than isolated examples. On day five, participants complete an application-planning workshop and technical review, producing a prioritised use-case proposal and implementation plan for their own organisation.

Course outline

Day 1: Deep learning system design and reproducible baselines

• Deep learning suitability assessment versus gradient-boosted tree baselines
• Dataset splits for temporal, grouped and leakage-sensitive data
• PyTorch tensors, Dataset classes and DataLoader pipelines
• TensorFlow/Keras functional API model construction
• Loss functions for multiclass, multilabel and imbalanced classification
• MLflow experiment structure, run logging and artefact storage
• Baseline evaluation with learning curves and validation protocols

Workshop: Build a reproducible baseline classifier for a business dataset, log its parameters and metrics in MLflow, and produce a baseline comparison note.

Day 2: Computer vision architectures and transfer learning

• Convolution, pooling, receptive fields and feature hierarchy design
• Residual networks and EfficientNet architecture trade-offs
• Image augmentation policies using geometric and photometric transforms
• Pretrained backbone selection and feature-extraction workflows
• Layer freezing, discriminative learning rates and staged fine-tuning
• Class activation maps for visual error investigation
• Image classification metrics, threshold selection and calibration

Workshop: Fine-tune a pretrained vision model for visual defect classification and deliver an error gallery with class activation map findings.

Day 3: Sequence modelling, attention and transformers

• Sequence encoding, padding, masking and variable-length batching
• LSTM and GRU networks for sequential classification and forecasting
• Attention mechanisms and query-key-value computation
• Transformer encoder architecture and positional encodings
• Hugging Face pretrained transformer fine-tuning workflow
• Tokenisation choices, truncation risk and text-data leakage
• Multistep forecasting evaluation and rolling-origin backtesting

Workshop: Fine-tune a transformer for customer-support text routing and compare it with an LSTM baseline using confusion slices.

Day 4: Training optimisation, robustness and model diagnosis

• Optimisers: SGD with momentum, AdamW and adaptive update behaviour
• Learning-rate range tests, warm-up and cosine decay schedules
• Regularisation with weight decay, dropout, label smoothing and early stopping
• Batch normalisation, gradient clipping and exploding-gradient diagnosis
• Mixed-precision training and GPU memory-performance trade-offs
• Hyperparameter search with structured trials and pruning criteria
• Robustness, calibration and subgroup error analysis

Workshop: Repair an unstable training run by testing optimisation and regularisation changes, then submit an experiment report justifying the selected configuration.

Day 5: Production readiness and technical decision review

• Model packaging with saved weights, configuration files and inference interfaces
• Docker containerisation principles for reproducible inference environments
• Batch, real-time and asynchronous inference architecture choices
• Model cards, data documentation and intended-use statements
• Post-deployment monitoring for drift, latency, confidence and data quality
• Champion-challenger testing and model rollback criteria
• Deep learning use-case prioritisation by value, feasibility and risk

Workshop: Complete and defend an end-to-end project package containing a model card, inference design, monitoring plan and 90-day implementation roadmap.

Tools & standards covered

PyTorch, TensorFlow/Keras, MLflow, Docker

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