TensorFlow Deep Learning Model Building Training Course

Course overview

Many organisations have data science teams that can explore data in notebooks but struggle to turn experiments into reliable deep learning models. TensorFlow is widely used for image, text, tabular and time-series workloads, yet effective use requires more than assembling layers. Practitioners need to shape input pipelines, select architectures, control training, diagnose failures, evaluate business-relevant metrics and package models for repeatable use. This course addresses the gap between introductory neural-network concepts and the practical work of building, improving and validating TensorFlow models.

Participants build deep learning workflows with TensorFlow and Keras, beginning with tensors, model APIs and data preparation. They create feed-forward, convolutional and sequence models; use tf.data for scalable input pipelines; manage overfitting with regularisation, dropout and augmentation; tune optimisers and learning rates; and assess models with confusion matrices, precision, recall, F1 score and error analysis. The course also covers TensorBoard experiment tracking, model saving, TensorFlow Serving-ready export and reproducible training practices.

Instruction combines concise technical briefings with guided coding labs in Python. Each day uses a realistic modelling problem, such as image classification, customer-text categorisation or demand forecasting, requiring participants to make and justify architecture and evaluation decisions. By the final day, each participant produces a documented TensorFlow project: a prepared dataset pipeline, trained and evaluated model, TensorBoard evidence, saved model artefact and a short deployment and monitoring plan that can be adapted to an active workplace use case.

The course is designed for developers, analysts and data professionals who need hands-on TensorFlow capability rather than a theory-only introduction. Managers benefit from staff who can create traceable model experiments, identify weak model performance early and communicate the operational requirements of putting a model into use.

Course objectives

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

• Build neural network models with TensorFlow Keras Sequential and Functional APIs
• Construct repeatable tf.data input pipelines for loading, batching, shuffling and prefetching datasets
• Train convolutional neural networks for supervised image classification
• Develop sequence models using embeddings and recurrent or transformer-based Keras layers
• Apply dropout, regularisation, data augmentation and early stopping to control overfitting
• Evaluate classification models with confusion matrices, precision, recall, F1 score and threshold analysis
• Diagnose training behaviour with TensorBoard metrics, learning curves and error-analysis workflows
• Export a versioned SavedModel and produce a deployment and monitoring checklist

Benefits of attending

Target competencies

Who should attend

• Data Scientists — who need to move from exploratory modelling to repeatable TensorFlow training workflows
• Machine Learning Engineers — who build, evaluate and package deep learning models for production use
• Python Developers — who need to implement image, text or prediction models using TensorFlow and Keras
• Data Analysts — who are extending statistical and dashboarding work into supervised machine learning
• AI Product Managers — who must assess model feasibility, evaluation evidence and deployment requirements
• Technical Leads — who need to set practical standards for model experiments, validation and handover

Requirements and prerequisites

Participants should be comfortable writing and running basic Python, including functions, lists, dictionaries, loops and importing packages. They should understand tabular datasets, train/test splits and the purpose of common supervised-learning tasks such as classification and regression. Familiarity with NumPy, pandas and a Python notebook or IDE is helpful; prior use of TensorFlow is not required. Attendees should bring a laptop able to run Python and browser-based notebooks, subject to organisational security policy. Calculus, advanced linear algebra, prior neural-network implementation and production cloud experience are not required, although complete Python beginners should first take a practical Python course.

Training methodology

The programme alternates instructor demonstrations with guided Python labs using TensorFlow and Keras. Participants inspect working code, modify architecture and training choices, then compare results through TensorBoard rather than treating model output as a black box. Case exercises use labelled image, text and structured-data scenarios to practise data preparation, validation and error analysis. Small-group reviews require participants to defend metric choices and deployment assumptions. The final workshop converts an individual model build into a concise handover pack containing evaluation findings, a saved model and an application plan.

Course outline

Day 1: TensorFlow foundations and data pipelines

• TensorFlow execution model, tensors and eager execution
• Keras Sequential versus Functional API model design
• Supervised learning workflow and train-validation-test splits
• Tensor shapes, dtypes and feature encoding
• tf.data Dataset creation from arrays and files
• Batching, shuffling, caching and prefetching strategies
• Baseline model creation and compile configuration

Workshop: Build a baseline Keras classifier with a tf.data pipeline and produce a reproducible train-validation-test workflow.

Day 2: Training control and model evaluation

• Loss functions for binary, multiclass and regression tasks
• Optimisers, gradient descent and learning-rate selection
• Model fit loops, callbacks and checkpoint management
• Early stopping and learning-rate scheduling
• Accuracy, precision, recall, F1 score and ROC-AUC
• Confusion matrices, threshold selection and class imbalance
• TensorBoard scalars, graphs and experiment comparison

Workshop: Train alternative classifier configurations, analyse their TensorBoard runs and recommend a threshold using a confusion-matrix trade-off.

Day 3: Computer vision with convolutional networks

• Convolution, filters, feature maps and pooling operations
• CNN construction with Conv2D, MaxPooling2D and GlobalAveragePooling2D
• Image loading and preprocessing with tf.data
• Keras data augmentation layers
• Transfer learning with pretrained Keras applications
• Fine-tuning strategy and frozen-layer control
• Visual error analysis for misclassified images

Workshop: Create and evaluate an image classifier using transfer learning, then produce an error gallery and improvement proposal.

Day 4: Text and sequence model development

• Text standardisation, tokenisation and TextVectorization
• Word embeddings and embedding-layer configuration
• Sequence padding, masking and variable-length inputs
• LSTM and GRU models for sequential data
• Attention mechanisms and Keras transformer encoder layers
• Time-series windowing and supervised forecasting datasets
• Architecture selection for text classification and forecasting

Workshop: Develop a sequence model for text classification or time-series prediction and document the architecture rationale and validation results.

Day 5: Reliable delivery and deployment preparation

• Overfitting diagnosis from training and validation curves
• Dropout, L1 and L2 regularisation techniques
• Hyperparameter search using KerasTuner concepts and search spaces
• Random seeds, dataset versioning and experiment reproducibility
• SavedModel export and TensorFlow model signatures
• TensorFlow Serving integration patterns and API inference considerations
• Model monitoring requirements for drift, performance and retraining

Workshop: Package a selected model as a SavedModel and produce a model handover pack containing evaluation evidence, serving assumptions and monitoring actions.

Tools & standards covered

TensorFlow, Keras, TensorBoard, tf.data

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