Machine Learning with TensorFlow

Course overview

This training includes Machine Learning with TensorFlow: tensors and computation graphs, building and training models in TensorFlow, as well as exporting them for use in applications. Your competencies Representing data as tensors and processing it in TensorFlow Building and training models in TensorFlow layer by layer Monitoring training progress and fine-tuning models as needed Saving trained models and exporting them for applications Basic concepts of TensorFlow Tensors, operations, and computation graphs Data pipelines for large datasets Data types and dimensions of tensors Training models with TensorFlow Model building from layers, loss function, and optimizer Training runs, validation, and visualization of metrics Recognizing and limiting overfitting Application in professional everyday life Adapting existing TensorFlow models to new data Estimating computation time and resources for training runs Documenting model results for development teams Professional fields of application Development teams operating models with TensorFlow Data teams in industry and technology companies Data preparation and classical learning methods via programming code are covered by the offering "Machine Learning with Python", while the comparison of two frameworks for deep networks is addressed in the offering "Deep Learning with TensorFlow and PyTorch".

Upcoming dates

• 2026-10-29T23:00:00.000Z · 2026-12-24T23:00:00.000Z · Aachen · Combined Learning
• 2026-10-29T23:00:00.000Z · 2026-12-24T23:00:00.000Z · Zwickau · Combined Learning
• 2026-10-29T23:00:00.000Z · 2026-12-24T23:00:00.000Z · Zittau · Combined Learning
• 2026-10-29T23:00:00.000Z · 2026-12-24T23:00:00.000Z · Würzburg · Combined Learning
• 2026-10-29T23:00:00.000Z · 2026-12-24T23:00:00.000Z · Wuppertal · Combined Learning