Course Description
Deep learning — neural networks with many layers — powers the most advanced industrial AI applications, from complex fault diagnosis and computer vision to sensor fusion and prognostics. This course develops a practical understanding of deep learning and its application to industrial problems.
Participants learn the fundamentals of neural networks, the main architectures (CNNs, RNNs/LSTMs), and how deep learning is applied to industrial data — image-based inspection, time-series prognostics, and multi-sensor fusion. The course balances conceptual understanding with practical application, aimed at those who want to go beyond classical machine learning into deep learning for industry.
What you will achieve
By the end of this training course, participants will be able to:
- Explain the fundamentals of neural networks and deep learning
- Understand how neural networks are trained
- Describe convolutional neural networks (CNNs) and their uses
- Describe recurrent networks (RNN/LSTM) for sequence data
- Apply deep learning to industrial fault diagnosis
- Apply deep learning to sensor and time-series data
- Understand data and computational requirements
- Recognise when deep learning is and is not appropriate
How the course is delivered
The course combines deep learning theory with industrial application examples across vision, time-series, and sensor fusion. Concepts are explained progressively and grounded in realistic industrial use cases.
The programme balances architectural understanding with practical guidance on when and how to apply deep learning in industry.
Designed for
This training course is ideal for:
- Industrial data scientists and ML engineers
- Engineers applying advanced AI to industry
- Reliability engineers exploring deep learning
- Technical staff working on complex diagnosis
- Those advancing beyond classical machine learning
Daily programme
- From machine learning to deep learning
- Neural network fundamentals
- How networks learn: training and backpropagation
- Frameworks: TensorFlow and PyTorch overview
- Convolutional neural networks (CNNs)
- CNNs for industrial image tasks
- Architectures and transfer learning
- Vision applications in industry
- Recurrent networks and LSTMs
- Deep learning for time-series
- Prognostics and RUL with deep learning
- Sensor sequence modelling
- Multi-sensor fusion
- Complex fault diagnosis
- Autoencoders and anomaly detection
- Physics-informed neural networks
- Data and computational requirements
- When to use (and not use) deep learning
- Deployment considerations
- Capstone exercise and review
Certification & accreditation
BII Certificate of Completion
BII Certificate of Completion Upon successful completion, participants receive a BII Development Institute Certificate of Completion with a unique reference code that is independently verifiable. Our certificates are recognised internationally and reflect successful completion of your chosen programme.
