We can help you achieve quantitatively better results with deep learning, even when there are fixed data/inputs, pre-selected algorithms, preferred/adopted software and/or hardware and defined time or resource constraints. Incremental improvements come from a pragmatic and iterative tuning approach, rather than just accepting what comes out of the box first. Conversely, endless iterations for insignificant performance gains (or even degradations) are to be avoided.
Doing deep learning – better, can take many forms depending on your requirements. Some examples of the kinds of things that we can do to help include:
- Measure and evaluate the contributions from training data to deep learning model coverage and effectiveness
- Evaluate the different types or variants of deep learning algorithms as to their potential gains in terms of results
- Fine-tune existing algorithm, software, services and hardware components and pipelines to deliver faster, better results
- Optimise deep learning training and inference tasks to take maximum advantage of hardware environments on which they are deployed
- Develop and use a balanced scorecard for deep learning for your specific problems to understand the tradeoffs that can be made in pursuit of better results