Production Deployment

You’ve carefully formulated and specified a target problem, collected and processed training data, picked a familiar framework (or learned a new one) and through a process of research, intuition, and luck chosen an initial network architecture to represent your problem. You’ve methodically run experiments and tuned hyperparameters, getting side-tracked along the way to write tools…Read More

Machine Learning Labs

Machine learning in practice can be an arduous task. Managing multiple iterations of code, processing input data, feature engineering, training models, visualizing and tabulating results, performing analysis, and using experience to draw conclusions and adapt the system in a way which might yield improved results. In many cases you feel like you have more promising…Read More

Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks

This is a really great example of not only how data-driven tools of the future might help enhance the creative process but also of how a machine learning problem can be broken down into smaller sub-models in order to aid interactivity and provide greater control. Authoring virtual terrains presents a challenge and there is a…Read More