Introduction to Neural Network Intelligence

摘要

There was a great burst of deep learning industry in the past few years. Deep learning approaches have achieved outstanding results in computer vision, natural language processing, robotics, time series forecasting, and optimal control theory. However, there is no "silver bullet model" to solve all kinds of problems. Each problem and dataset needs a specific model architecture to achieve suitable performance. Machine learning models, especially deep learning models, have a lot of tunable parameters that can drastically affect the model performance. Those are model design, training method, model configuration hyperparameters, etc. The model optimization process is performed for each application and even each dataset. Data scientists and machine learning experts often spend a lot of time performing manual model optimization. This activity can be frustrating because it takes too much time and is usually based on an expert's experience and quasi-random search. Neural network intelligence (NNI) toolkit provides the latest state-of-the-art techniques to solve the most challenging automated deep learning problems. We’ll start exploring the basic NNI features in this chapter.