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Outliers in data can distort predictions and affect the accuracy, if you don’t detect and handle them appropriately especially in regression models.
We’ve learned how Artificial Neural Networks (ANN) can be used to recognize handwritten digits in a previous post. In the current post, we discuss additional techniques to improve the accuracy of neural networks.
Image recognition is important for many of the advanced technologies we use today. It is used in visual surveillance, guiding autonomous vehicles and even identifying ailments from X-ray images. Most modern smartphones also come with image recognition apps that convert handwriting into typed words. In this chapter we will look at how we can train an ANN algorithm to recognize images of handwritten digits. We will be using the images from the famous MNIST (Mixed National Institute of Standards and Technology) database.
Disease outcome prediction is an important part of clinical research and holds promise for precision medicine. However, most diseases are complex and training data may be highly heterogeneous. We propose a prediction method based on penalized linear regressions that uses fusion penalties on the regression parameters to share information across subgroups within the training samples.
DeepMind Lab is a first-person 3D game platformdesigned for research and development of general artificial intelligence and machine learning systems. DeepMind Lab can be used to study how autonomous artificial agents may learn complex tasks in large, partially observed, and visually diverse worlds. DeepMind Lab has a simple and flexible API enabling creative task-designs and novel AI-designs to be explored and quickly iterated upon. It is powered by a fast and widely recognised game engine, and tailored for effective use by the research community.
Use fast weights to aid in learning associative tasks and store temporary memories of recent past. In a traditional recurrent architecture we have our slow weights which are used to determine the next hidden state and hold long-term memory. We introduce the concept of fast weights, in conjunction with the slow weights, in order to account for short-term knowledge. These weights are quick to update and decay as they change from the introduction of new hidden states.