While the preprocessing pipeline we are focusing on in this post is mainly centered around Deep Learning, most of it will also be applicable to conventional machine learning models too.
This paper provides a succinct overview of this emerging theory of overparameterized ML (henceforth abbreviated as TOPML) that explains these recent findings through a statistical signal processing perspective. We emphasize the unique aspects that define the TOPML research area as a subfield of modern ML theory and outline interesting open questions that remain.
«In this work, we generalize the reaction-diffusion equation in statistical physics, Schrödinger equation in quantum mechanics, Helmholtz equation in paraxial optics into the neural partial differential equations (NPDE), which can be considered as the fundamental equations in the field of artificial intelligence research. We take finite difference method to discretize NPDE for finding numerical solution, and the basic building blocks of deep neural network architecture, including multi-layer perceptron, convolutional neural network and recurrent neural networks, are generated. The learning strategies, such as Adaptive moment estimation, L-BFGS, pseudoinverse learning algorithms and partial differential equation constrained optimization, are also presented. We believe it is of significance that presented clear physical image of interpretable deep neural networks, which makes it be possible for applying to analog computing device design, and pave the road to physical artificial intelligence».
EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions, it is a library for automated machine learning (AutoML) and model understanding, written in Python. Combined with Featuretools and Compose, EvalML can be used to create end-to-end supervised machine learning solutions. “EvalML provides a simple, unified interface for building machine learning models, using those models to generate insights and to make accurate predictions. EvalML provides access to multiple modeling libraries under the same API. EvalML supports a variety of supervised machine learning problem types including regression, binary classification and multiclass classification. Custom objective functions let users phrase their search for a model directly in terms of what they value. Above all we’ve aimed to make EvalML stable and performant, with ML performance testing on every release”
We present a panoptic driving perception network (YOLOP) to perform traffic object detection, drivable area segmentation and lane detection simultaneously.
This document gives a concise outline of some of the common mistakes that occur when using machine learning techniques, and what can be done to avoid them. It is intended primarily as a guide for research students, and focuses on issues that are of particular concern within academic research, such as the need to do rigorous comparisons and reach valid conclusions. It covers five stages of the machine learning process: what to do before model building, how to reliably build models, how to robustly evaluate models, how to compare models fairly, and how to report results
OU Analyse is a system powered by machine learning methods for early identification of students at risk of failing. All students with their risk of failure in their next assignment are updated weekly and made available to the course tutors and the Student Support Teams to consider appropriate support. The overall objective is to significantly improve the retention of OU students.
How are analytics and machine learning transforming education now, and what is the potential for the future? Hear examples of education leaders who are using analytics & ML to understand student performance and develop new forms of teaching and support. Carnegie Mellon has developed SARA, a socially aware robot tutor, and Unizin and Ivy Tech are using analytics and ML to understand student performance and pilot interventions. Learn how Google is bringing ML to education and organizations.