《Understanding Machine Learning: From Theory to Algorithms》 介绍:耶路撒冷希伯来大学教授Shai Shalev-Shwartz和滑铁卢大学教授Shai Ben-David的新书Understanding Machine Learning: From Theory to Algorithms,此书写的比较偏理论,适合对机器学习理论有兴
Typically, a professor teaching control theory draws an FCA on a whiteboard. To show and analyze the dynamic behavior of the system—how it behaves with various inputs—the professor draws a plot next to the block diagram. But that takes time, and the plot is redrawn for each change...
This tutorial is an introduction to deep learning. We will motivate the excitement in this field with a survey of recent state-of-the-art results, and we will outline some of the theory behind representational learning. We will then discuss a small implementation of a convolutional network ...
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires m
From circuit theory. Certain functions can be represented much more efficiently using a multi-stage architecture. A classic example is the parity function. However parity function is not very relevant to functions that we approximate in practice. Besides, gradient descent is horrible at learning funct...
MMD is a kernel-based learning method. It measures the distance between two distributions in the regenerated Hilbert space. Read notes for lecture 4 to learn more about KLD. About MMD and JSD, please read here. 4.2.2 Other Divergences Maximum Mean Discrepancy (MMD) Jensen-Shannon Divergence ...
《Deep Learning for NLP - Lecture October 2015》 介绍:Nils Reimers面向NLP的深度学习(Theano/Lasagne)系列教程. 《Connection between probability theory and real analysis》 介绍:主讲人是陶哲轩,资料Probability: Theory and Examples,笔记. 《Data Science Learning Resources》 介绍:数据科学(学习)资源列表....
In recent years, there has been growing interest in using deep learning methods to improve the accuracy of stock price prediction, which has always been challenging due to the unpredictable nature of the market. This paper introduces two new hybrid deep learning-based models, named “En-Tweet-D...
Advances in the artificial neural network have made machine learning techniques increasingly more important in image analysis tasks. Recently, convolutional neural networks (CNN) have been applied to the problem of cell segmentation from microscopy images. However, previous methods used a supervised traini...
Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India[J]. Geoscience Frontiers, 2021, 12(5): 101203. DOI: 10.1016/j.gsf.2021.101203 Citation: Kanu Mandal, Sunil Saha, Sujit Mandal. Applying deep ...