Chinese Edition: A Statistical View of Deep Learning (II)/ 从统计学角度来看深度学习

Colleagues from the Capital of Statistics, an online statistics community in China, have been kind enough to translate my second post in this series, A statistical View of Deep Learning (II): Auto-encoders and Free Energy,  in the hope that they might be of interest to machine learning and statistics researchers in China (and to Chinese readers). Find ...

Chinese Edition: A statistical View of Deep Learning (I)/ 从统计学角度来看深度学习

Colleagues from the Capital of Statistics, an online statistics community in China, have been kind enough to translate my first post in this series, A statistical View of Deep Learning (I): Recursive GLMs,  in the hope that they might be of interest to machine learning and statistics researchers in China (and to Chinese readers). Find it here: ...

A Statistical View of Deep Learning (V): Generalisation and Regularisation 1

A Statistical View of Deep Learning (V): Generalisation and Regularisation
We now routinely build complex, highly-parameterised models in an effort to address the complexities of modern data sets. We design our models so that they have enough 'capacity', and this is now second nature to us using the layer-wise design principles of deep learning. But some problems continue to affect us, those that we encountered even in the low-data ...

A Statistical View of Deep Learning (IV): Recurrent Nets and Dynamical Systems 3

A Statistical View of Deep Learning (IV): Recurrent Nets and Dynamical Systems
Recurrent neural networks (RNNs) are now established as one of the key tools in the machine learning toolbox for handling large-scale sequence data. The ability to specify highly powerful models, advances in stochastic gradient descent, the availability of large volumes of data, and large-scale computing infrastructure, now allows us to apply RNNs in the most creative ...