# Machine Learning Trick of the Day (6): Tricks with Sticks

· Read in  · Our first encounters with probability are often through a collection of simple games. The games of probability are played with coins, dice, cards, balls and urns, and sticks and strings. Using these games, we built an intuition that … Continue reading Machine Learning Trick of the Day (6): Tricks with Sticks

# Bayesian Reasoning and Deep Learning

I gave a talk entitled 'Bayesian Reasoning and Deep Learning' recently. Here is the abstract and the slides for interest. Slides Bayesian Reasoning and Deep Learning Abstract Deep learning and Bayesian machine learning are currently two of the most active … Continue reading Bayesian Reasoning and Deep Learning

# A Statistical View of Deep Learning (VI): What is Deep?

Throughout this series, we have discussed deep networks by examining prototypical instances of these models, e.g., deep feed-forward networks, deep auto-encoders, deep generative models, but have not yet interrogated the key word we have been using. We have not posed the question what … Continue reading A Statistical View of Deep Learning (VI): What is Deep?

# 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: 从统计学角度来看深度学习（1）：递归广义线性模型   Continue reading Chinese Edition: A statistical View of Deep Learning (I)/ 从统计学角度来看深度学习

# A Statistical View of Deep Learning (I): Recursive GLMs

Deep learning and the use of deep neural networks [cite key="bishop1995neural"] are now established as a key tool for practical machine learning. Neural networks have an equivalence with many existing statistical and machine learning approaches and I would like to explore one of these views in this post. In particular, I'll look at the view of deep neural networks as recursive generalised linear models (RGLMs). Generalised linear models form one of the cornerstones of probabilistic modelling and are used in almost every field of experimental science, so this connection is an extremely useful one to have in mind. I'll focus here on what are called feedforward neural networks and leave a discussion of the statistical connections to recurrent networks to another post.