Learning Graphical Models

Sign languages aren't easy to learn and are even harder to teach. They use not just hand gestures but also mouthings, facial expressions and body posture to communicate meaning. This complexity means professional teaching programmes are still rare and often expensive. But this could all change soon, with a little help from artificial intelligence (AI). My colleagues and I are working on software for teaching yourself sign languages in an automated, intuitive way.

Sign languages aren't easy to learn and are even harder to teach. They use not just hand gestures but also mouthings, facial expressions and body posture to communicate meaning. This complexity means professional teaching programmes are still rare and often expensive. But this could all change soon, with a little help from artificial intelligence (AI). My colleagues and I are working on software for teaching yourself sign languages in an automated, intuitive way.

That means the pooling layer computes a feature vector of size 128 which is passed into dense layers of the feedforward network as we mentioned above. The overall structure of the DNN can be understood as a preprocessor defined in the first part that is being trained to map text sequences into feature vectors in such a way that the weights of the second part can be trained to obtain optimal classification results from the overall network. More details on the implementation and text preprocessing can be found in my GitHub repository for this project. I trained this network for 10 epochs with a batch size of 128 using an 80-20 training/hold-out set. A couple of notes on additional parameters: The vast majority of documents in this collection is of length 5000 or less. So for the maximum input sequence length for the DNN I chose 5000 words. There are roughly 100,000 unique words in this collection of documents. I arbitrarily limited the dictionary that the DNN can learn to 25% of that: 25,000 words. Finally, for the embedding dimension, I chose 300 simply because that is the default embedding dimension for both word2vec and GloVe.

The Naive Bayes Classifier is a well known machine learning classifier with applications in Natural Language Processing (NLP) and other areas. Despite its simplicity, it is able to achieve above average performance in different tasks like sentiment analysis. Today we will elaborate on the core principles of this model and then implement it in Python. In the end, we will see how well we do on a dataset of 2000 movie reviews. The math behind this model isn't particularly difficult to understand if you are familiar with some of the math notation.

Imagine you are a company selling a fast-moving consumer good in the market. Let's assume that the customer would follow the given journey to make the final purchase: These are the states at which the customer would be at any point in the purchase journey. Now, how to find out in which state the customers would be after 6 months? Markov Chain comes to the rescue!! Let's first understand what Markov Chain is. Let's delve a little deeper.

In my opinion RBMs have one of the easiest architectures of all neural networks. As it can be seen in Fig.1. The absence of an output layer is apparent. But as it can be seen later an output layer wont be needed since the predictions are made differently as in regular feedforward neural networks. Energy is a term that may not be associated with deep learning in the first place.

I've done previous work on haiku generation. This generator uses Markov chains trained on a corpus of non-haiku poetry, generates haiku one word at a time, and ensures the 5-7-5 structure by backspacing when all the possible next words would violate the 5–7–5 structure. This isn't unlike what I do when I'm writing a haiku. I try things, count out the syllables, find they don't work and go back. It feels more like brute force than something that actually understands what it means to write a haiku.

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x,

Asynchronous Gibbs sampling has been recently shown to be fast-mixing and an accurate method for estimating probabilities of events on a small number of variables of a graphical model satisfying Dobrushin's condition~\cite{DeSaOR16}. We investigate whether it can be used to accurately estimate expectations of functions of {\em all the variables} of the model. Under the same condition, we show that the synchronous (sequential) and asynchronous Gibbs samplers can be coupled so that the expected Hamming distance between their (multivariate) samples remains bounded by $O(\tau \log n),$ where $n$ is the number of variables in the graphical model, and $\tau$ is a measure of the asynchronicity. A similar bound holds for any constant power of the Hamming distance. Hence, the expectation of any function that is Lipschitz with respect to a power of the Hamming distance, can be estimated with a bias that grows logarithmically in $n$. Going beyond Lipschitz functions, we consider the bias arising from asynchronicity in estimating the expectation of polynomial functions of all variables in the model. Using recent concentration of measure results~\cite{DaskalakisDK17,GheissariLP17,GotzeSS18}, we show that the bias introduced by the asynchronicity is of smaller order than the standard deviation of the function value already present in the true model. We perform experiments on a multi-processor machine to empirically illustrate our theoretical findings.

The problem of estimating an unknown discrete distribution from its samples is a fundamental tenet of statistical learning. Over the past decade, it attracted significant research effort and has been solved for a variety of divergence measures. Surprisingly, an equally important problem, estimating an unknown Markov chain from its samples, is still far from understood. We consider two problems related to the min-max risk (expected loss) of estimating an unknown k-state Markov chain from its n sequential samples: predicting the conditional distribution of the next sample with respect to the KL-divergence, and estimating the transition matrix with respect to a natural loss induced by KL or a more general f-divergence measure. For the first measure, we determine the min-max prediction risk to within a linear factor in the alphabet size, showing it is \Omega(k\log\log n/n) and O(k^2\log\log n/n). For the second, if the transition probabilities can be arbitrarily small, then only trivial uniform risk upper bounds can be derived. We therefore consider transition probabilities that are bounded away from zero, and resolve the problem for essentially all sufficiently smooth f-divergences, including KL-, L_2-, Chi-squared, Hellinger, and Alpha-divergences.