Modern approaches to text to speech require the entire input character sequence to be processed before any audio is synthesised. This latency limits the suitability of such models for time-sensitive tasks like simultaneous interpretation. Interleaving the action of reading a character with that of synthesising audio reduces this latency. However, the order of this sequence of interleaved actions varies across sentences, which raises the question of how the actions should be chosen. We propose a reinforcement learning based framework to train an agent to make this decision. We compare our performance against that of deterministic, rule-based systems. Our results demonstrate that our agent successfully balances the trade-off between the latency of audio generation and the quality of synthesised audio. More broadly, we show that neural sequence-to-sequence models can be adapted to run in an incremental manner.

Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. Association Rules find all sets of items (itemsets) that have support greater than the minimum support and then using the large itemsets to generate the desired rules that have confidence greater than the minimum confidence. The lift of a rule is the ratio of the observed support to that expected if X and Y were independent. A typical and widely used example of association rules application is market basket analysis.

Machine learning and Artificial intelligence are the new buzz words that are being thrown around more than any other trending technology today. It is starting to reshape how we think about building products. It's time we understood what it is and why it matters. Machine Learning: (ML) is an area of computational science that enables machines (computers) to undertake tasks without being explicitly programmed. The idea behind machine learning is that by training computers to analyze and interpret existing data from prior human interactions, machines are able to find patterns and structures in data.

Calculation of Return: Return is calculated based on dynamic position size, where position size of a new trade is determined in the following way: Stop loss value is always 1-3% of account balance. For example, if your account balance is at $1000, you will risk losing $10-$30 for the next trade. New signals will automatically cut existing positions in opposite direction. Strategies: There are two categories of strategies. One uses rule-based system which covers both trend and range based strategies.

We introduce a novel rule-based approach for handling regression problems. The new methodology carries elements from two frameworks: (i) it provides information about the uncertainty of the parameters of interest using Bayesian inference, and (ii) it allows the incorporation of expert knowledge through rule-based systems. The blending of those two different frameworks can be particularly beneficial for various domains (e.g. engineering), where, even though the significance of uncertainty quantification motivates a Bayesian approach, there is no simple way to incorporate researcher intuition into the model. We validate our models by applying them to synthetic applications: a simple linear regression problem and two more complex structures based on partial differential equations. Finally, we review the advantages of our methodology, which include the simplicity of the implementation, the uncertainty reduction due to the added information and, in some occasions, the derivation of better point predictions, and we address limitations, mainly from the computational complexity perspective, such as the difficulty in choosing an appropriate algorithm and the added computational burden.

TL;DR scikit-learn does not allow you to add hard-coded rules to your machine learning model, but for many use cases, you should! This article explores how you can leverage domain knowledge and object-oriented programming (OOP) to build hybrid rule-based machine learning models on top of scikit-learn. Supervised machine learning models are great for making predictions under uncertainty; they pick up patterns in past data and accurately extrapolate them into the future. Machine learning has pushed the frontier in fields where determining the most likely outcome, whether a class or specific value, has historically been challenging, prone to error, or too time-consuming or expensive at scale. Still, there exist many domains in which some of all possible outcomes are not ambiguous but certain by definition.

As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably, the most explainable machine learning models use decision rules. This paper focuses on decision sets, a type of model with unordered rules, which explains each prediction with a single rule. In order to be easy for humans to understand, these rules must be concise. Earlier work on generating optimal decision sets first minimizes the number of rules, and then minimizes the number of literals, but the resulting rules can often be very large. Here we consider a better measure, namely the total size of the decision set in terms of literals. So we are not driven to a small set of rules which require a large number of literals. We provide the first approach to determine minimum-size decision sets that achieve minimum empirical risk and then investigate sparse alternatives where we trade accuracy for size. By finding optimal solutions we show we can build decision set classifiers that are almost as accurate as the best heuristic methods, but far more concise, and hence more explainable.

Simply put, for the last 200 years, advisers have worked on the principle of information asymmetry, where they have better information than their clients. Today, we are at the point where machine intelligence is gaining information asymmetry over advisers, and that's only going to get more acute and asymmetrical as time goes on. The only possible hope for human advisers is that they co-opt machine intelligence into their process.

As a data scientist, have you ever been frustrated that your stakeholders don't see the value that you bring to the table? You may ask yourself, "How far should I go in explaining the work I do or what my models are doing?" If that sounds like you, then pay close attention to this post and the next, as they are all about improving collaboration between data scientists and other stakeholders. This is a two-part post: This article covers underlying assumptions and gaps in understanding that cause friction between data scientists and stakeholders; my other article, offers concrete steps for better collaboration. Machine Learning (ML) models are inherently complex and hard to explain.

MACHINE learning is a popular buzzword today, and has been heralded as one of the greatest innovations conceived by man. A branch of artificial intelligence (AI), machine learning is increasingly embedded in daily life, such as automatic email reply predictions, virtual assistants, and chatbots. The technology is also expected to revolutionize the world of finance. While it is slower than other industries in embracing the technology, the impact of ML is already visibly significant. Most recently, HSBC said that the bank was using the technology to combat financial crime.