As an example, mobile network operators are increasing their investment in big data analytics and machine learning technologies as they transform into digital application developers and cognitive service providers. With a long history of handling huge datasets, and with their path now led by the IT ecosystem, mobile operators will devote more than $50 billion to big data analytics and machine learning technologies through 2021, according to the latest global market study by ABI Research. Machine learning can deliver benefits across telecom provider operations with financially-oriented applications - including fraud mitigation and revenue assurance - which currently make the most compelling use cases. Predictive machine learning applications for network performance optimization and real-time management will introduce more automation and efficient resource utilization.
In contrast to k-nearest neighbors, a simple example of a parametric method would be logistic regression, a generalized linear model with a fixed number of model parameters: a weight coefficient for each feature variable in the dataset plus a bias (or intercept) unit. While the learning algorithm optimizes an objective function on the training set (with exception to lazy learners), hyperparameter optimization is yet another task on top of it; here, we typically want to optimize a performance metric such as classification accuracy or the area under a Receiver Operating Characteristic curve. Thinking back of our discussion about learning curves and pessimistic biases in Part II, we noted that a machine learning algorithm often benefits from more labeled data; the smaller the dataset, the higher the pessimistic bias and the variance -- the sensitivity of our model towards the way we partition the data. We start by splitting our dataset into three parts, a training set for model fitting, a validation set for model selection, and a test set for the final evaluation of the selected model.
A positive label means that an utterance was an actual response to a context, and a negative label means that the utterance wasn't – it was picked randomly from somewhere in the corpus. Each record in the test/validation set consists of a context, a ground truth utterance (the real response) and 9 incorrect utterances called distractors. Before starting with fancy Neural Network models let's build some simple baseline models to help us understand what kind of performance we can expect. The Deep Learning model we will build in this post is called a Dual Encoder LSTM network.
Last week, machine learning took a big leap forward when Google's AlphaGo, a machine algorithm, beat the world champion, Lee Sedol, in the game Go. When IBM Watson beat former champions Ken Jennings and Brad Rutter in the game show Jeopardy! Even though it doesn't rely on encoded rules, IBM Watson requires close monitoring by domain experts to provide data and evaluate its performance. AlphaGo was programmed to seek positive rewards in the form of scores and continually improve its system by playing millions of games against tweaked versions of itself.
The objective of the Kaggle competition was to predict the 2016 NCAA Basketball Tournament, called March Madness. Predicting performance through machine learning algorithms is a crucial aspect for HR Analytics. I learnt about a Bayesian skill rating system called TrueSkill used in large-scale commercial online gaming platforms, for example Xbox Live developed by Microsoft. Kaggle is a community of data scientists who come to compete in machine learning competitions.
In rail, and specifically when it comes to rolling stock maintenance, big data is synonymous with Condition Based Maintenance (CBM) and Predictive Maintenance (PM). In terms of operational intelligence, some of the relevant AI techniques to address problems like fleet monitoring and asset maintenance in the Rail industry include Knowledge Based Systems, Case Based Reasoning, Genetic Algorithms, Neural Networks and Fuzzy Logic etc. They eliminate the need for lengthy root cause identification and arrive at the required repair action more quickly, leading to faster repair, reduced maintenance cost and increased fleet availability and customer satisfaction. When it comes to asset intelligence, the continuous data streams produced from various sub-systems in trains help OEMs build digital twins that represent physical systems in real-time.