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Speech Recognition Transformation

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Voice technology has reached maturity. The quality of speech recognition surpassed 95 percent accuracy in 2020. That is the same quality as normal communication between human beings. And the influence is now being felt. The modern Microsoft Windows update vigorously pushes its voice feature -- a mechanism that allows the user to dictate messages at the speed of normal speech, which is four times faster than typing. There are more than 2,600 voice apps (called "skills") available for download on Apple & Google app stores.


Why generalization in RL is difficult: epistemic POMDPs and implicit partial observability

AIHub

Many experimental works have observed that generalization in deep RL appears to be difficult: although RL agents can learn to perform very complex tasks, they don't seem to generalize over diverse task distributions as well as the excellent generalization of supervised deep nets might lead us to expect. In this blog post, we will aim to explain why generalization in RL is fundamentally harder, and indeed more difficult even in theory. We will show that attempting to generalize in RL induces implicit partial observability, even when the RL problem we are trying to solve is a standard fully-observed MDP. This induced partial observability can significantly complicate the types of policies needed to generalize well, potentially requiring counterintuitive strategies like information-gathering actions, recurrent non-Markovian behavior, or randomized strategies. Ordinarily, this is not necessary in fully observed MDPs but surprisingly becomes necessary when we consider generalization from a finite training set in a fully observed MDP.



Markov models and Markov chains explained in real life: probabilistic workout routine

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Andrei Markov didn't agree with Pavel Nekrasov, when he said independence between variables was necessary for the Weak Law of Large Numbers to be applied. When you collect independent samples, as the number of samples gets bigger, the mean of those samples converges to the true mean of the population. But Markov believed independence was not a necessary condition for the mean to converge. So he set out to define how the average of the outcomes from a process involving dependent random variables could converge over time. Thanks to this intellectual disagreement, Markov created a way to describe how random, also called stochastic, systems or processes evolve over time.



Analyzing Patient Trajectories With Artificial Intelligence

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For example, electronic health records store the history of a patient's diagnoses, medications, laboratory values, and treatment plans [1-3]. Wearables collect granular sensor measurements of various neurophysiological body functions over time [4-6]. Intensive care units (ICUs) monitor disease progression via continuous physiological measurements (eg, electrocardiograms) [7-10]. As a result, patient data in digital medicine are regularly of longitudinal form (ie, consisting of health events from multiple time points) and thus form patient trajectories. Analyzing patient trajectories provides opportunities for more effective care in digital medicine [2,7,11]. Patient trajectories encode rich information on the history of health states that are also predictive of the future course of a disease (eg, individualized differences in disease progression or responsiveness to medications) [9,10,12]. As such, it is possible to construct patient trajectories that capture the entire disease course and characterize the many possible disease progression patterns, such as recurrent, stable, or rapidly deteriorating disease states (Figure 1). Hence, modeling the patient trajectories allows one to build robust models of diseases that capture disease dynamics seen in patient trajectories. Here, we replace disease models with data from only a single or a small number of time points by disease models that account for the longitudinal nature of patient trajectories, thus offering vast potential for digital medicine. Several studies have previously introduced artificial intelligence (AI) in medicine for practitioners [13,14].


Unsupervised Machine Learning Hidden Markov Models in Python

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Created by Lazy Programmer Inc. English [Auto-generated], Portuguese [Auto-generated] Preview this Udemy Course - GET COUPON CODE Description The Hidden Markov Model or HMM is all about learning sequences. A lot of the data that would be very useful for us to model is in sequences. Stock prices are sequences of prices. Language is a sequence of words. Credit scoring involves sequences of borrowing and repaying money, and we can use those sequences to predict whether or not you're going to default.


Discrete Markov chains

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The discrete case, generally known as a Markov chain, is discussed on this page. The Markov approach can be applied to the random behaviour of systems that vary discretely or continuously with respect to time and space. This discrete or continuous random variable is known as a stochastic process. Not all stochastic processes can be modelled using the basic Markov approach although there are techniques available for modelling some additional stochastic processes using extensions of this basic method. In order for the basic Markov approach to be applicable, the behaviour of the system must be characterized by a lack of memory, that is, the future states of a system are independent of all past states' except the immediately preceding one.


PRISM: A Hierarchical Intrusion Detection Architecture for Large-Scale Cyber Networks

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The increase in scale of cyber networks and the rise in sophistication of cyber-attacks have introduced several challenges in intrusion detection. The primary challenge is the requirement to detect complex multi-stage attacks in realtime by processing the immense amount of traffic produced by present-day networks. In this paper we present PRISM, a hierarchical intrusion detection architecture that uses a novel attacker behavior model-based sampling technique to minimize the realtime traffic processing overhead. PRISM has a unique multi-layered architecture that monitors network traffic distributedly to provide efficiency in processing and modularity in design. PRISM employs a Hidden Markov Model-based prediction mechanism to identify multi-stage attacks and ascertain the attack progression for a proactive response. Furthermore, PRISM introduces a stream management procedure that rectifies the issue of alert reordering when collected from distributed alert reporting systems. To evaluate the performance of PRISM, multiple metrics has been proposed, and various experiments have been conducted on a multi-stage attack dataset. The results exhibit up to 7.5x improvement in processing overhead as compared to a standard centralized IDS without the loss of prediction accuracy while demonstrating the ability to predict different attack stages promptly.


The Complete Neural Networks Bootcamp: Theory, Applications

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Including NLP and Transformers Students also bought Recommender Systems and Deep Learning in Python Machine Learning A-Z: Become Kaggle Master Unsupervised Deep Learning in Python Deep Learning: Recurrent Neural Networks in Python Unsupervised Machine Learning Hidden Markov Models in Python Deep Learning: Convolutional Neural Networks in Python Preview this Udemy Course GET COUPON CODE Description This course is a comprehensive guide to Deep Learning and Neural Networks. The theories are explained in depth and in a friendly manner. After that, we'll have the hands-on session, where we will be learning how to code Neural Networks in PyTorch, a very advanced and powerful deep learning framework! We will walk through an example and do the calculations step-by-step. We will also discuss the activation functions used in Neural Networks, with their advantages and disadvantages!