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Markov Models

Recommender Systems and Deep Learning in Python - Udemy Free Coupons Discount - Couse Sites


Free Coupon Discount - Recommender Systems and Deep Learning in Python The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques | Created by Lazy Programmer Inc. Students also bought Artificial Intelligence: Reinforcement Learning in Python Data Science: Natural Language Processing (NLP) in Python Unsupervised Machine Learning Hidden Markov Models in Python Natural Language Processing with Deep Learning in Python Cluster Analysis and Unsupervised Machine Learning in Python Preview this Udemy Course GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Explore the World of Data-Tech with DataHour - Analytics Vidhya


DataHour sessions are an excellent opportunity for aspiring individuals looking to launch a career in the data-tech industry, including students and freshers. Current professionals seeking to transition into the data-tech domain or data science professionals seeking to enhance their career growth and development can also benefit from these sessions. In this blog post, we will introduce you to some of the upcoming DataHour sessions, including contrastive learning for image classification, feature engineering, POS tagging, document segmentation using Layout Parser, and many more. Each session is designed to provide you with insights into various data tech topics, techniques, and methods. Attendees will learn from experts in the field, gain practical knowledge, and get to ask questions to clear their doubts.

How Deep Q Network works part1(Machine Learning)


Abstract: Reinforcement Learning (RL) is currently one of the most commonly used techniques for traffic signal control (TSC), which can adaptively adjusted traffic signal phase and duration according to real-time traffic data. However, a fully centralized RL approach is beset with difficulties in a multi-network scenario because of exponential growth in state-action space with increasing intersections. Multi-agent reinforcement learning (MARL) can overcome the high-dimension problem by employing the global control of each local RL agent, but it also brings new challenges, such as the failure of convergence caused by the non-stationary Markov Decision Process (MDP). In this paper, we introduce an off-policy nash deep Q-Network (OPNDQN) algorithm, which mitigates the weakness of both fully centralized and MARL approaches. The OPNDQN algorithm solves the problem that traditional algorithms cannot be used in large state-action space traffic models by utilizing a fictitious game approach at each iteration to find the nash equilibrium among neighboring intersections, from which no intersection has incentive to unilaterally deviate.

Introduction to the Special Track on Artificial Intelligence and COVID-19

Journal of Artificial Intelligence Research

The human race is facing one of the most meaningful public health emergencies in the modern era caused by the COVID-19 pandemic. This pandemic introduced various challenges, from lock-downs with significant economic costs to fundamentally altering the way of life for many people around the world. The battle to understand and control the virus is still at its early stages yet meaningful insights have already been made. The uncertainty of why some patients are infected and experience severe symptoms, while others are infected but asymptomatic, and others are not infected at all, makes managing this pandemic very challenging. Furthermore, the development of treatments and vaccines relies on knowledge generated from an ever evolving and expanding information space. Given the availability of digital data in the modern era, artificial intelligence (AI) is a meaningful tool for addressing the various challenges introduced by this unexpected pandemic. Some of the challenges include: outbreak prediction, risk modeling including infection and symptom development, testing strategy optimization, drug development, treatment repurposing, vaccine development, and others.

Machine Learning: Concepts and Applications


This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning. A key feature of this course is that you not only learn how to apply these techniques, you also learn the conceptual basis underlying them so that you understand how they work, why you are doing what you are doing, and what your results mean. The course also features real-world datasets, drawn primarily from the realm of public policy.

Sri Lankans lament Independence Day pomp amid economic crisis

Al Jazeera

Sri Lankans have protested in the capital Colombo against the government's decision to organise a pompous military parade to mark 75 years of independence from British colonial rule at a time when the country is experiencing a dire economic crisis. The celebration on Saturday was condemned by many Buddhists and Christian clergies who announced a boycott of the event in Colombo, while activists and others expressed anger at what they regard as a waste of money. Despite the criticism, armed troops paraded along the main esplanade in the city, showcasing military equipment, as navy ships sailed in the sea and helicopters and aircraft flew over the city. Sri Lanka gained independence in 1948. "Given inflation, given increasing costs, given the way the local currency devalued … ordinary Sri Lankans are struggling to make ends meet. And at a time like this when you have a celebration that people have heard is costing so many thousands of dollars, they are not happy," said Al Jazeera's Minelle Fernandez.

An Introduction to Markov Chains - KDnuggets


Markov chains are a type of mathematical system that undergoes transitions from one state to another according to certain probabilistic rules. They were first introduced by Andrey Markov in 1906 as a way to model the behavior of random processes, and have since been applied to a wide range of fields, including physics, biology, economics, statistics, machine learning, and computer science. Markov chains are named after Andrey Markov, a Russian mathematician who is credited with developing the theory of these systems in the early 20th century. Markov was interested in understanding the behavior of random processes, and he developed the theory of Markov chains as a way to model such processes. Markov chains are often used to model systems that exhibit memoryless behavior, where the system's future behavior is not influenced by its past behavior.



Machine Learning (ML) is the branch of Artificial Intelligence in which we use algorithms to learn from data provided to make predictions on unseen data. Recently, the demand for Machine Learning engineers has rapidly grown across healthcare, Finance, e-commerce, etc. According to Glassdoor, the median ML Engineer Salary is $131,290 per annum. In 2021, the global ML market was valued at $15.44 billion. It is expected to grow at a significant compound annual growth rate (CAGR) above 38% until 2029.

Robust Control for Dynamical Systems with Non-Gaussian Noise via Formal Abstractions

Journal of Artificial Intelligence Research

Controllers for dynamical systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modeled as process noise in a dynamical system, and common assumptions are that the underlying distributions are known and/or Gaussian. In practice, however, these assumptions may be unrealistic and can lead to poor approximations of the true noise distribution. We present a novel controller synthesis method that does not rely on any explicit representation of the noise distributions. In particular, we address the problem of computing a controller that provides probabilistic guarantees on safely reaching a target, while also avoiding unsafe regions of the state space. First, we abstract the continuous control system into a finite-state model that captures noise by probabilistic transitions between discrete states. As a key contribution, we adapt tools from the scenario approach to compute probably approximately correct (PAC) bounds on these transition probabilities, based on a finite number of samples of the noise. We capture these bounds in the transition probability intervals of a so-called interval Markov decision process (iMDP). This iMDP is, with a user-specified confidence probability, robust against uncertainty in the transition probabilities, and the tightness of the probability intervals can be controlled through the number of samples. We use state-of-the-art verification techniques to provide guarantees on the iMDP and compute a controller for which these guarantees carry over to the original control system. In addition, we develop a tailored computational scheme that reduces the complexity of the synthesis of these guarantees on the iMDP. Benchmarks on realistic control systems show the practical applicability of our method, even when the iMDP has hundreds of millions of transitions.