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On the Prediction of Evaporation in Arid Climate Using Machine Learning Model

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Evaporation calculations are important for the proper management of hydrological resources, such as reservoirs, lakes, and rivers. Data-driven approaches, such as adaptive neuro fuzzy inference, are getting popular in many hydrological fields. This paper investigates the effective implementation of artificial intelligence on the prediction of evaporation for agricultural area. In particular, it presents the adaptive neuro fuzzy inference system (ANFIS) and hybridization of ANFIS with three optimizers, which include the genetic algorithm (GA), firefly algorithm (FFA), and particle swarm optimizer (PSO). Six different measured weather variables are taken for the proposed modelling approach, including the maximum, minimum, and average air temperature, sunshine hours, wind speed, and relative humidity of a given location. Models are separately calibrated with a total of 86 data points over an eight-year period, from 2010 to 2017, at the specified station, located in Arizona, United States of America. Farming lands and humid climates are the reason for choosing this location. Ten statistical indices are calculated to find the best fit model. Comparisons shows that ANFIS and ANFIS–PSO are slightly better than ANFIS–FFA and ANFIS–GA. Though the hybrid ANFIS–PSO (R2= 0.99, VAF = 98.85, RMSE = 9.73, SI = 0.05) is very close to the ANFIS (R2 = 0.99, VAF = 99.04, RMSE = 8.92, SI = 0.05) model, preference can be given to ANFIS, due to its simplicity and easy operation.


Machine Learning : The Subset of Artificial Intelligence

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You may also have heard machine learning and AI used interchangeably. AI includes machine learning, but machine learning doesn't fully define AI. Machine learning and AI both have strong engineering components. You find AI and machine learning used in a great many applications today. Artificial Intelligence (AI) is a huge topic today, and it's getting bigger all the time thanks to the success of technologies such as Siri.


Diffusion Models Made Easy

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In the recent past, I have talked about GANs and VAEs as two important Generative Models that have found a lot of success and recognition. GANs work great for multiple applications however, they are difficult to train, and their output lack diversity due to several challenges such as mode collapse and vanishing gradients to name a few. Although VAEs have the most solid theoretical foundation however, the modelling of a good loss function is a challenge in VAEs which makes their output to be suboptimal. There is another set of techniques which originate from probabilistic likelihood estimation methods and take inspiration from physical phenomenon; it is called, Diffusion Models. The central idea behind Diffusion Models comes from the thermodynamics of gas molecules whereby the molecules diffuse from high density to low density areas.


Kaggle - Get The Best Data Science, Machine Learning Profile

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Welcome to " Kaggle - Get Best Profile in Data Science & Machine Learning " course. Kaggle is Machine Learning & Data Science community. Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges. Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer, Oak Academy has a course to help you apply machine learning to your work. It's hard to imagine our lives without machine learning.


Mathematical Foundations of Machine Learning

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Understand the fundamentals of linear algebra and calculus, critical mathematical subjects underlying all of machine learning and data science Manipulate tensors using all three of the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch How to apply all of the essential vector and matrix operations for machine learning and data science Reduce the dimensionality of complex data to the most informative elements with eigenvectors, SVD, and PCA Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion) Appreciate how calculus works, from first principles, via interactive code demos in Python Intimately understand advanced differentiation rules like the chain rule Compute the partial derivatives of machine-learning cost functions by hand as well as with TensorFlow and PyTorch Grasp exactly what gradients are and appreciate why they are essential for enabling ML via gradient descent Use integral calculus to determine the area under any given curve Be able to more intimately grasp the details of cutting-edge machine learning papers Develop an understanding of what's going on beneath the hood of machine learning algorithms, including those used for deep learning Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion) Develop an understanding of what's going on beneath the hood of machine learning algorithms, including those used for deep learning All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples. Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information -- such as understanding charts and rearranging simple equations -- then you should be well-prepared to follow along with all of the mathematics. All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples. Familiarity with secondary school-level mathematics will make the class easier to follow along with.


Linear Algebra for Machine Learning

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Good data scientists are familiar with machine learning libraries and algorithms. It is akin to being an amazing pilot of an airplane, with skills that go beyond flying and borders an airplane mechanic. But to be a great data scientist, those skills will have to surpass the mechanics and thus require a greater understanding. The great data scientist knows how those libraries and algorithms work under the hood. The great data scientist understands the mathematics behind the science. With the speed of technology, there may come a day when the algorithm itself replaces the data scientist.


International lab dedicated to artificial intelligence kicks-off in Montreal

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Montreal-based centre unites strengths of McGill University, ÉTS, Mila, CNRS, Université Paris-Saclay, and CentraleSupélec A consortium of research organizations has gathered together to form a new International Research Laboratory (IRL) focused on artificial intelligence (AI) in Montreal. The new centre gathers together McGill University, École de technologie supérieure (ÉTS), Mila – Quebec AI Institute, France’s Centre Nationale de la Recherche Scientifique (CNRS), Université Paris-Saclay, and the École CentraleSupélec. The move confirms Montreal’s status as a leader in AI. While great strides have been made in AI recently, there is still a pressing need for new theoretical knowledge to better understand not only the capacities of this new technology, but how it achieves its results. The ILLS will focus on five main themes of research: fundamental aspects of artificial intelligence, sequential (real-time) machine learning, robust autonomous systems, natural language and speech processing, and applications to computer vision, signals, and information processing. In addition, the new centre will emphasize interdisciplinary collaborations with an aim to develop new methodologies and integrate these techniques into learning systems. “This new laboratory confirms Montreal’s global leadership in AI,” said Benoit Boulet, Associate Vice-Principal, Research & Innovation at McGill University. “This is a major hub with a talent pool that continues to deepen, and McGill researchers and students are embedded at every level of this activity. This new initiative will offer opportunities for our researchers to make even more breakthrough discoveries.” “The expertise of ÉTS in AI includes several laboratories and research chairs in artificial intelligence. This collaboration between France and Quebec makes it possible to innovate and deepen research in AI, a cross-cutting discipline from which we can benefit in many fields, including health, the built environment, robotics, and the Internet of Things. It is therefore with pride that ÉTS welcomes the new ILLS centre within its establishment,” said Christian Casanova, Director of Research and Partnerships at ÉTS. “Through its tools of international cooperation, CNRS supports the most promising cutting-edge joint research projects. The new international research laboratory brings together a powerful network of researchers from France and Québec to advance the knowledge and applications of AI. For the CNRS, this new lab is also an opportunity to strengthen more broadly its ties with the whole Canadian AI community,” said Antoine Petit, Chairman and CEO of CNRS. “AI at Paris-Saclay involves nearly 1,000 researchers, teacher-researchers, engineers and technicians and around forty laboratories, grouped together within our DataIA Institute. We will make our contribution to the ILLS in the form of the mobility of researchers, including the reception of Canadian colleagues at Paris-Saclay, the reception of Masters trainees, thesis funding in particular/among others. The University of Paris-Saclay is honored and proud to be associated with this signing ceremony for the creation of the IRL ILLS and to ensure its joint supervision" added Michel Guidal, Deputy Vice-President Research Sciences and Engineering at Université Paris-Saclay. “The ILLS, resulting from an unprecedented and international union, offers a unique potential for progress in the field of AI. It is an honor for CentraleSupélec to participate with our prestigious partners in this laboratory. Backed by this research, our teaching will thus be at the forefront of the world in terms of AI,” added Romain Soubeyran, Director of CentraleSupélec. The ILLS will join a burgeoning artificial intelligence (AI) sector in Montreal, which has attracted other major investments from government and business for the past several years. As a result, the city is one of the world’s leading hubs in this domain, with an estimated 27,000 workers in AI-related technologies and over 14,000 post-secondary students enrolled in AI-related study programs. The ILLS is the latest such laboratory to be launched in Canada, specifically in Quebec. In 2014, the CNRS and the Fonds de recherche du Québec – Nature et technologie (FRQNT) signed a letter of intent to support and promote the tradition of scientific cooperation that exists between France and Quebec. This collaboration has resulted in two International Research Laboratories in Quebec, as well as other shared research activities across the province. The CNRS has also established three other IRLs in Canada in partnership with other institutions. Present at the signing ceremony were: Frédéric Sanchez (Consul General of France), Remi Quirion (Quebec’s Chief Scientist), Antoine Petit (CNRS), Suzanne Fortier (McGill University), Francois Gagnon (ETS), Michel Guidal (Université Paris-Saclay), Franck Richecoeur (École CentraleSupélec), and Laurence Beaulieu (Mila). About McGill University Founded in Montreal, Quebec, in 1821, McGill University is Canada’s top ranked medical doctoral university. McGill is consistently ranked as one of the top universities, both nationally and internationally. It is a world-renowned institution of higher learning with research activities spanning three campuses, 11 faculties, 13 professional schools, 300 programs of study and over 39,000 students, including more than 10,400 graduate students. McGill attracts students from over 150 countries around the world, its 12,000 international students making up 30% of the student body. Over half of McGill students claim a first language other than English, including approximately 20% of our students who say French is their mother tongue.


Explorations in Cyber-Physical Systems Education

Communications of the ACM

The field of CPS draws from several areas in computer science, electrical engineering, and other engineering disciplines, including computer architecture, embedded systems, programming languages, software engineering, real-time systems, operating systems and networking, formal methods, algorithms, computation theory, control theory, signal processing, robotics, sensors and actuators, and computer security. Similarly, over the past 14 years, we have had students from computer science, electrical and computer engineering, mechanical engineering, civil engineering, and even bioengineering. Integrating this bewildering diversity of subject areas into a coherent whole for students with such a wide breadth of backgrounds has been a challenge we had to overcome. One approach would have been to not attempt such an integration. Instead, we could have opted for a collection of courses that together cover all the key areas in CPS.


AI glossary: Artificial Intelligence terms - Dataconomy

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The most completed list of Artificial Intelligence terms as a dictionary is here for you. Artificial intelligence is already all around us. As AI becomes increasingly prevalent in the workplace, it's more important than ever to keep up with the newest words and use types. Leaders in the field of artificial intelligence are well aware that it is revolutionizing business. So, how much do you know about it? You'll discover concise definitions for automation tools and phrases below. It's no surprise that the world is moving ahead quickly thanks to artificial intelligence's wonders. Technology has introduced new values and creativity to our personal and professional lives. While frightening at times, the rapid evolution has been complemented by artificial intelligence (AI) technology with new aspects. It has provided us with new phrases to add to our everyday vocab that we have never heard of before.


9 Completely Free Statistics Courses for Data Science

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This is a complete Free course for statistics. In this course, you will learn how to estimate parameters of a population using sample statistics, hypothesis testing and confidence intervals, t-tests and ANOVA, correlation and regression, and chi-squared test. This course is taught by industry professionals and you will learn by doing various exercises.