The course helps you gain an advanced level understanding of Machine Learning application and algorithm like regression, clustering, classification, and prediction. It also covers deep learning and Spark Machine learning. The course includes 2 industry-based projects on designing recommendation and prediction system. It’s best suited for data scientists and analytics professionals.
The course helps you gain an advanced level understanding of Machine Learning application and algorithm like regression, clustering, classification, and prediction. It also covers deep learning and Spark Machine learning. The course includes 2 industry-based projects on designing recommendation and prediction system. It’s best suited for data scientists and analytics professionals.
The course helps you gain an advanced level understanding of Machine Learning application and algorithm like regression, clustering, classification, and prediction. It also covers deep learning and Spark Machine learning. The course includes 2 industry-based projects on designing recommendation and prediction system. It’s best suited for data scientists and analytics professionals.
The course helps you gain an advanced level understanding of Machine Learning application and algorithm like regression, clustering, classification, and prediction. It also covers deep learning and Spark Machine learning. The course includes 2 industry-based projects on designing recommendation and prediction system. It’s best suited for data scientists and analytics professionals.
The course helps you gain an advanced level understanding of Machine Learning application and algorithm like regression, clustering, classification, and prediction. It also covers deep learning and Spark Machine learning. The course includes 2 industry-based projects on designing recommendation and prediction system. It’s best suited for data scientists and analytics professionals.
The course helps you gain an advanced level understanding of Machine Learning application and algorithm like regression, clustering, classification, and prediction. It also covers deep learning and Spark Machine learning. The course includes 2 industry-based projects on designing recommendation and prediction system. It’s best suited for data scientists and analytics professionals.
The course helps you gain an advanced level understanding of Machine Learning application and algorithm like regression, clustering, classification, and prediction. It also covers deep learning and Spark Machine learning. The course includes 2 industry-based projects on designing recommendation and prediction system. It’s best suited for data scientists and analytics professionals.
The course helps you gain an advanced level understanding of Machine Learning application and algorithm like regression, clustering, classification, and prediction. It also covers deep learning and Spark Machine learning. The course includes 2 industry-based projects on designing recommendation and prediction system. It’s best suited for data scientists and analytics professionals.
The course helps you gain an advanced level understanding of Machine Learning application and algorithm like regression, clustering, classification, and prediction. It also covers deep learning and Spark Machine learning. The course includes 2 industry-based projects on designing recommendation and prediction system. It’s best suited for data scientists and analytics professionals.
The course helps you gain an advanced level understanding of Machine Learning application and algorithm like regression, clustering, classification, and prediction. It also covers deep learning and Spark Machine learning. The course includes 2 industry-based projects on designing recommendation and prediction system. It’s best suited for data scientists and analytics professionals.
The course helps you gain an advanced level understanding of Machine Learning application and algorithm like regression, clustering, classification, and prediction. It also covers deep learning and Spark Machine learning. The course includes 2 industry-based projects on designing recommendation and prediction system. It’s best suited for data scientists and analytics professionals.
Scott Clark, Co-Founder and CEO, SigOpt at MLconf SF 2016MLconf
Using Bayesian Optimization to Tune Machine Learning Models: In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. We will motivate the problem and give example applications.
We will also talk about our development of a robust benchmark suite for our algorithms including test selection, metric design, infrastructure architecture, visualization, and comparison to other standard and open source methods. We will discuss how this evaluation framework empowers our research engineers to confidently and quickly make changes to our core optimization engine.
We will end with an in-depth example of using these methods to tune the features and hyperparameters of a real world problem and give several real world applications.
Building ML Pipelines:
- What do ML Pipelines Look Like?
- Building one ML pipeline
- ML pipeline in code
- Why use ML pipeline?
By Debidatta Dwibedi, presented at Data Science Meetup at InMobi.
http://technology.inmobi.com/events/data-science-meetup
From Black Box to Black Magic, Pycon Ireland 2014Gloria Lovera
Machine learning algorithms in automotive field.
If you are interested in, I suggest also this presentation:
http://www.slideshare.net/bix883/machine-learning-virtual-sensors-automotive-intelligent-tire
Heuristic design of experiments w meta gradient searchGreg Makowski
Once you have started learning about predictive algorithms, and the basic knowledge discovery in databases process, what is the next level of detail to learn for a consulting project?
* Give examples of the many model training parameters
* Track results in a "model notebook"
* Use a model metric that combines both accuracy and generalization to rank models
* How to strategically search over the model training parameters - use a gradient descent approach
* One way to describe an arbitrarily complex predictive system is by using sensitivity analysis
Bridging the Gap: Machine Learning for Ubiquitous Computing -- EvaluationThomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (evaluation session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Nils Hammerla <n.hammerla@gmail.com>
video recording of talks as they wer held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
Application of Machine Learning in AgricultureAman Vasisht
With the growing trend of machine learning, it is needless to say how machine learning can help reap benefits in agriculture. It will be boon for the farmer welfare.
Machine learning for IoT - unpacking the blackboxIvo Andreev
Have you ever considered Machine Learning as a black box? It sounds as a kind of magic happening. Although being one among many solutions available, Azure ML has proved to be a great balance between flexibility, usability and affordable price. But how does Azure ML compare with the other ML providers? How to choose the appropriate algorithm? Do you understand the key performance indicators and how to improve the quality of your models? The session is about understanding the black box and using it for IoT workload and not only.
The objective of this project is to find out whether a novice investor or trader will be able to invest in stocks that are trustworthy by applying machine learning algorithms. Moreover, is it possible to accomplish this solely by analyzing the financial indicators of a company? The sample comprises of 4392 US-based companies with 225 financial indicators as features for the 2018 financial year falling under macro-area.
Class imbalance problems frequently occur in real-world tasks, and conventional deep learning algorithms are well known for performance degradation on imbalanced training datasets. To mitigate this problem, many approaches have aimed to balance among given classes by re-weighting or re-sampling training samples. These re-balancing methods increase the impact of minority classes and reduce the influence of majority classes on the output of models. However, the extracted representations may be of poor quality owing to the limited number of minority samples. To handle this restriction, several methods have been developed that increase the representations of minority samples by leveraging the features of the majority samples. Despite extensive recent studies, no deep analysis has been conducted on determination of classes to be augmented and strength of augmentation has been conducted. In this study, we first investigate the correlation between the degree of augmentation and class-wise performance, and find that the proper degree of augmentation must be allocated for each class to mitigate class imbalance problems. Motivated by this finding, we propose a simple and efficient novel curriculum, which is designed to find the appropriate per-class strength of data augmentation, called CUDA: CUrriculum of Data Augmentation for long-tailed recognition. CUDA can simply be integrated into existing long-tailed recognition methods. We present the results of experiments showing that CUDA effectively achieves better generalization performance compared to the state-of-the-art method on various imbalanced datasets such as CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018.
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
In olden days for controlling the manufacturing processes relays were used. Because of excessive consumption of power it is difficult to figure out the linked problems with it, therefore it must be regularly replaced. To solve the problems, Programmable Logic Controller was unveiled. For more information join the electrical automation course to make your career in this field.
In olden days for controlling the manufacturing processes relays were used. Because of excessive consumption of power it is difficult to figure out the linked problems with it, therefore it must be regularly replaced. To solve the problems, Programmable Logic Controller was unveiled. For more information join the electrical automation course to make your career in this field.
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Scott Clark, Co-Founder and CEO, SigOpt at MLconf SF 2016MLconf
Using Bayesian Optimization to Tune Machine Learning Models: In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. We will motivate the problem and give example applications.
We will also talk about our development of a robust benchmark suite for our algorithms including test selection, metric design, infrastructure architecture, visualization, and comparison to other standard and open source methods. We will discuss how this evaluation framework empowers our research engineers to confidently and quickly make changes to our core optimization engine.
We will end with an in-depth example of using these methods to tune the features and hyperparameters of a real world problem and give several real world applications.
Building ML Pipelines:
- What do ML Pipelines Look Like?
- Building one ML pipeline
- ML pipeline in code
- Why use ML pipeline?
By Debidatta Dwibedi, presented at Data Science Meetup at InMobi.
http://technology.inmobi.com/events/data-science-meetup
From Black Box to Black Magic, Pycon Ireland 2014Gloria Lovera
Machine learning algorithms in automotive field.
If you are interested in, I suggest also this presentation:
http://www.slideshare.net/bix883/machine-learning-virtual-sensors-automotive-intelligent-tire
Heuristic design of experiments w meta gradient searchGreg Makowski
Once you have started learning about predictive algorithms, and the basic knowledge discovery in databases process, what is the next level of detail to learn for a consulting project?
* Give examples of the many model training parameters
* Track results in a "model notebook"
* Use a model metric that combines both accuracy and generalization to rank models
* How to strategically search over the model training parameters - use a gradient descent approach
* One way to describe an arbitrarily complex predictive system is by using sensitivity analysis
Bridging the Gap: Machine Learning for Ubiquitous Computing -- EvaluationThomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (evaluation session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Nils Hammerla <n.hammerla@gmail.com>
video recording of talks as they wer held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
Application of Machine Learning in AgricultureAman Vasisht
With the growing trend of machine learning, it is needless to say how machine learning can help reap benefits in agriculture. It will be boon for the farmer welfare.
Machine learning for IoT - unpacking the blackboxIvo Andreev
Have you ever considered Machine Learning as a black box? It sounds as a kind of magic happening. Although being one among many solutions available, Azure ML has proved to be a great balance between flexibility, usability and affordable price. But how does Azure ML compare with the other ML providers? How to choose the appropriate algorithm? Do you understand the key performance indicators and how to improve the quality of your models? The session is about understanding the black box and using it for IoT workload and not only.
The objective of this project is to find out whether a novice investor or trader will be able to invest in stocks that are trustworthy by applying machine learning algorithms. Moreover, is it possible to accomplish this solely by analyzing the financial indicators of a company? The sample comprises of 4392 US-based companies with 225 financial indicators as features for the 2018 financial year falling under macro-area.
Class imbalance problems frequently occur in real-world tasks, and conventional deep learning algorithms are well known for performance degradation on imbalanced training datasets. To mitigate this problem, many approaches have aimed to balance among given classes by re-weighting or re-sampling training samples. These re-balancing methods increase the impact of minority classes and reduce the influence of majority classes on the output of models. However, the extracted representations may be of poor quality owing to the limited number of minority samples. To handle this restriction, several methods have been developed that increase the representations of minority samples by leveraging the features of the majority samples. Despite extensive recent studies, no deep analysis has been conducted on determination of classes to be augmented and strength of augmentation has been conducted. In this study, we first investigate the correlation between the degree of augmentation and class-wise performance, and find that the proper degree of augmentation must be allocated for each class to mitigate class imbalance problems. Motivated by this finding, we propose a simple and efficient novel curriculum, which is designed to find the appropriate per-class strength of data augmentation, called CUDA: CUrriculum of Data Augmentation for long-tailed recognition. CUDA can simply be integrated into existing long-tailed recognition methods. We present the results of experiments showing that CUDA effectively achieves better generalization performance compared to the state-of-the-art method on various imbalanced datasets such as CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018.
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
In olden days for controlling the manufacturing processes relays were used. Because of excessive consumption of power it is difficult to figure out the linked problems with it, therefore it must be regularly replaced. To solve the problems, Programmable Logic Controller was unveiled. For more information join the electrical automation course to make your career in this field.
In olden days for controlling the manufacturing processes relays were used. Because of excessive consumption of power it is difficult to figure out the linked problems with it, therefore it must be regularly replaced. To solve the problems, Programmable Logic Controller was unveiled. For more information join the electrical automation course to make your career in this field.
In olden days for controlling the manufacturing processes relays were used. Because of excessive consumption of power it is difficult to figure out the linked problems with it, therefore it must be regularly replaced. To solve the problems, Programmable Logic Controller was unveiled. For more information join the electrical automation course to make your career in this field.
In olden days for controlling the manufacturing processes relays were used. Because of excessive consumption of power it is difficult to figure out the linked problems with it, therefore it must be regularly replaced. To solve the problems, Programmable Logic Controller was unveiled. For more information join the electrical automation course to make your career in this field.
In olden days for controlling the manufacturing processes relays were used. Because of excessive consumption of power it is difficult to figure out the linked problems with it, therefore it must be regularly replaced. To solve the problems, Programmable Logic Controller was unveiled. For more information join the electrical automation course to make your career in this field.
In olden days for controlling the manufacturing processes relays were used. Because of excessive consumption of power it is difficult to figure out the linked problems with it, therefore it must be regularly replaced. To solve the problems, Programmable Logic Controller was unveiled. For more information join the electrical automation course to make your career in this field.
E2Matrix Jalandhar provides Best Big Data training based on current industry standards that helps attendees to secure placements in their dream jobs at MNCs. E2Matrix Provides Best Big Data Training in Jalandhar Amritsar Ludhiana Phagwara Mohali Chandigarh. E2Matrix is one of the best Big Data training institute offering hands on practical knowledge. At E2Matrix Big Data training is conducted by subject specialist corporate professionals best experience in managing real-time Big Data projects. E2Matrix implements a blend of academic learning and practical sessions to give the student optimum exposure. At E2Matrix’s well-equipped Big Data training Institute aspirants learn the skills for Big Data Overview, Use Cases, Data Analytics Process, Data Preparation, Tools for Data Preparation, Hands on Exercise : Using SQL and NoSql DB's, Hands on Exercise : Usage of Tools, Data Analysis Introduction, Classification, Data Visualization using R, Automation Testing Training on real time projects.
E2Matrix Jalandhar provides Best Big Data training based on current industry standards that helps attendees to secure placements in their dream jobs at MNCs. E2Matrix Provides Best Big Data Training in Jalandhar Amritsar Ludhiana Phagwara Mohali Chandigarh. E2Matrix is one of the best Big Data training institute offering hands on practical knowledge. At E2Matrix Big Data training is conducted by subject specialist corporate professionals best experience in managing real-time Big Data projects. E2Matrix implements a blend of academic learning and practical sessions to give the student optimum exposure. At E2Matrix’s well-equipped Big Data training Institute aspirants learn the skills for Big Data Overview, Use Cases, Data Analytics Process, Data Preparation, Tools for Data Preparation, Hands on Exercise : Using SQL and NoSql DB's, Hands on Exercise : Usage of Tools, Data Analysis Introduction, Classification, Data Visualization using R, Automation Testing Training on real time projects.
E2Matrix Jalandhar provides Best Big Data training based on current industry standards that helps attendees to secure placements in their dream jobs at MNCs. E2Matrix Provides Best Big Data Training in Jalandhar Amritsar Ludhiana Phagwara Mohali Chandigarh. E2Matrix is one of the best Big Data training institute offering hands on practical knowledge. At E2Matrix Big Data training is conducted by subject specialist corporate professionals best experience in managing real-time Big Data projects. E2Matrix implements a blend of academic learning and practical sessions to give the student optimum exposure. At E2Matrix’s well-equipped Big Data training Institute aspirants learn the skills for Big Data Overview, Use Cases, Data Analytics Process, Data Preparation, Tools for Data Preparation, Hands on Exercise : Using SQL and NoSql DB's, Hands on Exercise : Usage of Tools, Data Analysis Introduction, Classification, Data Visualization using R, Automation Testing Training on real time projects.
The Raspberry Pi is a credit-card sized computer
It can be plugged into your TV and a keyboard, and can be used for many of the things that your average desktop does - spreadsheets, word-processing, games and it also plays high-definition video.
measuring approximately 9cm x 5.5cm
History : The Raspberry Pi is the work of the Raspberry Pi Foundation, a charitable organisation.
UK registered charity (No. 1129409), May 2009
It's supported by the University of Cambridge Computer Laboratory and tech firm Broadcomm
Motivation : Computer science skills increasingly important
Decline in CS student numbers
Access to computers
Computers are the tool of the 21st century
Computer Science is concerned with much more than simply being able to use a computer.
Children should understand how they work and how to program them
The Raspberry Pi is a credit-card sized computer
It can be plugged into your TV and a keyboard, and can be used for many of the things that your average desktop does - spreadsheets, word-processing, games and it also plays high-definition video.
measuring approximately 9cm x 5.5cm
History : The Raspberry Pi is the work of the Raspberry Pi Foundation, a charitable organisation.
UK registered charity (No. 1129409), May 2009
It's supported by the University of Cambridge Computer Laboratory and tech firm Broadcomm
Motivation : Computer science skills increasingly important
Decline in CS student numbers
Access to computers
Computers are the tool of the 21st century
Computer Science is concerned with much more than simply being able to use a computer.
Children should understand how they work and how to program them
The Raspberry Pi is a credit-card sized computer
It can be plugged into your TV and a keyboard, and can be used for many of the things that your average desktop does - spreadsheets, word-processing, games and it also plays high-definition video.
measuring approximately 9cm x 5.5cm
History : The Raspberry Pi is the work of the Raspberry Pi Foundation, a charitable organisation.
UK registered charity (No. 1129409), May 2009
It's supported by the University of Cambridge Computer Laboratory and tech firm Broadcomm
Motivation : Computer science skills increasingly important
Decline in CS student numbers
Access to computers
Computers are the tool of the 21st century
Computer Science is concerned with much more than simply being able to use a computer.
Children should understand how they work and how to program them
The Raspberry Pi is a credit-card sized computer
It can be plugged into your TV and a keyboard, and can be used for many of the things that your average desktop does - spreadsheets, word-processing, games and it also plays high-definition video.
measuring approximately 9cm x 5.5cm
History : The Raspberry Pi is the work of the Raspberry Pi Foundation, a charitable organisation.
UK registered charity (No. 1129409), May 2009
It's supported by the University of Cambridge Computer Laboratory and tech firm Broadcomm
Motivation : Computer science skills increasingly important
Decline in CS student numbers
Access to computers
Computers are the tool of the 21st century
Computer Science is concerned with much more than simply being able to use a computer.
Children should understand how they work and how to program them
The Raspberry Pi is a credit-card sized computer
It can be plugged into your TV and a keyboard, and can be used for many of the things that your average desktop does - spreadsheets, word-processing, games and it also plays high-definition video.
measuring approximately 9cm x 5.5cm
History : The Raspberry Pi is the work of the Raspberry Pi Foundation, a charitable organisation.
UK registered charity (No. 1129409), May 2009
It's supported by the University of Cambridge Computer Laboratory and tech firm Broadcomm
Motivation : Computer science skills increasingly important
Decline in CS student numbers
Access to computers
Computers are the tool of the 21st century
Computer Science is concerned with much more than simply being able to use a computer.
Children should understand how they work and how to program them
The Raspberry Pi is a credit-card sized computer
It can be plugged into your TV and a keyboard, and can be used for many of the things that your average desktop does - spreadsheets, word-processing, games and it also plays high-definition video.
measuring approximately 9cm x 5.5cm
History : The Raspberry Pi is the work of the Raspberry Pi Foundation, a charitable organisation.
UK registered charity (No. 1129409), May 2009
It's supported by the University of Cambridge Computer Laboratory and tech firm Broadcomm
Motivation : Computer science skills increasingly important
Decline in CS student numbers
Access to computers
Computers are the tool of the 21st century
Computer Science is concerned with much more than simply being able to use a computer.
Children should understand how they work and how to program them
Selenium is a program mechanization instrument, normally utilized for composing end-to-end trial of web applications. A program mechanization apparatus does precisely what you would expect: robotize the control of a program so dreary errands can be computerized. It sounds like a straightforward issue to comprehend, however as we will see, a great deal needs to occur off camera to influence it to work. Before portraying the engineering of Selenium it sees how the different related bits of the venture fit together. At an abnormal state, Selenium is a suite of three apparatuses. The first of these apparatuses, Selenium IDE, is an expansion for Firefox that enables clients to record and playback tests. The last device, Selenium Grid, makes it conceivable to utilize the Selenium APIs to control program examples circulated over a framework of machines, enabling more tests to keep running in parallel. selenium training in Bangalore - Inside the undertaking, they are alluded to as "IDE", "WebDriver" and "Lattice". This part investigates the engineering of Selenium WebDriver.
Selenium is a program mechanization instrument, normally utilized for composing end-to-end trial of web applications.
A program mechanization apparatus does precisely what you would expect: robotize the control of a program so dreary errands can be computerized. It sounds like a straightforward issue to comprehend, however as we will see, a great deal needs to occur off camera to influence it to work.
Before portraying the engineering of Selenium it sees how the different related bits of the venture fit together. At an abnormal state, Selenium is a suite of three apparatuses. The first of these apparatuses, Selenium IDE, is an expansion for Firefox that enables clients to record and playback tests.
The last device, Selenium Grid, makes it conceivable to utilize the Selenium APIs to control program examples circulated over a framework of machines, enabling more tests to keep running in parallel. selenium training in Bangalore - Inside the undertaking, they are alluded to as "IDE", "WebDriver" and "Lattice". This part investigates the engineering of Selenium WebDriver.
Selenium is a program mechanization instrument, normally utilized for composing end-to-end trial of web applications.
A program mechanization apparatus does precisely what you would expect: robotize the control of a program so dreary errands can be computerized. It sounds like a straightforward issue to comprehend, however as we will see, a great deal needs to occur off camera to influence it to work.
Before portraying the engineering of Selenium it sees how the different related bits of the venture fit together. At an abnormal state, Selenium is a suite of three apparatuses. The first of these apparatuses, Selenium IDE, is an expansion for Firefox that enables clients to record and playback tests.
The last device, Selenium Grid, makes it conceivable to utilize the Selenium APIs to control program examples circulated over a framework of machines, enabling more tests to keep running in parallel. selenium training in Bangalore - Inside the undertaking, they are alluded to as "IDE", "WebDriver" and "Lattice". This part investigates the engineering of Selenium WebDriver.
Selenium is a program mechanization instrument, normally utilized for composing end-to-end trial of web applications.
A program mechanization apparatus does precisely what you would expect: robotize the control of a program so dreary errands can be computerized. It sounds like a straightforward issue to comprehend, however as we will see, a great deal needs to occur off camera to influence it to work.
Before portraying the engineering of Selenium it sees how the different related bits of the venture fit together. At an abnormal state, Selenium is a suite of three apparatuses. The first of these apparatuses, Selenium IDE, is an expansion for Firefox that enables clients to record and playback tests.
The last device, Selenium Grid, makes it conceivable to utilize the Selenium APIs to control program examples circulated over a framework of machines, enabling more tests to keep running in parallel. selenium training in Bangalore - Inside the undertaking, they are alluded to as "IDE", "WebDriver" and "Lattice". This part investigates the engineering of Selenium WebDriver.
Selenium is a program mechanization instrument, normally utilized for composing end-to-end trial of web applications.
A program mechanization apparatus does precisely what you would expect: robotize the control of a program so dreary errands can be computerized. It sounds like a straightforward issue to comprehend, however as we will see, a great deal needs to occur off camera to influence it to work.
Before portraying the engineering of Selenium it sees how the different related bits of the venture fit together. At an abnormal state, Selenium is a suite of three apparatuses. The first of these apparatuses, Selenium IDE, is an expansion for Firefox that enables clients to record and playback tests.
The last device, Selenium Grid, makes it conceivable to utilize the Selenium APIs to control program examples circulated over a framework of machines, enabling more tests to keep running in parallel. selenium training in Bangalore - Inside the undertaking, they are alluded to as "IDE", "WebDriver" and "Lattice". This part investigates the engineering of Selenium WebDriver.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
4. One size never fits all…
• Improving an algorithm:
– First option: better features
• Visualize classes
• Trends
• Histograms
– Next: make the algorithm smarter (more complicated)
• Interaction of features
• Better objective and training criteria
WEKA or GGOBI
5. -4 -2 0 2 4 6
-20
-10
0
10
20
30
40
y=1 + 0.5t + 4t2 - t3
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Categories of ML algorithms
By training:
Supervised (labeled) Unsupervised (unlabeled)
By model:
Non-parametric
Raw data only
Parametric
Model parameters only
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Kernel
methods
7. Training a ML algorithm
• Choose data
• Optimize model parameters according to:
– Objective function
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Regression Classification
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Mean Square Error
Max Margin
8. Pitfalls of ML algorithms
• Clean your features:
– Training volume: more is better
– Outliers: remove them!
– Dynamic range: normalize it!
• Generalization
– Over fitting
– Under fitting
• Speed: parametric vs. non
• What are you learning? …features, features, features…
14. K-Means clustering
•Planar decision boundaries,
depending on space you are in…
•Highly Efficient
•Not always great (but usually
pretty good)
•Needs good starting criteria
15. K-Nearest Neighbor
•Arbitrary decision boundaries
•Not so efficient…
•With enough data in each class…
optimal
•Easy to train, known as a lazy classifier
16. Mixture of Gaussians
•Arbitrary decision boundaries
with enough boundaries
•Efficient, depending on number
of models and Gaussians
•Can represent more than just
Gaussian distributions
•Generative, sometimes tough to
train up
•Spurious singularities
•Can get a distribution for a
specific class and feature(s)… and
get a Bayesian classifier
21. Hidden Markov Models
•Arbitrary Decision boundaries
•Efficiency depends on state
space and number of models
•Generalizes to incorporate
features that change over time
22. More sophisticated approaches
• Graphical models (like an HMM)
– Bayesian network
– Markov random fields
• Boosting
– Adaboost
• Voting
• Cascading
• Stacking…