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.
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
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.
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
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 lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
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.
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.
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.
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.
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
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.
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
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 lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
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.
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.
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.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
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
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.
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.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
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
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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
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7. Training a ML algorithm
• Choose data
• Optimize model parameters according to:
– Objective function
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Regression Classification
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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…