Know how Computer Vision and Augmented Reality overlap each other?
Knoldus » ML, AI and Data Engineering
by Tanishka Garg
1w ago
Reading Time: 2 minutes To understand the overlapping of Computer Vision and Augmented Reality, Let’s understand what is computer vision, augumented reality and virtual reality. Computer Vision Augmented Reality Virtual Reality A direct comparison of Augmented Reality and Virtual Reality Application of Augmented Reality and Computer Vision Augmented Reality and Computer Vision in Autonomous Cars Security Monitoring with Augmented Vision and Computer Vision Augmented Reality and Computer Vision in The post Know how Computer Vision and Augmented Reality overlap each other? appeared first on Knol ..read more
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Build easy conversational AI – GCP Dialogflow
Knoldus » ML, AI and Data Engineering
by Tanishka Garg
1w ago
Reading Time: 3 minutes Dialogflow is a GCP framework that enables users to develop easy human-computer interaction technologies that can support Natural Language Processing (NLP). Basically, Dialogflow handles the job of translating natural language to machine-readable data using machine-learning models trained by your examples. Why use GCP Dialogflow A few reasons to use Dialogflow are – Building Blocks of GCP Dialogflow AGENTS The agent is basically your entire chatbot The post Build easy conversational AI – GCP Dialogflow appeared first on Knoldus Blogs ..read more
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MarkLogic And Machine Learning: Easy way of ML
Knoldus » ML, AI and Data Engineering
by Saurabh Suresh Dhotre
5M ago
Reading Time: 6 minutes Introduction Machine learning is a subfield of computer science. Used to deal with the construction of artificial intelligence systems that can learn without being explicitly programmed. It has been applied in many areas such as data analysis, pattern recognition, and understanding human behavior. MarkLogic combines database internals, search-style indexing, and application server behavior into a unified system. It uses XML and JSON documents along with The post MarkLogic And Machine Learning: Easy way of ML appeared first on Knoldus Blogs ..read more
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Explore OpenCV & Why Do We Need To Know About It?
Knoldus » ML, AI and Data Engineering
by Tanishka Garg
8M ago
Reading Time: 4 minutes OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. It was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. OpenCV OpenCV is the huge open-source library for computer vision, The post Explore OpenCV & Why Do We Need To Know About It? appeared first on Knoldus Blogs ..read more
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Introduction to Ensemble Learning
Knoldus » ML, AI and Data Engineering
by Aayush Srivastava
10M ago
Reading Time: 4 minutes Ensemble methods are techniques that create multiple models and then combine them to produce improved results. Ensemble learning usually produces more accurate solutions than a single model would. This has been the case in a number of machine learning competitions and, where the winning solutions used ensemble methods. Ensemble methods You must ensure that your models are independent of one another and when creating a The post Introduction to Ensemble Learning appeared first on Knoldus Blogs ..read more
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Activation Function : Everything You Needed To Know
Knoldus » ML, AI and Data Engineering
by Ayush
10M ago
Reading Time: 4 minutes An activation function is a mathematical function that accepts input and produces output. It translates the input to the output of a layer-specific perceptron. These functions cause neurons to activate. It’s a non-linear adjustment we make to input before sending it to the next layer of neurons. Transfer Function is the another name for it. We employ Activation functions for a variety of reasons : The post Activation Function : Everything You Needed To Know appeared first on Knoldus Blogs ..read more
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Paradigms in Pentaho Data Integration
Knoldus » ML, AI and Data Engineering
by Chiranjeev Kumar
11M ago
Reading Time: 4 minutes PDI has three paradigms for storing user input Arguments Parameters Variables Arguments A PDI argument is a named, user-supplied, single-value input given as a command-line argument (running a transformation or job manually from Pan or Kitchen, or as part of a script). Each transformation or job can have a maximum of 10 arguments. Each argument declared as space-separated values given after the rest of the The post Paradigms in Pentaho Data Integration appeared first on Knoldus Blogs ..read more
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Pentaho Database Connection
Knoldus » ML, AI and Data Engineering
by Harsh Vardhan
11M ago
Reading Time: 3 minutes If you want to work with a database, either read, write, view data, etc, in Pentaho the first thing you will have to do is to create a connection with that database. This blog will teach you how to do this. So, let’s start. Getting ready In order to set up the connection, you will require to know the connection settings. At least you will need The post Pentaho Database Connection appeared first on Knoldus Blogs ..read more
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Explore how to apply continual learning to your machine learning models
Knoldus » ML, AI and Data Engineering
by Tanishka Garg
1y ago
Reading Time: 3 minutes In this blog, we will be learning about continual learning (CI) importance in artificial intelligence. Continual Learning means the ability of a model to learn independently whenever new data comes in. Some may know it as auto-adaptive learning, or continual AutoML. The idea of CL is to mimic human’s ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. For example, we’ve The post Explore how to apply continual learning to your machine learning models appeared first on Knoldus Blogs ..read more
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How Feature selection techniques for machine learning are important?
Knoldus » ML, AI and Data Engineering
by Tanishka Garg
1y ago
Reading Time: 5 minutes Feature selection is a way of selecting the subset of the most relevant features from the original features set by removing the redundant, irrelevant, or noisy features. Features are the input variables that we provide to our models. Each column in our dataset constitutes a feature. To train an optimal model, we need to make sure that we use only the essential features. If we The post How Feature selection techniques for machine learning are important? appeared first on Knoldus Blogs ..read more
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