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Terms you want to get familiarized with!
AI Glossary :
Artificial Intelligence (AI): Artificial intelligence is intelligence demonstrated by machines, as opposed to the intelligence of humans and other animals. Example tasks in which this is done include speech recognition, computer vision, translation between languages, as well as other mappings of inputs. Wikipedia
Machine Learning (ML): Machine learning is a field devoted to understanding and building methods that let machines "learn" – that is, methods that leverage data to improve computer performance on some set of tasks. It is seen as a broad subfield of artificial intelligence. Wikipedia
Deep Learning: Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised, or unsupervised. Wikipedia
Neural Networks: Artificial neural networks, usually simply called neural networks or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Wikipedia
Natural Language Processing (NLP): Natural language processing is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. Wikipedia
Reinforcement Learning: Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take action in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Wikipedia
Computer Vision: Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Wikipedia
Supervised Learning: Supervised learning is a machine learning paradigm for problems where the available data consists of labeled examples, meaning that each data point contains features and an associated label. Wikipedia
Unsupervised Learning: Unsupervised learning is a type of algorithm that learns patterns from untagged data. The goal is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and then generate imaginative content from it. Wikipedia
Transfer Learning: Transfer learning is a research problem in machine learning that focuses on applying knowledge gained while solving one task to a related task. For example, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks. Wikipedia
Learn to Earn Glossary:
Gamification: Gamification is adding game mechanics into nongame environments, like a website, online community, learning management system, or business intranet to increase participation. The goal of gamification is to engage with consumers, employees, and partners to inspire collaborate, share, and interact.
Microlearning: The concept of Microlearning originates from the Greek word 'Micro' which means 'small'. It refers to a set of compact e-learning modules that are based mainly on keeping the 'learner fatigue in mind. The learning modules could be educational, professional, or skill-based. Wikipedia
Learning Management System (LMS): A learning management system is a software application for the administration, documentation, tracking, reporting, automation, and delivery of educational courses, training programs, materials, or learning and development programs. The learning management system concept emerged directly from e-Learning. Wikipedia
Competency-based learning: Competency-based learning or competency-based education is a framework for teaching and assessment of learning. It is also described as a type of education based on predetermined "competencies," which focuses on outcomes and real-world performance. Wikipedia
Personalized Learning: Personalized learning, individualized instruction, personal learning environment, and direct instruction all refer to efforts to tailor education to meet the different needs of students. Wikipedia
Learning Analytics: Learning analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. Wikipedia
Self-paced Learning: Self-paced instruction is any kind of instruction that proceeds based on learner response. The content itself can be curriculum, corporate training, technical tutorials, or any other subject that does not require the immediate response of an instructor. Wikipedia
Blended Learning: Blended learning, also known as technology-mediated instruction, web-enhanced instruction, or mixed-mode instruction, is an approach to education that combines online educational materials and opportunities for interaction online with physical place-based classroom methods. Wikipedia
Synchronous Learning: Synchronous learning refers to a learning event in which a group of students is engaging in learning at the same time. Before learning technology allowed for synchronous learning environments, most online education took place through asynchronous learning methods. Wikipedia
Asynchronous Learning: Asynchronous learning is a general term used to describe forms of education, instruction, and learning that do not occur in the same place or at the same time. It uses resources that facilitate information sharing outside the constraints of time and place among a network of people. Wikipedia
Blockchain: A blockchain is a distributed database or ledger shared among a computer network's nodes. They are best known for their crucial role in cryptocurrency systems for maintaining a secure and decentralized record of transactions, but they are not limited to cryptocurrency uses. Blockchains can be used to make data in any industry immutable—the term used to describe the inability to be altered.
Cryptocurrency: A cryptocurrency, crypto-currency, or crypto is a digital currency designed to work as a medium of exchange through a computer network that is not reliant on any central authority, such as a government or bank, to uphold or maintain it. Wikipedia
Mining: Cryptocurrency mining is a process of creating new digital "coins." However, that is as far as simplicity goes. The process of recovering these coins requires solving complex puzzles, validating cryptocurrency transactions on a blockchain network, and adding them to a distributed ledger to locate them.
Consensus: A consensus mechanism is any method used to achieve agreement, trust, and security across a decentralized computer network. In the context of blockchains and cryptocurrencies, proof-of-work (PoW) and proof-of-stake (PoS) are two of the most prevalent consensus mechanisms.
Hash Function: A hash is a function that meets the encrypted demands needed to secure information. Hashes are of a fixed length, making it nearly impossible to guess the hash if someone was trying to crack a blockchain. The same data will always produce the same hashed value. Hashes are one of the backbones of the blockchain network.
Proof-of-Work (PoW): Proof-of-work is a form of cryptographic proof in which one party proves to others that a certain amount of a specific computational effort has been expended. Verifiers can subsequently confirm this expenditure with minimal effort on their part. Wikipedia
Public Key Infrastructure (PKI): A Public Key Infrastructure (PKI) is a combination of policies, procedures, and technology needed to manage digital certificates in a public key cryptography scheme.
Distributed Ledger: Distributed ledger technology is a platform that uses ledgers stored on separate, connected devices in a network to ensure data accuracy and security. Blockchains evolved from distributed ledgers to address growing concerns that too many third parties are involved in too many transactions.
Block Height: Block height refers to a specific location in a blockchain, measured by how many confirmed blocks precede it. The current block height of a blockchain is an indication of its current size or time in existence.
Governance token: Governance tokens are a type of cryptocurrency that allow token holders to vote on the direction of a blockchain project. The primary purpose of governance tokens is to decentralize decision-making and to give holders a say in how the project is run.
Utility token: A utility token is a cryptocurrency on a smart contract blockchain that serves a specific function in a crypto project's ecosystem. Unlike cryptos like Bitcoin (BTC), utility tokens aren't designed to be a real-world medium of exchange.
Avatar: A NFT avatar is a digital image, often in a cartoonish and pixelated design, mostly used as profile pictures for Web 2 and Web 3 social media platforms. You can purchase them with any cryptocurrency in your crypto wallet, such as Ethereum or Binance.
Metaverse: Crypto metaverses are immersive virtual worlds with immense social and financial potential. Their use of blockchain infrastructure enables them to tap into the wider crypto economy, making virtual items exchangeable for real economic value beyond the confines of the metaverse.