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Fundamentals Of Ai For Beginners
What you'll learn
You are going to learn basics of AI
You are going to learn various types of AI models
You are going to learn language models
You are going to learn how to use AI in various applications
Requirements
You need to have internet to take this course
Description
The development of AI relies heavily on machine learning a subset of AI that focuses on enabling machines to learn from data and improve their performance over time without being explicitly programmed. In machine learning, algorithms are trained using large datasets to recognize patterns, make decisions, and predict outcomes. A key technique is supervised learning, where algorithms are trained with labeled data, learning to map input to output. Other approaches include unsupervised learning, where the machine identifies patterns in unlabeled data, and reinforcement learning, where an agent learns by interacting with an environment and receiving feedback in the form of rewards or penalties.AI can be classified into two types: narrow AI and general AI. Narrow AI is designed for specific tasks, such as image recognition or language translation, and is the most common form seen today. General AI, still theoretical, would have the ability to perform any intellectual task that a human can, encompassing a broader range of capabilities. It can struggle with tasks requiring common sense reasoning, emotional intelligence, or understanding context as deeply as humans do.AI is a rapidly evolving field that combines data, machine learning, and neural networks to replicate aspects of human intelligence, with immense potential for transforming industries and everyday life, yet requiring careful consideration of its societal implications.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Transfer Learning
Lecture 3 Language Modeling
Lecture 4 Various types of models
Lecture 5 Generative models
Lecture 6 Language Modeling
Lecture 7 Different Resources to learn
Lecture 8 Architecture of Resources
Lecture 9 Mapping in Languages
If you want to learn with detailed examples for every concept, this course will be for you
rapidgator.net:
ddownload.com:
Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.11 GB | Duration: 0h 59m
You are going to master the basics of AI and their models with Language models
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.11 GB | Duration: 0h 59m
You are going to master the basics of AI and their models with Language models
What you'll learn
You are going to learn basics of AI
You are going to learn various types of AI models
You are going to learn language models
You are going to learn how to use AI in various applications
Requirements
You need to have internet to take this course
Description
The development of AI relies heavily on machine learning a subset of AI that focuses on enabling machines to learn from data and improve their performance over time without being explicitly programmed. In machine learning, algorithms are trained using large datasets to recognize patterns, make decisions, and predict outcomes. A key technique is supervised learning, where algorithms are trained with labeled data, learning to map input to output. Other approaches include unsupervised learning, where the machine identifies patterns in unlabeled data, and reinforcement learning, where an agent learns by interacting with an environment and receiving feedback in the form of rewards or penalties.AI can be classified into two types: narrow AI and general AI. Narrow AI is designed for specific tasks, such as image recognition or language translation, and is the most common form seen today. General AI, still theoretical, would have the ability to perform any intellectual task that a human can, encompassing a broader range of capabilities. It can struggle with tasks requiring common sense reasoning, emotional intelligence, or understanding context as deeply as humans do.AI is a rapidly evolving field that combines data, machine learning, and neural networks to replicate aspects of human intelligence, with immense potential for transforming industries and everyday life, yet requiring careful consideration of its societal implications.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Transfer Learning
Lecture 3 Language Modeling
Lecture 4 Various types of models
Lecture 5 Generative models
Lecture 6 Language Modeling
Lecture 7 Different Resources to learn
Lecture 8 Architecture of Resources
Lecture 9 Mapping in Languages
If you want to learn with detailed examples for every concept, this course will be for you
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