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    Artificial Intelligence (AI) - Simply Explained for Beginners

    Posted By: lucky_aut
    Artificial Intelligence (AI) - Simply Explained for Beginners

    Artificial Intelligence (AI) - Simply Explained for Beginners
    Duration: 57m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 314 MB
    Genre: eLearning | Language: English

    This video course on artificial intelligence is aimed at beginners and is designed to teach you the basics within the historical development of AI. For this reason, our journey begins with the section "Introduction and historical background of AI".

    Topics and contents of the lessons:

    I. Introduction and historical background

    What is AI - a philosophical consideration
    Strong and Weak AI
    The Turing Test
    The birth of the AI
    The era of great expectations
    Catching up with reality
    How to teach a machine to learn
    Distributed systems in the AI
    Deep Learning, Machine Learning, Natural Language Processing
    II. The general problem solver

    Proof Program - Logical Theorist
    Example from "Human Problem Solving" (Simon)
    The structure of a problem
    In this section, we first take up the initial techniques of AI. You will learn about the concepts and famous example systems that triggered this early phase of euphoria.

    III. Expert Systems

    Factual knowledge and heuristic knowledge
    Frames, Slots and Filler
    Forward and backward chaining
    The MYCIN Programme
    Probabilities in expert systems
    Example - Probability of hairline cracks
    In this section, we discuss expert systems that, similar to the general problem solvers, only deal with specific problems. But instead, they use excessive rules and facts in the form of a knowledge base.

    IV. Neuronal Networks

    The human neuron
    Signal processing of a neuron
    The Perceptron
    This section heralds a return to the idea of being able to reproduce the human brain and thus make it accessible to digital information processing in the form of neural networks. We look at the early approaches and highlight the ideas that were still missing to help neural networks achieve a breakthrough.

    V. Machine Learning (Deep Learning & Computer Vision)

    Example - potato harvest
    The birth year of Deep Learning
    Layers of deep learning networks
    Machine Vision / Computer Vision
    Convolutional Neural Network.
    The idea of an agent and its interaction in a multi-agent system is described in the fifth section. The main purpose of such a system is to distribute complexity over several instances.

    The sixth section deals with the breakthrough of multi-layer neural networks, machine learning, machine vision, speech recognition and some other applications of today's AI.

    More Info