- Instructor: Alper Karakus
- Lectures: 16
- Students: 420
- Duration: 10 weeks
Neural networks are parallel computing devices, which are basically an attempt to make a computer model of the brain. The main objective is to develop a system to perform various computational tasks faster than the traditional systems. This course by Academy Europe covers the basic concept and terminologies involved in Artificial Neural Network. Sections of this course also explain the architecture as well as the training algorithm of various networks used in ANN.
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Audience
This course will be useful for graduates, post graduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The reader can be a beginner or an advanced learner.
Prerequisites
ANN is an advanced topic, hence the reader must have basic knowledge of Algorithms, Programming, and Mathematics.
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Artificial Neural Network - Basic Concepts
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Lecture 2.1Artificial Neural Network – Basic Concepts
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Artificial Neural Network - Building Blocks
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Lecture 3.1Artificial Neural Network – Building Blocks
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Learning and Adaptation
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Lecture 4.1Learning and Adaptation
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Supervised Learning
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Lecture 5.1Supervised Learning
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Unsupervised Learning
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Lecture 6.1Unsupervised Learning
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Learning Vector Quantization
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Lecture 7.1Learning Vector Quantization
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Adaptive Resonance Theory
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Lecture 8.1Adaptive Resonance Theory
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Kohonen Self-Organizing Feature Maps
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Lecture 9.1Kohonen Self-Organizing Feature Maps
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Associate Memory Network
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Lecture 10.1Associate Memory Network
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Artificial Neural Network - Hopfield Networks
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Lecture 11.1Artificial Neural Network – Hopfield Networks
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Boltzmann Machine
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Lecture 12.1Boltzmann Machine
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Brain-State-in-a-Box Network
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Lecture 13.1Brain-State-in-a-Box Network
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Optimization Using Hopfield Network
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Lecture 14.1Optimization Using Hopfield Network
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Other Optimization Techniques
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Lecture 15.1Other Optimization Techniques
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Artificial Neural Network - Genetic Algorithm
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Lecture 16.1Artificial Neural Network – Genetic Algorithm
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Applications of Neural Networks
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Lecture 17.1Applications of Neural Networks
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