Lecture 4.1 | Artificial neural network | Perceptron, Gradient Descent, Delta rule | #mlt #aktu #ml
9.2 هزار بار بازدید -
7 ماه پیش
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Welcome to a deep dive
Welcome to a deep dive into the world of Artificial Neural Networks (ANNs)! In this comprehensive video, we unravel the complexities of neural networks, exploring key topics that form the backbone of this transformative field.
Here's what we've got for you:
Perceptron: We begin with the fundamental building block of neural networks—the perceptron. Gain insights into how it processes information and lays the groundwork for more complex architectures.
Gradient Descent: Explore the optimization journey with Gradient Descent. Learn how this algorithm fine-tunes the neural network's parameters, enabling efficient learning and model improvement.
Delta Rule: Dive into the Delta Rule, a crucial concept in adjusting weights during the learning process. Understand its role in enhancing the adaptability of neural networks.
Backpropagation Algorithm: Uncover the mechanics of Backpropagation, a cornerstone in training multilayer neural networks. See how it facilitates learning by minimizing errors through iterative adjustments.
SOM Algorithm and Variants: Delve into the Self-Organizing Map (SOM) algorithm and its variants. Explore their applications in clustering and visualizing complex data structures.
By the end of this video, you'll have a holistic understanding of Artificial Neural Networks, from the basic perceptron to advanced optimization techniques, learning algorithms, and specialized models like SOM.
🔗 Timestamps:
00:00 - Introduction
03:55 - Perceptron
12:20 - Gradient Descent
14:45 - Delta Rule
17:30 - Backpropagation Algorithm
21:18 - SOM Algorithm
24:00 - SOM Variants
🔍 Hashtags:
#ArtificialNeuralNetworks #MachineLearning #DeepLearning #NeuralNetworkAlgorithms #GradientDescent #DeltaRule #Backpropagation #SOMAlgorithm #Tutorial #DataScience #AIExplained #aktu #mlt #machinelearningbasics #ai #ml #unit4 #aktuexam
Here's what we've got for you:
Perceptron: We begin with the fundamental building block of neural networks—the perceptron. Gain insights into how it processes information and lays the groundwork for more complex architectures.
Gradient Descent: Explore the optimization journey with Gradient Descent. Learn how this algorithm fine-tunes the neural network's parameters, enabling efficient learning and model improvement.
Delta Rule: Dive into the Delta Rule, a crucial concept in adjusting weights during the learning process. Understand its role in enhancing the adaptability of neural networks.
Backpropagation Algorithm: Uncover the mechanics of Backpropagation, a cornerstone in training multilayer neural networks. See how it facilitates learning by minimizing errors through iterative adjustments.
SOM Algorithm and Variants: Delve into the Self-Organizing Map (SOM) algorithm and its variants. Explore their applications in clustering and visualizing complex data structures.
By the end of this video, you'll have a holistic understanding of Artificial Neural Networks, from the basic perceptron to advanced optimization techniques, learning algorithms, and specialized models like SOM.
🔗 Timestamps:
00:00 - Introduction
03:55 - Perceptron
12:20 - Gradient Descent
14:45 - Delta Rule
17:30 - Backpropagation Algorithm
21:18 - SOM Algorithm
24:00 - SOM Variants
🔍 Hashtags:
#ArtificialNeuralNetworks #MachineLearning #DeepLearning #NeuralNetworkAlgorithms #GradientDescent #DeltaRule #Backpropagation #SOMAlgorithm #Tutorial #DataScience #AIExplained #aktu #mlt #machinelearningbasics #ai #ml #unit4 #aktuexam
7 ماه پیش
در تاریخ 1402/09/25 منتشر شده
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