Deep Learning Simplified: How NEURAL NETWORKS Work? [Real-World Example]

Discover how neural networks work and their incredible power, in this deep dive into how they work, using a real-world example of property valuation. This tutorial walks you through the basics of neural networks, focusing on how input parameters like area, number of bedrooms, and distance to the city influence property prices. You'll learn about the crucial role of hidden layers, which add flexibility and accuracy to predictions. Perfect for beginners, this video demystifies the complex world of deep learning and shows you how neural networks can revolutionize fields like real estate.
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In this video, you can learn the following:
- Basics of Neural Networks: An introduction to how neural networks are structured and the fundamental concepts involved in their operation.
- Application of Neural Networks: A practical example of how a neural network can be applied to real-world scenarios, specifically in property valuation.
- Understanding Input and Output Layers: How different input parameters (like area, number of bedrooms, distance to the city, and property age) contribute to the output (property price) in a neural network.
- Role of Hidden Layers: The importance of hidden layers in a neural network, how they add flexibility and accuracy, and how they differentiate neural networks from other machine learning algorithms.
- Neuron Functions: How individual neurons within the hidden layers focus on specific combinations of input parameters and how they influence the final output.
- Intuitive Understanding of Neural Networks: The video provides intuitive examples and analogies to help you grasp how neural networks "think" and make predictions.
- Real-World Application: Insight into how neural networks can be used in real estate for property valuation, showing the practical benefits of this technology in business.
- Deep Learning Principles: Understanding the principles behind deep learning and how neural networks are a powerful tool for making predictions based on data.