Скачать или смотреть Weights in Neural Networks | What are Weights in ANN | Role of the Weights in Neural Networks

In this video, we will deeply explore the concept of Weights in Neural Networks (ANN – Artificial Neural Networks). If you are learning Machine Learning, Deep Learning, or AI, then understanding weights is one of the most fundamental steps.

🔹 What are Weights in Neural Networks?

In an Artificial Neural Network, weights are numerical values that connect one neuron to another. Each input feature has a weight, and these weights determine how much influence that input has on the final output. Simply put, weights are the adjustable parameters that help the network learn patterns and make accurate predictions.

For example, in a simple neuron, the equation is:

Output = (Input × Weight) + Bias

Here, the weight decides the strength of the connection. If the weight is high, that input is more important; if the weight is low, the input contributes less.

🔹 Role of Weights in ANN

Learning Process: During training, neural networks adjust weights using algorithms like Gradient Descent to minimize the error.

Pattern Recognition: Weights help the network identify patterns in large datasets, such as images, speech, or text.

Decision Making: Different layers in the network have different weights, which together guide the decision-making process.

Accuracy Improvement: Properly optimized weights ensure the model provides accurate predictions.

🔹 Example of Weights in Action

Consider a neural network for image recognition. If you input a picture of a cat, the network will use weights to give more importance to features like “whiskers,” “pointy ears,” and “fur texture.” Over time, the network learns by adjusting weights until it can accurately classify cats from other animals.

🔹 Why are Weights Important?

They are the heart of neural networks.

Without weights, the network cannot learn.

They are tuned during backpropagation to reduce errors.

Good weight optimization = better AI performance.

🔹 Topics Covered in This Video

Definition of weights in ANN

Role of weights in training and prediction

Weights vs Bias in neural networks

How weights are updated (backpropagation)

Real-world examples of weights in AI systems

This video is perfect for students, beginners in AI/ML, computer science learners, and professionals who want to understand the basics of neural networks.

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