Backpropagation in Neural Networks | PyTorch Backpropagation – Theory and Example | Intellipaat

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Backpropagation in Neural Networks is a fundamental algorithm used to train neural networks by optimizing their weights. Essentially, it involves calculating the gradient of the loss function with respect to each weight by applying the chain rule, allowing the model to learn from errors. In this video, we learn how to improve the performance of the network on any given task by iteratively adjusting the weights based on these gradients. In this section we also learn about Backpropagation in Neural Networks, how it ensures that the learning process is efficient and effective, helping models converge to optimal solutions. Overall, understanding Backpropagation in Neural Networks is crucial for anyone interested in mastering machine learning techniques.

📖Below are the concepts covered in this ‘PyTorch Backpropagation Tutorial’:
00:00 – Introduction to Backpropagation in PyTorch
03:53 – Understanding the Neural Network
05:26 – Calculating Hidden Layer and Output Layer
08:02 – Computational Graph
08:34 – Calculating the cost function for Linear Regression
15:51 – Performing Backpropagation Using PyTorch

✅ What is backpropagation in PyTorch?
Backpropagation in PyTorch is the process of calculating the gradients of the loss function with respect to the model’s parameters to optimize its performance. Using PyTorch’s automatic differentiation feature, you can easily compute these gradients with just a few lines of code. This allows you to efficiently update the model weights during training, helping it learn from its mistakes and improve over time.

✅ How does backward propagation in ANN work?
Backward propagation in artificial neural networks (ANNs) works by calculating the gradients of the loss function with respect to each weight through the chain rule. It starts from the output layer, propagating the error backward through the network to adjust the weights. This process helps the ANN learn by minimizing the error and improving its predictions with each training iteration.

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