Neural Networks Train Using Computational Graphs and Basic Operations in Forward Pass
| Source: Mastodon | Original article
Neural networks train via computational graphs. Derivatives are obtained through the chain rule.
Neural networks rely on computational graphs to train, constructing these graphs in the forward pass by chaining basic operations into composite functions. The backward pass then applies the chain rule to obtain derivatives of the output with respect to every input, a process crucial for training.
This process matters because it enables the efficient computation of gradients, which are necessary for training models with millions or even billions of parameters. Understanding computational graphs and backpropagation is key to grasping how deep learning frameworks operate.
As researchers and developers continue to explore and explain computational graphs and their role in deep learning, we can expect further insights into the training of complex neural networks. Given the complexity of computing derivatives on these graphs, ongoing explanations and tutorials, such as those found on TensorTonic and GeeksforGeeks, will remain essential resources for those seeking to understand the fundamentals of neural network training.
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