Chapter 6: Embeddings, the Forward Pass, and the Loss Function
📰 Dev.to · Gary Jackson
Learn to implement embeddings, forward pass, and loss function for a neural network
Action Steps
- Implement token and position embeddings using learned vector identities
- Assemble a minimal forward pass to logits
- Compute cross-entropy loss for the model
- Apply the forward pass and loss function to a sample dataset
- Compare the performance of the model with and without embeddings
Who Needs to Know This
This lesson is beneficial for machine learning engineers and data scientists who want to understand the fundamentals of neural network implementation. It can be applied by ML engineers to improve their model's performance and by data scientists to better understand their data.
Key Insight
💡 Embeddings and forward pass are crucial components of a neural network, and cross-entropy loss is a common choice for classification problems
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🤖 Implement embeddings, forward pass, and loss function for neural networks
Key Takeaways
Learn to implement embeddings, forward pass, and loss function for a neural network
Full Article
Give tokens and positions learned vector identities, assemble a minimal forward pass to logits, and compute cross-entropy loss.
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