Foundations of Deep Learning: Gradient Descent, Back Propagation and Self-Attention

2026-04-26 · Source: Naturallanguageprocessing on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Novice, medium

Summary

This article explains the foundational algorithms underpinning modern Large Language Models (LLMs) like GPT, Opus, Sonnet, and Llama: Gradient Descent, Back Propagation, and Self-Attention. Gradient Descent, introduced in 1847 by Augustin-Louis Cauchy, is an iterative optimization technique used to minimize a differentiable multivariate function, specifically reducing loss in neural networks by adjusting neuron weights. Back Propagation, the backbone of deep learning, efficiently calculates and propagates error gradients through a neural network using the chain rule of derivatives to update weights. Self-Attention, introduced in the 2017 paper "Attention is all you need," allows models to selectively focus on relevant parts of an input sequence, capturing long-range dependencies and enabling context-aware word representations, which is a critical component of Transformer architectures.

Key takeaway

For machine learning engineers building or optimizing neural networks, understanding the interplay of Gradient Descent, Back Propagation, and Self-Attention is crucial. You should select appropriate optimizers like Adam and fine-tune learning rates (e.g., 0.001 to 0.1) to achieve optimal model performance. Additionally, leveraging self-attention mechanisms within Transformer architectures is essential for developing context-aware models, especially for natural language processing tasks.

Key insights

Gradient Descent, Back Propagation, and Self-Attention are core algorithms enabling modern neural networks and LLMs.

Principles

• Optimal learning rates are typically between 0.001 and 0.1.
• Attention mechanisms allow models to selectively focus on input.
• Transformers use various attention types for different tasks.

Method

Gradient Descent minimizes loss by iteratively adjusting weights using a learning rate. Back Propagation computes gradients via the chain rule. Self-Attention uses Query, Key, and Value vectors to weigh input relevance.

In practice

• Use Adam as a stable optimizer for neural networks.
• Employ Word2Vec for basic word embeddings.
• Implement Transformers for sequence-to-sequence tasks.

Topics

• Gradient Descent
• Backpropagation
• Self-Attention Mechanism
• Neural Networks
• Large Language Models

Best for: AI Student, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Naturallanguageprocessing on Medium.