Optimizers in Deep Learning: From Gradient Descent to Adam

2026-06-19 · Source: NLP on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, long

Summary

Deep learning optimizers are crucial for training neural networks by efficiently adjusting weights and biases to minimize loss. The article details the evolution from basic Gradient Descent (GD), which uses the entire dataset, to more advanced methods. Mini-Batch Gradient Descent improves GD by updating weights with small data subsets (typically 32-256 samples), balancing speed and stability. Momentum-Based GD (using a momentum coefficient like β=0.9) accelerates learning and reduces oscillations by incorporating past gradients. AdaGrad adapts learning rates per parameter, effective for sparse data but prone to premature learning cessation. RMSProp (with decay rate β=0.9) addresses AdaGrad's issue by using an exponentially decaying average of squared gradients. Finally, the Adam optimizer (combining Momentum with β1=0.9 and RMSProp with β2=0.999, ε=1e-8, plus bias correction) offers fast, stable, and adaptive convergence, making it a widely adopted default for many deep learning tasks.

Key takeaway

For machine learning engineers or AI scientists selecting an optimizer for model training, your choice significantly impacts training speed, stability, and final model performance. You should start with Adam or AdamW as a robust default for most architectures. If seeking peak generalization on well-understood problems like CNN image classification, consider tuning SGD with Momentum. For sparse data, experiment with AdaGrad or RMSProp. Always pair your optimizer with a suitable learning rate schedule to ensure effective training.

Key insights

Optimizers guide neural network training by efficiently updating parameters to minimize loss, evolving from basic GD to adaptive Adam.

Principles

• Adaptive learning rates improve sparse data handling.
• Momentum smooths convergence and escapes local minima.
• Batching balances training speed and stability.

Method

Gradient Descent involves initializing weights, forward propagation, computing loss, calculating gradients, and iteratively updating weights using θ = θ - η ∇ J(θ) until minimum loss is reached.

In practice

• Start with Adam or AdamW for most tasks.
• Consider SGD with Momentum for best generalization.
• Use AdaGrad/RMSProp for sparse datasets.

Topics

• Deep Learning Optimizers
• Gradient Descent
• Adam Optimizer
• Mini-Batch GD
• Adaptive Learning Rates
• Neural Network Training

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

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