Optimizers in Deep Learning: From Gradient Descent to Adam

📰 Medium · Machine Learning

Learn about optimizers in deep learning, from gradient descent to Adam, and how to apply them to train neural networks effectively

intermediate Published 19 Jun 2026

Action Steps

Implement gradient descent to update weights and biases in a neural network
Use momentum to escape local minima and improve convergence
Apply Adam optimizer to adapt learning rates for each parameter
Compare the performance of different optimizers on a dataset
Tune hyperparameters to optimize the learning process

Who Needs to Know This

Data scientists and machine learning engineers can benefit from understanding optimizers to improve the performance of their neural networks

Key Insight

💡 Choosing the right optimizer can significantly impact the performance of a neural network

Key Takeaways

Learn about optimizers in deep learning, from gradient descent to Adam, and how to apply them to train neural networks effectively

Full Article

Title: Optimizers in Deep Learning: From Gradient Descent to Adam

URL Source: https://medium.com/@naveengangumalla2001/optimizers-in-deep-learning-from-gradient-descent-to-adam-d652373ef7e4?source=rss------machine_learning-5

Published Time: 2026-06-19T17:36:43Z

Markdown Content:
# Optimizers in Deep Learning: From Gradient Descent to Adam | by GANGUMALLA NAVEEN KUMAR | Jun, 2026 | Medium

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# Optimizers in Deep Learning: From Gradient Descent to Adam

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## Introduction

Training a Neural Network involves finding the optimal values of weights and biases that minimize the loss (error) f

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Optimizers in Deep Learning: From Gradient Descent to Adam

Key Takeaways

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