Optimization Algorithms in AI and Machine Learning | Deep Learning Explained

Explore the critical role of optimization algorithms in machine learning and AI. These algorithms improve model performance, minimize errors, and enable smarter predictions. From Gradient Descent to advanced optimizers like Adam and RMSProp, they are essential for training neural networks and fine-tuning parameters for real-world applications.

🔍 What You’ll Learn in This Video

1️⃣ What Are Optimization Algorithms?

Understand how algorithms find the best solution by minimizing or maximizing a function.

Learn why optimization is crucial in training machine learning models.

2️⃣ Core Optimization Techniques

Gradient Descent: Minimizing cost functions effectively.

Stochastic Gradient Descent (SGD): Fast parameter updates for large datasets.

Momentum & Nesterov Accelerated Gradient (NAG): Improved convergence.

Adam Optimizer: Adaptive learning rates combining RMSProp and momentum.

3️⃣ Applications

Training deep learning models like RNNs, CNNs, and Transformers.

Fine-tuning hyperparameters to improve accuracy.

Solving real-world problems in image recognition, NLP, and predictive analytics.

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