Nonlinear Optimization Explain | Deep Learning Training & Reinforcement Learning Math's | Lec No 31

Описание: Welcome to The Learning Studio! 🎓
In this thirty-first episode of our Mathematics Series, we explore Nonlinear Optimization — the mathematical engine that drives deep learning, reinforcement learning, and decision-making AI systems. This is where mathematics meets intelligence — turning abstract equations into smart, adaptive models.

In this video, you’ll learn:
The fundamentals of Nonlinear Optimization — how we minimize or maximize functions that don’t follow simple linear patterns.
How gradient-based algorithms like SGD and Adam train deep neural networks.
Why loss functions, local minima, and convexity matter in machine learning.
How optimization techniques help agents learn and adapt in reinforcement learning environments.
The mathematical intuition behind learning rates, convergence, and performance tuning in AI systems.

📌 Watch the complete playlist here:
   • Mathematics Series | Core Foundations for ...  

📌 Subscribe and follow us for the final episode on Differential Geometry and for upcoming practical mathematical studies that connect theory with real-world AI and robotics.
   / @whiteboardlearningstudio  

👉 Whether you’re a student, researcher, or AI enthusiast, this episode will help you see how Nonlinear Optimization turns mathematics into motion — powering the learning process behind every intelligent system.