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Newton’s Laws of Motion: The Original Gradient Descent

This text explores the connection between the gradient descent algorithm in machine learning and Newton’s laws of motion. It explains that gradient descent is used to update parameters in a neural network to minimize a loss function, drawing parallels to the concept of potential and conservative forces in Newtonian physics. The article emphasizes the unified mathematical framework underlying these seemingly disparate concepts.

 Newton’s Laws of Motion: The Original Gradient Descent

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AI Solutions

Exploring the shared language of gradient descent and Newton’s motion equations

Overview of a neural network training

In supervised neural network tasks, the goal is to minimize the difference between predicted and true values. This is achieved through a function called the loss function, denoted as L. Training the neural network involves updating the parameters to minimize the loss function, essentially optimizing via gradient descent.

Optimizing via gradient descent

Gradient descent is used to calculate new parameters by moving in the opposite direction of the gradient of the loss function, effectively minimizing the loss. This motion corresponds to the physical trajectory of a particle seeking to rest in the lowest possible potential around it, demonstrating the non-random nature of the algorithm.

Newton’s Second Law

Newton’s second law of motion has a mathematical formulation similar to the gradient descent equation. It describes how momentum of a body always points towards the direction where the potential decreases the fastest, with a step size given by Δt.

Practical AI Solutions

To evolve your company with AI, consider leveraging Newton’s Laws of Motion and AI for your advantage. AI can redefine your way of work by automating key customer interactions, identifying automation opportunities, defining measurable KPIs, selecting suitable AI solutions, and implementing AI usage gradually.

Spotlight on a Practical AI Solution

Consider the AI Sales Bot from itinai.com/aisalesbot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages.

Contact Us

For AI KPI management advice, connect with us at hello@itinai.com. For continuous insights into leveraging AI, stay tuned on our Telegram channel or Twitter.



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Vladimir Dyachkov, Ph.D
Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

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