NN#6 — Neural Networks Decoded: Concepts Over Code

Author(s): RSD Studio.ai

Originally published on Towards AI.

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In the previous article, we dissected the mechanics of backpropagation, gradient descent, and the pivotal role of the chain rule in training neural networks. While these concepts form the backbone of deep learning, they are merely the starting point. The real challenge lies in optimizing these processes to ensure models converge efficiently, avoid local minima, and generalize well to unseen data.

This article dives into the art and science of optimization techniques, focusing on how to refine stochastic gradient descent (SGD), a form of which we studied in previous article, adapt learning rates dynamically and leverage advanced optimizers. By the end, you’ll understand how algorithms like Adam, RMSprop and Momentum transcend vanilla SGD to accelerate training and improve model performance.

Optimization algorithms are the techniques we use to fine-tune the internal parameters of our models, guiding them towards making more accurate predictions. They’re the mechanisms that allow neural networks to truly learn from their mistakes and improve over time. They are what makes it all make sense at the core level and more!

Stochastic Gradient Descent (SGD) is the workhorse of neural network optimization. Unlike batch gradient descent, which… Read the full blog for free on Medium.

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Published via Towards AI