Learning Rate – 1-Cycle Scheduling, exponential decay and Cyclic Exponential Decay (CED) – Part 4 – Day 45

Advanced Learning Rate Scheduling Methods for Machine Learning: Learning rate scheduling is critical in optimizing machine learning models, helping them converge faster and avoid pitfalls such as getting stuck in local minima. So far in our pervious days articles we have explained a lot about optimizers, learning rate schedules, etc. In this guide, we explore three key learning rate schedules: Exponential Decay, Cyclic Exponential Decay (CED), and 1-Cycle Scheduling, providing mathematical proofs, code implementations, and theory behind each method. 1. Exponential Decay Learning Rate Exponential Decay reduces the learning rate by a factor of , allowing larger updates early in training and smaller, more refined updates as the model approaches convergence. Formula: Where: is the learning rate at time step , is the initial learning rate, is the decay rate, controlling how fast the learning rate decreases, represents the current time step (or epoch). Mathematical Proof of Exponential Decay The core idea of exponential decay is that the learning rate decreases over time. Let’s prove that this results in convergence. The parameter update rule for gradient descent is: Substituting the exponentially decayed learning rate: As , the decay factor , meaning that the updates to become smaller and smaller, allowing...

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Theory Behind 1Cycle Learning Rate Scheduling & Learning Rate Schedules – Day 43

  The 1Cycle Learning Rate Policy: Accelerating Model Training  In our pervious article  (day 42) , we have explained The Power of Learning Rates in Deep Learning and Why Schedules Matter, lets now focus on 1Cycle Learning Rate to explain it  in more detail :  The 1Cycle Learning Rate Policy, first introduced by Leslie Smith in 2018, remains one of the most effective techniques for optimizing model training. By 2025, it continues to prove its efficiency, accelerating convergence by up to 10x compared to traditional learning rate schedules, such as constant or exponentially decaying rates. Today, both researchers and practitioners are pushing the boundaries of deep learning with this method, solidifying its role as a key component in the training of modern AI models. How the 1Cycle Policy Works The 1Cycle policy deviates from conventional learning rate schedules by alternating between two distinct phases: Phase 1: Increasing Learning Rate – The learning rate starts low and steadily rises to a peak value (η_max). This phase promotes rapid exploration of the loss landscape, avoiding sharp local minima. Phase 2: Decreasing Learning Rate – Once the peak is reached, the learning rate gradually decreases to a very low value, enabling the model...

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The Power of Learning Rates in Deep Learning and Why Schedules Matter – Day 42

  The Power of Learning Rates in Deep Learning and Why Schedules Matter In deep learning, one of the most critical yet often overlooked hyperparameters is the learning rate. It dictates how quickly a model updates its parameters during training, and finding the right learning rate can make the difference between a highly effective model and one that never converges. This post delves into the intricacies of learning rates, their sensitivity, and how to fine-tune training using learning rate schedules. Why is Learning Rate Important? The learning rate controls the size of the step the optimizer takes when adjusting model parameters during each iteration of training. If this step is too large, the model may overshoot the optimal values and fail to converge, leading to oscillations in the loss function. On the other hand, a very small learning rate causes training to proceed too slowly, taking many epochs to approach the global minimum. Learning Rate Sensitivity Here’s what happens with different learning rates: Too High: With a high learning rate, the model may diverge entirely, with the loss function increasing rapidly due to overshooting. This can cause the model to fail entirely. Too Low: A low learning rate leads to...

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