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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...

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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...

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863

Deep Learning Optimizers: NAdam, AdaMax, AdamW, and NAG Comparison – Day 41

  A Detailed Comparison of Deep Learning Optimizers: NAdam, AdaMax, AdamW, and NAG Introduction Optimizers are fundamental to training deep learning models effectively. They update the model’s parameters during training to minimize the loss function. In this article, we’ll compare four popular optimizers: NAdam, AdaMax, AdamW, and NAG. We’ll also explore their compatibility across frameworks like TensorFlow, PyTorch, and MLX for Apple Silicon, ensuring you choose the best optimizer for your specific machine learning task. 1. NAdam (Nesterov-accelerated Adam) Overview: NAdam combines the benefits of Adam with Nesterov Accelerated Gradient (NAG). It predicts the future direction of the gradient by...

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Adam Optimizer deeply explained by Understanding Local Minimum – Day 40

Introduction to Optimization Concepts Understanding Local Minimum, Global Minimum, and Gradient Descent in Optimization In optimization problems, especially in machine learning and deep learning, concepts like local minima, global minima, and gradient descent are central to how algorithms find optimal solutions. Let’s break down these concepts: 1. Local Minimum vs. Global Minimum Local Minimum: This is a point in the optimization landscape where the function value is lower than the surrounding points, but it might not be the lowest possible value overall. It’s “locally” the best solution, but there might be a better solution elsewhere in the space. Global Minimum:...

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Adam vs SGD vs AdaGrad vs RMSprop vs AdamW – Day 39

Choosing the Best Optimizer for Your Deep Learning Model When training deep learning models, choosing the right optimization algorithm can significantly impact your model’s performance, convergence speed, and generalization ability. Below, we will explore some of the most popular optimization algorithms, their strengths, the reasons they were invented, and the types of problems they are best suited for. 1. Stochastic Gradient Descent (SGD) Why It Was Invented SGD is one of the earliest and most fundamental optimization algorithms used in machine learning and deep learning. It was invented to handle the challenge of minimizing cost functions efficiently, particularly when dealing...

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AdaGrad vs RMSProp vs Adam: Why Adam is the Most Popular? – Day 38

A Comprehensive Guide to Optimization Algorithms: AdaGrad, RMSProp, and Adam In the realm of machine learning, selecting the right optimization algorithm can significantly impact the performance and efficiency of your models. Among the various options available, AdaGrad, RMSProp, and Adam are some of the most widely used optimization algorithms. Each of these algorithms has its own strengths and weaknesses. In this article, we’ll explore why AdaGrad ( which we explained fully on day 37 ) might not always be the best choice and how RMSProp & Adam could address some of its shortcomings. AdaGrad: Why It’s Not Always the Best...

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A Comprehensive Guide to AdaGrad: Origins, Mechanism, and Mathematical Proof – Day 37

Introduction to AdaGrad AdaGrad, short for Adaptive Gradient Algorithm, is a foundational optimization algorithm in machine learning and deep learning. It was introduced in 2011 by John Duchi, Elad Hazan, and Yoram Singer in their paper titled “Adaptive Subgradient Methods for Online Learning and Stochastic Optimization”. AdaGrad revolutionized the field by offering a solution to the limitations of traditional gradient descent, especially in scenarios involving sparse data and high-dimensional optimization problems. The Origins of AdaGrad The motivation behind AdaGrad was to improve the robustness and efficiency of the Stochastic Gradient Descent (SGD) method. In high-dimensional spaces, using a fixed learning...

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81

Nag as optimiser in deep learning – day 36

Nesterov Accelerated Gradient (NAG): A Comprehensive Overview Introduction to Nesterov Accelerated Gradient Nesterov Accelerated Gradient (NAG), also known as Nesterov Momentum, is an advanced optimization technique introduced by Yurii Nesterov in the early 1980s. It is an enhancement of the traditional momentum-based optimization used in gradient descent, designed to accelerate the convergence rate of the optimization process, particularly in the context of deep learning and complex optimization problems. How NAG Works The core idea behind NAG is the introduction of a “look-ahead” step before calculating the gradient, which allows for a more accurate and responsive update of parameters. In traditional...

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186

Momentum – part 3 – day 35

Comprehensive Guide: Understanding Gradient Descent and Momentum in Deep Learning Gradient descent is a cornerstone algorithm in the field of deep learning, serving as the primary method by which neural networks optimize their weights to minimize the loss function. This article will delve into the principles of gradient descent, its importance in deep learning, how momentum enhances its performance, and the role it plays in model training. We will also explore practical examples to illustrate these concepts. What is Gradient Descent? Gradient Descent is an optimization algorithm used to minimize a loss function by iteratively adjusting the model’s parameters (weights...

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Momentum vs Normalization in Deep learning -Part 2 – Day 34

Comparing Momentum and Normalization in Deep Learning: A Mathematical Perspective Momentum and normalization are two pivotal techniques in deep learning that enhance the efficiency and stability of training. This article explores the mathematics behind these methods, provides examples with and without these techniques, and demonstrates why they are beneficial for deep learning models.  Comparing Momentum and Normalization Momentum: Smoothing and Accelerating Convergence Momentum is an optimization technique that modifies the standard gradient descent by adding a velocity term to the update rule. This velocity term is a running average of past gradients, which helps the optimizer to continue moving in...

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