Top 10+ Optimizers in Deep Learning for Neural Networks in 2025

The best optimizers in deep learning in 2025 are those that accelerate convergence, enhance model accuracy, and improve generalization. Optimizers like Adam, RMSprop, and newer options such as sigSignAdamW are designed to minimize training time, tackle issues like vanishing gradients, and prevent oscillations. 

Each optimizer is designed to adapt learning rates, manage momentum, and mitigate overfitting, making them crucial for optimizing deep learning models.

In this blog, we’ll explore the top deep learning optimizers for 2025, their key features, ideal use cases, and best practices for selection.

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Top Optimizers in Deep Learning for Neural Networks in 2025: Definition and Types

In deep learning, an optimizer is a crucial algorithm used to minimize the loss function by adjusting the weights of a neural network. Its goal is to help the model learn by iteratively reducing the error between predicted and actual values. 

Optimizers, such as stochastic gradient descent (SGD) and its variants, provide different methods for adjusting learning rates and overcoming challenges associated with local minima.

Why Are Optimizers Important?

Optimizers in deep learning play a strong role in training neural networks. The right choice can significantly enhance model performance and speed up convergence, while a poor one may hinder the process and lead to suboptimal results. 

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Since neural networks often have millions of parameters, optimizers are essential for efficiently managing this complexity and guiding the model's learning trajectory.

To understand the significance of optimizers in deep learning more clearly, consider the following table:

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Each optimizer has its own unique strengths and is suited for different types of models and tasks. Here are the most popular ones in 2025:

1. Gradient Descent (GD)

Source: EDUCBA

Gradient Descent is the simplest optimization method that aims to minimize the loss function by taking steps proportional to the negative gradient.

• How Does Gradient Descent Work?: It updates parameters by moving against the gradient of the loss function.
• Key Components:
  • Learning Rate: Controls the step size for each update.
  • Momentum: N/A
  • Beta Parameters: N/A
  • Gradient Clipping: N/A
• Formula:
  θ=θ−α∇θ​J​J(θ)
• Popular Use Cases: Suitable for simple models or convex optimization problems with smooth, well-behaved loss functions.

2. Stochastic Gradient Descent (SGD)

Source: Medium

SGD updates the model parameters based on a single data point, offering faster convergence but more variance. It’s widely used in many machine learning tasks for quicker results.

• How Does SGD Work?: Parameters are updated after processing each data point.
• Key Components:
  • Learning Rate: Determines the magnitude of updates.
  • Momentum: Optional, used to speed up convergence.
  • Beta Parameters: N/A
  • Gradient Clipping: N/A
• Formula:
  θ=θ−α∇θ​​J(θ)
• Popular Use Cases: Ideal for large-scale datasets and tasks, such as training deep neural networks, particularly when computational resources are limited and real-time updates are required (e.g., in online learning).

3. Mini-Batch Gradient Descent

Source: Medium

Mini-Batch Gradient Descent strikes a balance by updating parameters using a small batch of data points at a time. This speeds up the training while reducing variance compared to pure SGD.

• How Does Mini-Batch GD Work?: The model is updated after each mini-batch instead of after each training example.
• Key Components:
  • Learning Rate: Adjusts the speed of updates.
  • Momentum: Optional.
  • Beta Parameters: N/A
  • Gradient Clipping: N/A
• Formula:

θ=θ−am​i=1m​∇θ​J(θ,x(i),y(i))

• Popular Use Cases: Commonly used in training deep learning models, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), where large datasets require efficient and stable convergence.

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4. SGD with Momentum

Source: Papers with Code

SGD with Momentum adds a momentum term to the gradient, helping the optimizer avoid local minima and speed up convergence. It’s useful when the loss function has steep or shallow regions.

• How Does SGD with Momentum Work?: The optimizer uses the momentum term to smooth the update direction.
• Key Components:
  • Learning Rate: Controls the step size.
  • Momentum: Helps accelerate gradients along the correct direction.
  • Beta Parameters: N/A
  • Gradient Clipping: N/A
• Formula:
  v=βv+(1−β)∇θ​​J(θ)
• Popular Use Cases: Frequently used in training complex neural networks with noisy gradients, like in image classification or natural language processing (NLP), where faster convergence and stabilization are critical.

5. AdaGrad (Adaptive Gradient Descent)

Source: Dmitrijis Kass’ Blog

AdaGrad adjusts the learning rate for each parameter based on its historical gradient, making it especially useful for sparse data or features. It adapts the learning rate to the geometry of the data.

• How Does AdaGrad Work?: It accumulates the squared gradients and scales the learning rate inversely proportional to this sum. The more updates a parameter receives, the smaller the learning rate becomes.
• Key Components of AdaGrad
  • Learning Rate: Adaptive based on the squared gradients.
  • Momentum: Not used in AdaGrad.
  • Beta Parameters: N/A
  • Gradient Clipping: Not applicable.
• Formula: Learning Rate for each parameter: nGt+. Where Gt is the sum of the squared gradients up to time step t.
• Popular Use Cases: AdaGrad is ideal for problems with sparse data like text classification or natural language processing (NLP).

Also Read: Top Differences Between ML, Deep Learning, And NLP

6. RMSProp (Root Mean Square Propagation)

Source: Built In

RMSProp modifies AdaGrad by introducing a moving average of squared gradients, which stabilizes the learning rate. It is effective in training deep networks where AdaGrad might fail.

• How Does RMSProp Work?: RMSProp divides the learning rate by an exponentially decaying average of squared gradients, enabling better convergence on non-stationary objectives.
• Key Components of RMSProp
  • Learning Rate: Adaptive based on the moving average of squared gradients.
  • Momentum: Yes, it can be used optionally.
  • Beta Parameters: Decay rate β\betaβ (default 0.9).
  • Gradient Clipping: This can be applied to control the gradient explosion.
• Formula: vt​=βvt-1​+(1−β)gt2​
                     t​=t-1−nvt+​gt
• Popular Use Cases: RMSProp is preferred for training RNNs, particularly when training with sequences and time-series data.

7. AdaDelta

Source: velog

AdaDelta is an extension of AdaGrad that addresses the problem of a rapidly decreasing learning rate. It dynamically adapts based on a moving window of past gradients.

• How Does AdaDelta Work?: Unlike AdaGrad, AdaDelta does not accumulate all past gradients. Instead, it uses a decaying average of the past gradients, which prevents the learning rate from becoming too small.
• Key Components of AdaDelta
  • Learning Rate: Adaptively scaled based on the moving average of gradients.
  • Momentum: Not used in AdaDelta.
  • Beta Parameters: ρ\rhoρ (decay rate).
  • Gradient Clipping: Not applicable.
• Formula: Update rule: t​=t-1−gt[g2]t-1+​​
• Popular Use Cases: AdaDelta works well for tasks that require training on large datasets with noisy gradients or sparse data, like reinforcement learning.

8. Adam (Adaptive Moment Estimation)

Source: SlideTeam

Adam combines the benefits of both AdaGrad and RMSProp by maintaining two-moment estimates—the first moment (mean) and the second moment (uncentered variance). It is the most widely used optimizer for deep learning tasks.

• How Does Adam Work?: Adam computes adaptive learning rates for each parameter by using estimates of first and second moments (mean and variance of gradients).
• Key Components of Adam
  • Learning Rate: Adaptive for each parameter.
  • Momentum: Yes, uses the first moment (mean).
  • Beta Parameters: β1\beta_1β1​ (decay rate for first moment) and β2\beta_2β2​ (decay rate for second moment).
  • Gradient Clipping: This can be used to handle exploding gradients.
• Formula: ​mt=1​mt-1+(1−1)gt​ 
                    vt​=2​vt-1+(1−2)gt2​ 
• Popular Use Cases: Adam is widely used in training deep neural networks, including CNNs and RNNs, especially when the dataset is large or noisy.

Also Read: 52+ Must-Know Machine Learning Viva Questions and Interview Questions for 2025

9. Nesterov Accelerated Gradient (NAG)

Source: Naukri.com

NAG improves the momentum technique by adjusting the gradients with a look-ahead approach. It often leads to faster convergence and is preferred when optimizing non-convex problems.

• How Does NAG Work?: It first computes the gradient with momentum at the "lookahead" position, then corrects the parameters based on that information.
• Key Components of NAG
  • Learning Rate: Fixed or adaptive, based on the task.
  • Momentum: Yes, used with a lookahead step.
  • Beta Parameters: β\betaβ (momentum term).
  • Gradient Clipping: Not applicable.
• Formula: Update: vt​​=vt-1​+ηg(t-1-vt-1​​)
• Popular Use Cases: NAG is useful in optimization tasks where momentum can help accelerate convergence, particularly in tasks requiring precision, like NLP and image recognition.

10. Stochastic Gradient Descent with Gradient Clipping

Source: Spot Intelligence

This method extends SGD by adding gradient clipping to prevent exploding gradients, making it more stable during training.

• How Does It Work?: It limits the gradient values to a predefined threshold to avoid large updates, which can destabilize the model.
• Key Components of SGD with Gradient Clipping
  • Learning Rate: Fixed or decaying.
  • Momentum: Not typically used.
  • Beta Parameters: N/A
  • Gradient Clipping: Yes, it is used to avoid large gradient updates.
• Formula: Clip gradients: gt​=clip(gt​,threshold)
• Popular Use Cases: This method is used in models where gradients can become excessively large, such as deep neural networks or RNNs.

11. Momentum

Source: Data Science Stack Exchange

Momentum helps accelerate the gradient descent process by adding a fraction of the previous update to the current one, reducing oscillations and speeding up convergence.

• How Does Momentum Work?: It stores a velocity term that accumulates gradients over time, which helps to push the parameters toward the minimum more smoothly.
• Key Components of Momentum
  • Learning Rate: Constant or adaptive.
  • Momentum: Yes, used to accumulate past gradients.
  • Beta Parameters: N/A
  • Gradient Clipping: Not usually applied.
• Formula: vt​=vt-1​+ηgt​
• Popular Use Cases: Momentum is ideal for problems with complex loss surfaces, like training deep networks or models with many local minima.

Also Read: Top 10 Highest Paying Machine Learning Jobs in India [A Complete Report]

12. Nesterov Momentum

Source: Research Gate

Nesterov Momentum improves standard momentum by calculating gradients at the "lookahead" point. It often leads to better performance and faster convergence than traditional momentum.

• How Does Nesterov Momentum Work?: It calculates the gradient after taking a "lookahead" step, resulting in more efficient and stable updates.
• Key Components of Nesterov Momentum
  • Learning Rate: Fixed or decaying.
  • Momentum: Yes, with lookahead steps.
  • Beta Parameters: N/A
  • Gradient Clipping: Not typically used.
• Formula:  vt​=vt-1​+ηgt​(t-1−vt-1​)
• Popular Use Cases: Used in deep networks, particularly those with complex optimization landscapes, like CNNs and RNNs.

13. Adamax

Source: Research Gate

Adamax is a variant of Adam designed to handle large parameter spaces. It uses the infinity norm to scale the updates, providing better stability in some models.

• How Does Adamax Work?: It applies the infinity norm instead of the L2 norm to the gradient updates, which can be more effective when gradients are sparse.
• Key Components of Adamax
  • Learning Rate: Adaptive, similar to Adam.
  • Momentum: Yes, first and second-moment estimates.
  • Beta Parameters: β1\beta_1β1​ and β2\beta_2β2​.
  • Gradient Clipping: This can be used optionally.
• Formula: mt=1mt-1+(1-1)gt
• Popular Use Cases: Used in scenarios where sparse gradients occur, often in NLP and reinforcement learning.

14. SMORMS3

Source: Research Gate

SMORMS3 is a lesser-known optimizer that adapts the learning rate to the magnitude of gradients using a modified version of the Adam optimizer. It’s known for being robust in certain settings.

• How Does SMORMS3 Work?: It adjusts the learning rate in a way that avoids over-accumulation of past gradients and offers more stability than methods like AdaGrad, especially in cases with highly irregular gradients.
• Key Components of SMORMS3
  • Learning Rate: Adaptive, with a smaller rate for frequently updated parameters.
  • Momentum: Not used in SMORMS3.
  • Beta Parameters: N/A
  • Gradient Clipping: Yes, to prevent gradient explosions.
• Formula: t=t-1-nmt+gt
• Popular Use Cases: SMORMS3 is particularly useful for sparse data tasks, such as training models on natural language processing (NLP) or reinforcement learning, where gradients are often sparse or noisy.

Also Read: Evolution of Language Modelling in Modern Life

The following table provides a detailed overview of the pros and cons of each optimizer, helping you assess which one might be best suited for optimizing your neural network: 

Read More: Deep Learning Algorithm [Comprehensive Guide With Examples]

Now, let's explore how to choose and fine-tune optimizers in deep learning to optimize your neural network for better performance.

Choosing and Fine-Tuning Optimizers in Deep Learning for Your Neural Network

Selecting the right optimizer in deep learning is crucial for efficient training and optimal performance. The right choice ensures faster convergence, better generalization, and stability, while the wrong one can slow learning, lead to local minima, or hurt model performance. 

The optimal optimizer depends on factors like dataset size, model complexity, and available resources. Below, we explore how to choose the best optimizer for your task and fine-tune it for maximum efficiency.

How to Choose the Right Optimizers in Deep Learning 

When choosing an optimizer, it's essential to consider the nature of your dataset, the complexity of your model, and your computational constraints:

1. Dataset Size and Model Complexity

For large datasets, optimizers like Adam or SGD with Momentum are ideal as they efficiently handle noisy gradients and provide faster convergence. Simpler models with smaller datasets may work well with optimizers like SGD, which are less resource-intensive. 

Complex models, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), benefit from optimizers like Adam or RMSProp, which adapt the learning rate during training.

2. Task Type

For tasks such as image recognition, Adam and SGD with Momentum excel due to their ability to converge quickly and handle complex gradients. NLP models often utilize Adam due to its stability and efficiency in handling noisy data and managing long-term dependencies. 

For time series forecasting, RMSProp and Adam are effective, as they manage gradients efficiently over long sequences.

Also Read: 15+ Top Natural Language Processing Techniques To Learn in 2025

3. Computational Resources 

More advanced optimizers, such as Adam or AdaGrad, require more computational power. If you're limited in resources, consider simpler alternatives like SGD or Momentum, which are less resource-intensive and yield decent results for smaller tasks or models.

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Best Practices for Optimizer Usage

To get the best out of your chosen optimizer, here are a few best practices for fine-tuning and using optimizers effectively in your deep learning models:

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Also Read: Deep Learning vs Neural Networks: Difference Between Deep Learning and Neural Networks

How Can upGrad Help You in Your ML and Neural Networks Journey?

The top optimizers in deep learning for 2025, including Adam, SGD with Momentum, RMSProp, and AdaGrad, are crucial for faster convergence, improved generalization, and stable training.

These optimizers address challenges such as vanishing gradients and noisy data, making them essential for tasks like image recognition and NLP.  Master them by experimenting with different types and fine-tuning hyperparameters, such as learning rate and momentum, on real-world datasets.

Many learners struggle to select and tune the right optimizer due to the complexity of deep learning models. upGrad’s programs offer a structured approach to mastering machine learning techniques, with expert guidance and hands-on practice.

Some additional courses include: 

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Understanding optimizers can be confusing without practical context. upGrad supports this with expert guidance and offline centers for hands-on learning, helping you build real-world machine learning skills.

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Reference:
https://arxiv.org/abs/2408.11839