Types of Optimizers in Deep Learning: Best Optimizers for Neural Networks in 2025

In deep learning, an optimizer you pick is like the engine of your model—it drives learning and determines how fast and how well your network performs. The right optimizer can cut training time and boost accuracy, while the wrong one can leave you stuck in place, wasting time and resources. So, how do you choose the best one for your needs? 

With so many options out there, you’re probably overwhelmed. Some optimizers speed up training, while others excel at generalization. The key is understanding the trade-offs. Choosing the wrong optimizer can slow you down or lead to subpar performance, so getting this right is crucial.

As we move into 2025,  staying updated on the latest optimizers is more important than ever. Whether you're building models for computer vision, NLP, or other AI tasks, the right optimizer is the secret to success. Ready to explore your best options? Let’s dive in.

What is An Optimizer In Deep Learning?

An optimizer in deep learning adjusts a neural network‘s weights to minimize the loss function. This process is crucial for the model to learn effectively. It involves making small, incremental changes to the parameters, but finding the right balance is tricky.

An optimizer in a neural network helps guide the model to the best solution. Without them, the model might struggle to converge or learn effectively. The challenge is that deep learning models are complex, and the optimization process can be slow and unstable without the right approach.

Different optimizers address specific pain points. Some are better at handling slow convergence, while others tackle issues like unstable gradients.

Understanding what an optimizer is sets the stage for exploring why it plays a crucial role in training neural networks effectively.

Why Are Optimizers Critical for Neural Network Training?

Optimizers are the driving force behind effective neural network training, ensuring models learn and improve over time. Here’s why they’re essential for achieving optimal performance: 

• Optimizers adjust the network’s weights, steering the model toward the best possible solution.
• They control the learning rate, determining how fast or slow the model learns.
• Different types of optimizers in deep learning apply unique methods for updating weights, which can influence training speed and model performance.
• They help avoid issues like overfitting by controlling the complexity of the model’s learning process.
• The right optimizer in neural networks helps in smooth convergence, reducing training time and improving overall model accuracy.

Also Read: Deep Learning: Dive into the World of Machine Learning!

Common Types of Optimizers in Deep Learning

Deep learning models use optimizers to minimize loss and enhance performance. Understanding the key types of optimizers in deep learning helps you choose the right one for your project. Each optimizer has strengths and weaknesses, depending on factors like data size, model complexity, and training time.

Below is a table that outlines the pros and cons of each optimizer. This will help you evaluate which one might work best for your neural network.

Let’s have a deeper look at each of these one by one now to help you understand them better.

Understanding Each Optimizer

Optimizers play a crucial role in training deep learning models by adjusting weights to minimize errors. Let’s break down the different optimizers and their unique characteristics.

Gradient Descent (GD)

Gradient Descent is the simplest optimization method 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: Small-scale models with well-behaved loss functions.

Stochastic Gradient Descent (SGD)

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: Large datasets with noisy updates.

Mini-Batch Gradient Descent

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: Large datasets where full gradient descent is too slow.

SGD with Momentum

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: Tasks requiring fast convergence and stabilization of noisy gradients.

AdaGrad (Adaptive Gradient Descent)

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).

RMSProp (Root Mean Square Propagation)

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.

AdaDelta

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.

Adam (Adaptive Moment Estimation)

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.

Nesterov Accelerated Gradient (NAG)

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.

Stochastic Gradient Descent with Gradient Clipping

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.

Momentum

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.

Nesterov Momentum

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.

Adamax

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.

SMORMS3

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.

By understanding these optimizers and their strengths, you can make informed choices for your neural network training. Let’s now explore each optimizer in detail.

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

Choosing the Best Optimizer for Your Neural Network

The right optimizer can be the difference between a neural network that trains efficiently and one that gets stuck in a loop of slow convergence. With so many options available, how do you know which one will work best for your project? The decision depends on several key factors such as your dataset size, model complexity, and the computational resources at your disposal. 

Let’s break down the essential considerations and guide you toward making the best choice for your model.

Key Considerations When Choosing an Optimizer In Neural Network

When selecting an optimizer, consider factors like model complexity, data characteristics, and convergence speed. Each optimizer has strengths that suit specific types of models and training conditions.

• Dataset Size and Complexity: Larger datasets often require optimizers like Adam or SGD with Momentum to handle noisy gradients and improve convergence speed. Smaller datasets might benefit from simpler optimizers.
• Model Architecture and Type: Complex models, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), often require optimizers like Adam or RMSProp, which adapt the learning rate during training.
• Computational Cost: More advanced optimizers, such as Adam or AdaGrad, may require more computational resources. If you’re working with limited hardware, consider simpler options like SGD or Momentum.

Choosing Optimizers for Specific Deep Learning Tasks

Different tasks require different optimization strategies. For example, Adam is great for general use, while SGD might perform better in tasks requiring a more stable and controlled learning rate.

• Large Models (e.g., CNNs, RNNs): Adam, AdaGrad, and RMSProp are generally preferred for large models due to their ability to adapt the learning rate.
• Image Recognition: Adam and SGD with Momentum are highly effective for tasks like image classification due to their faster convergence.
• Natural Language Processing (NLP): Adam is often the go-to optimizer for NLP models due to its stability and efficiency.
• Time Series Forecasting: For sequential data, RMSProp and Adam work well because they handle gradients effectively over long sequences.

Practical Tips for Optimizer Tuning

Optimizer tuning involves adjusting parameters such as the learning rate and momentum. Experimentation and cross-validation are key to finding the optimal configuration for your model.

• Learning Rate Adjustment: Tuning the learning rate is crucial for most optimizers. For example, Adam and RMSProp typically work well with a learning rate in the range of 0.001–0.0001.
• Hyperparameter Tuning: Each optimizer has hyperparameters like momentum, beta values, and epsilon. Adjusting these values carefully through cross-validation can significantly improve model performance.

Choosing the right optimizer is key to improving your model's efficiency and results. Let’s now look at practical tips for fine-tuning optimizers for your specific project.

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

Best Practices for Optimizer Usage in Neural Networks

Optimizers play a pivotal role in training your neural network, and using them effectively can drastically improve performance and reduce training time. Here are some best practices to ensure your optimizer works at its full potential:

How to Tune Learning Rates for Different Optimizers?

Tuning the learning rate involves trial and error, often starting with a higher rate and gradually decreasing it. Learning rate schedules or learning rate annealing can help improve optimization performance.

• Start Small: Begin with a small learning rate (e.g., 0.001) for most optimizers like Adam or RMSProp. Gradually increase or decrease it based on model performance.
• Learning Rate Scheduling: Use learning rate decay or a scheduler to adjust the rate during training. Exponential decay or step decay is common for long training processes.
• Adaptive Learning Rates: With Adam and AdaGrad, the optimizer adjusts the learning rate during training, which can help converge faster without needing manual adjustments.

How to Handle the Vanishing and Exploding Gradient Problems?

To mitigate vanishing and exploding gradients, use techniques like gradient clipping, proper initialization, or activations like ReLU. These strategies help stabilize learning in deep networks.

• Use Gradient Clipping: In cases where gradients grow too large (exploding gradients), implement gradient clipping. This limits the gradient values to a pre-defined range and prevents instability.
• Choose the Right Optimizer: Optimizers like RMSProp and Adam are better suited for handling vanishing gradients because they adapt the learning rate to stabilize the training process.
• Weight Initialization: Proper initialization of weights can prevent both vanishing and exploding gradients. Methods like Xavier initialization or He initialization are widely used.

How to Incorporate Early Stopping with Optimizers?

Early stopping prevents overfitting by halting training when the model’s performance stops improving on a validation set. Combine it with optimizers to save time and prevent unnecessary computation.

• Monitor Validation Loss: Early stopping monitors validation loss during training. If the loss does not improve after a set number of epochs, the training stops.
• Optimizer-Specific Considerations: When using optimizers like Adam, it’s essential to ensure that the learning rate doesn’t overfit the data. Early stopping can help avoid overfitting by halting training before the model starts to memorize patterns in the training data.
• Use in Conjunction with Learning Rate Schedulers: Combine early stopping with learning rate schedulers to fine-tune your training and prevent wasting computational resources.

By following these best practices, you can make the most of your optimizer and train more efficient neural networks.

But before you decide, let's wrap up with some insights on how upGrad can elevate your deep learning journey.

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

upGrad Courses and Certifications for Deep Learning

upGrad offers top-notch AI & Deep Learning certifications designed to help you master the skills needed in today’s competitive AI field. With expert mentorship, hands-on projects, and a flexible learning model, you’ll gain practical experience and in-depth knowledge in deep learning.

The courses are designed by industry experts, ensuring you stay ahead in the fast-evolving tech landscape. Whether you’re starting or upskilling, upGrad provides the tools to build a strong foundation and advance your career in AI with courses such as:

•  AI & Deep Learning Certification - upGrad
• Post Graduate Certificate in Machine Learning & NLP (Executive)
• LL.M. in AI and Emerging Technologies (Blended Learning Program)

Want to get started? Contact our career counselors to choose the best program for you, or explore upGrad’s free courses to learn more!

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