What is Stochastic Gradient Descent (SGD)?
by Stephen M. Walker II, CoFounder / CEO
What is Stochastic Gradient Descent (SGD)?
Stochastic Gradient Descent (SGD) is like a smart shortcut for machine learning algorithms to find the best settings quickly. Instead of checking every possible option, it randomly samples a few and uses them to improve step by step.
SGD is an iterative optimization algorithm widely used in machine learning and deep learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs.
It is a variant of the gradient descent algorithm, but instead of performing computations on the entire dataset, SGD calculates the gradient using just a random small part of the observations, or a "minibatch".
This approach can significantly reduce computation time, especially when dealing with large datasets.
Stochastic Gradient Descent (SGD) follows these steps:
 Initialize model parameters randomly
 Set the iteration count and learning rate
 Shuffle the dataset for randomness
 For each training example or minibatch:
 Compute the gradient of the loss function
 Adjust parameters in the gradient's negative direction, scaled by the learning rate
 Repeat step 4 until convergence or the iteration limit is reached
SGD's stochastic approach helps it escape local minima and explore the parameter space more effectively. It's also suitable for online learning, adapting to new data as it arrives. However, SGD often requires more iterations to converge than traditional Gradient Descent.
SGD is particularly dominant in neural network training applications when combined with backpropagation. It finds applications in various domains and use cases, including image and speech recognition, natural language processing, recommendation systems, financial modeling and prediction, and fraud detection.
What are the advantages and disadvantages of stochastic gradient descent?
Stochastic Gradient Descent (SGD) has several advantages and disadvantages that make it suitable for certain applications and less ideal for others.
Advantages of Stochastic Gradient Descent

Computational Efficiency — SGD is computationally efficient, especially when dealing with large datasets. It performs updates more frequently, which can lead to faster convergence than batch training.

Memory Efficiency — Since SGD updates the parameters for each training example one at a time, it is memoryefficient and can handle large datasets that cannot fit into memory.

Avoidance of Local Minima — Due to the noisy updates in SGD, it has the ability to escape from local minima and converges to a global minimum.

Frequent Updates — The frequent updates allow us to have a pretty detailed rate of improvement.

Suitability for Online Learning — SGD is wellsuited for online learning scenarios where data arrives sequentially, allowing the model to adapt and update in realtime.
Disadvantages of Stochastic Gradient Descent

Noisy Updates — The updates in SGD are noisy and have a high variance, which can cause the error rate to jump around instead of slowly decreasing.

More Iterations Required — Even though it requires a higher number of iterations to reach the minima than typical Gradient Descent, it is still computationally much less expensive than typical Gradient Descent.

Potential for Nonoptimal Convergence — The convergence path of SGD is noisier than that of original gradient descent, which can result in a good value for model parameters but is not optimal.

Loss of Vectorization Benefits — SGD can lose the benefits of vectorization since it processes one example at a time.
What is the difference between stochastic gradient descent and minibatch gradient descent?
Stochastic Gradient Descent (SGD) and MiniBatch Gradient Descent are both iterative optimization algorithms used in machine learning and deep learning. They are variants of the Gradient Descent algorithm, but they differ in how they process data to compute the gradient of the cost function with respect to the model parameters.
In SGD, the gradient is computed and the model parameters are updated for each individual training example. This approach introduces a high level of noise into the optimization process, which can help the model escape from local optima. However, the convergence path of SGD can be noisy and it may require more iterations to reach the minima.
On the other hand, MiniBatch Gradient Descent is a compromise between SGD and Batch Gradient Descent (where the gradient is computed over the entire dataset). In MiniBatch Gradient Descent, the dataset is divided into small subsets or "minibatches". The gradient is computed and the model parameters are updated for each of these minibatches. This approach reduces the noise in the optimization process compared to SGD, and it can be more computationally efficient than Batch Gradient Descent, especially for large datasets.
The key difference between SGD and MiniBatch Gradient Descent lies in the number of training examples used to compute the gradient and update the model parameters in each iteration. SGD uses one example at a time, while MiniBatch Gradient Descent uses a small batch of examples. This difference impacts the noise in the optimization process, the computational efficiency, and the convergence path of the algorithms.
What are some common applications of stochastic gradient descent in machine learning?
Stochastic Gradient Descent (SGD) is a versatile algorithm essential for training various machine learning models. It is the standard method for optimizing artificial neural networks, particularly in deep learning, where it adjusts parameters using a random subset of data during each iteration. SGD is also instrumental in training linear support vector machines for classification and regression, and in estimating parameters for logistic regression, commonly applied to binary classification tasks.
Beyond these, SGD facilitates the training of graphical models representing complex probabilistic systems and is utilized in geophysics for Full Waveform Inversion to reconstruct medium properties from seismic waves. Its computational efficiency makes it ideal for largescale machine learning challenges where data is too extensive to fit in memory. Additionally, SGD's adaptability to online learning allows for realtime model updates with new data streams. The application of SGD is tailored to the specific needs and constraints of the problem being addressed.
Frequently Asked Questions about Stochastic Gradient Descent
What is the role of the learning rate in SGD?
The learning rate in SGD determines the size of the steps taken towards the minimum of the loss function. A proper learning rate helps ensure convergence, while a high learning rate can cause divergence and a very slow convergence can result in a long training process.
How does SGD avoid getting stuck in a local minimum?
SGD avoids local minima through its stochastic nature, which introduces noise into the parameter updates. This noise can help the algorithm to jump out of local minima and seek the global minimum.
What is the difference between SGD and minibatch gradient descent?
SGD updates the model's parameters using only one data point at a time, while minibatch gradient descent uses a subset of the training data, known as a minibatch, for updates. This makes minibatch gradient descent more stable and efficient compared to the high variance updates of SGD.
Can SGD be used for online learning?
Yes, SGD is wellsuited for online learning as it can update the model's parameters as new data points arrive, allowing the machine learning model to adapt in realtime.
What is the impact of the size of the training set on SGD?
The size of the training set can affect the number of iterations required for SGD to converge. Larger datasets may require more iterations, but SGD's ability to handle one data point at a time makes it scalable to large amounts of data.
How does SGD perform parameter updates?
SGD performs parameter updates by computing the gradient of the cost function with respect to the parameters for a single training example, and then adjusting the parameters in the opposite direction of the gradient.
What are some common applications of SGD in machine learning?
SGD is commonly used for training neural networks, linear regression, logistic regression, and natural language processing tasks. It is also applied in domains like image and speech recognition, recommendation systems, and more.
How do adaptive learning rate methods like Adam differ from SGD?
Adaptive learning rate methods, such as Adam, adjust the learning rate based on past gradients and squared gradients, which can lead to more stable convergence compared to SGD's constant learning rate.
What is the effect of a small learning rate on SGD?
A small learning rate can lead to slow convergence, meaning the algorithm will take smaller steps towards the minimum and may require a larger number of iterations to converge.
How can SGD be modified to improve its performance?
SGD can be improved by incorporating techniques like momentum or Nesterov momentum, which take into account the direction of previous updates, or by using a learning rate schedule to adjust the learning rate during training.