Solving Optimization Problems with Stochastic Gradient Descent

gradient ascent

Stochastic Gradient Descent (SGD) is a widely used optimization algorithm in machine learning and deep learning. It is especially popular for training large-scale models due to its efficiency and scalability. In this guide, we will take you through the basics of SGD, step by step, from scratch. Whether you’re a beginner in the field or just looking to refresh your knowledge, this article will provide you with a solid understanding of stochastic gradient descent.

What is Stochastic Gradient Descent?

Stochastic Gradient Descent is an optimization algorithm used to train machine learning models. It is a variant of the gradient descent algorithm that minimizes a cost function by iteratively adjusting the model’s parameters based on the gradient of the cost function evaluated on a subset of training examples called a mini-batch. Unlike the traditional gradient descent, which uses the entire training set for each iteration, SGD updates the parameters after processing each mini-batch, resulting in faster convergence and reduced computational requirements.

The Intuition Behind Stochastic Gradient Descent

To understand SGD better, let’s consider the analogy of climbing down a hill. Imagine you are standing at the top of a hill, and your goal is to reach the bottom. One way to achieve this is by taking small steps in the direction of the steepest slope at your current position. As you keep taking these small steps, you gradually descend down the hill until you reach the bottom.

This scenario is analogous to training a machine learning model using gradient descent. The hill represents the cost landscape, with the goal of minimizing the cost function. The steepest slope represents the gradient of the cost function, which indicates the direction of the fastest decrease in the cost. By iteratively updating the model’s parameters along the opposite direction of the cost gradient, we can reach the minimum of the cost function, thereby improving the model’s performance.

The Algorithm Steps

Now that we have some intuition behind SGD, let’s break down the algorithm into its step-by-step process:

  1. Initialize the model’s parameters (weights and biases) with random values.
  2. Split the training dataset into mini-batches.
  3. For each mini-batch:
    1. Compute the gradient of the cost function with respect to the model’s parameters using the mini-batch samples.
    2. Update the model’s parameters by taking a small step in the opposite direction of the computed gradient.
  4. Repeat steps 3-4 for a fixed number of iterations or until convergence criteria are met.

It’s important to note that each mini-batch is randomly sampled from the training dataset, hence the “stochastic” in SGD. This randomness adds noise to the gradient estimation, which can help the algorithm escape local minima and explore different regions of the cost landscape.

Learning Rate and Decaying Schedules

The learning rate is a hyperparameter that controls the step size in SGD. It determines how fast or slow the model’s parameters are updated at each iteration. Choosing an appropriate learning rate is crucial for the successful training of a model. A high learning rate can cause the algorithm to overshoot the optimal solution, while a low learning rate may result in slow convergence or getting stuck in suboptimal solutions.

Decaying schedules are often applied to the learning rate to gradually decrease its value over time. This allows the algorithm to take larger steps at the beginning when the model’s parameters are far from the optimum and smaller steps as it gets closer to convergence. Commonly used decaying schedules include step decay, exponential decay, and polynomial decay.

Variants of Stochastic Gradient Descent

Over the years, several variants of Stochastic Gradient Descent have been proposed to improve its performance and address its limitations. Some of the popular variants include:

  • Mini-Batch Gradient Descent: This variant performs updates based on a small subset of the training set, rather than a single training example (SGD) or the entire training set (Batch Gradient Descent).
  • Momentum: This variant adds a momentum term that accumulates gradients over previous iterations, allowing the algorithm to navigate through flat regions and accelerate convergence.
  • Adagrad: This variant adapts the learning rate individually for each parameter based on the historical gradient values, giving more weight to infrequently updated parameters.
  • RMSprop: This variant addresses the diminishing learning rate problem of Adagrad by accumulating a moving average of the squared gradients, resulting in a more stable learning process.
  • Adam: This variant combines the benefits of Momentum and RMSprop by incorporating both momentum and adaptive learning rates.

Common Challenges and Solutions

Training models using stochastic gradient descent can present its own set of challenges. Here are some common challenges and their potential solutions:

  • Vanishing or Exploding Gradients: In deep neural networks, gradients can become too small or too large, resulting in slow convergence or unstable training. Techniques like weight initialization, batch normalization, and gradient clipping can help mitigate these issues.
  • Overfitting: Stochastic gradient descent can be prone to overfitting, where the model performs well on the training data but fails to generalize to unseen data. Regularization techniques like L1, L2 regularization, and dropout can help prevent overfitting.
  • Lack of Convergence: Sometimes, SGD may not converge to an optimal solution due to factors like high learning rate, poor initialization, or noisy gradients. Adjusting the learning rate, initializing parameters wisely, and using techniques like learning rate schedules or early stopping can assist in achieving convergence.

Remember, training a machine learning model is an iterative process, and experimentation is key to finding the right hyperparameters and techniques for your specific problem.


In this beginner’s guide to stochastic gradient descent from scratch, we have covered the basics of SGD and its algorithmic steps. We also discussed the intuition behind SGD using the analogy of descending a hill and explored variants and common challenges. Remember, SGD is a powerful optimization algorithm widely used in machine learning, and understanding its working principles is essential for any aspiring data scientist or machine learning engineer.