Batch Gradient Descent In Machine Learning Made Simple & How To Tutorial In Python

by | May 15, 2024 | Data Science, Machine Learning

What is Batch Gradient Descent?

Batch gradient descent is a fundamental optimization algorithm in machine learning and numerical optimisation tasks. It is a variation of the gradient descent algorithm, which aims to minimise a given cost or loss function by iteratively adjusting a model’s parameters.

In batch gradient descent, the entire dataset is utilised to compute the gradient of the cost function concerning the model parameters in each iteration. This means the algorithm simultaneously considers all the training examples for every update step.

The general steps are:

  1. Initialise the model parameters randomly or with some predetermined values.
  2. Compute the gradient of the cost function for each parameter using the entire dataset.
  3. Update the parameters in the opposite direction of the gradient to minimise the cost function.
  4. Repeat steps 2 and 3 until convergence or a stopping criterion is met.

The algorithm guarantees convergence to the global minimum cost function under certain conditions, such as convexity and smoothness. However, it can be computationally expensive, especially for large datasets, as it requires processing the entire dataset in each iteration. Additionally, it may suffer from slow convergence when dealing with noisy or ill-conditioned optimisation problems.

Despite its drawbacks, batch gradient descent is widely used, particularly when computational resources permit or when convergence speed is prioritised over efficiency. Moreover, it serves as the basis for many advanced optimisation algorithms and is a fundamental concept in machine learning.

Understanding Batch Gradient Descent

In this section, we delve into the core concepts behind batch gradient descent, elucidating its principles and distinguishing features.

Fundamentals of Gradient Descent

Gradient descent is an iterative optimization algorithm that minimises a given cost or loss function. It iteratively updates model parameters in the direction that reduces the cost function, ultimately converging towards the optimal solution.

The Role of Gradients

Gradients indicate the direction of the steepest ascent in the parameter space. In optimization, the negative gradient points towards the direction of the steepest descent, guiding parameter updates. By following the negative gradient direction, the algorithm seeks to move towards the minimum of the cost function, achieving convergence to an optimal solution.


Illustration of batch gradient descent

In batch gradient descent, the entire dataset is utilised to compute the gradient of the cost function with respect to the model parameters.

Unlike stochastic gradient descent (SGD), which computes gradients using only one training example per iteration, batch gradient descent considers all training examples simultaneously.

Batch gradient descent ensures deterministic updates since each iteration’s parameter update is based on the complete dataset.

Comparison with Other Gradient Descent Variants

Contrast with stochastic gradient descent (SGD)

While batch gradient descent processes the entire dataset, SGD updates parameters using a randomly selected training example per iteration.

Batch gradient descent offers stable updates and convergence guarantees but can be computationally intensive, especially for large datasets. Understanding the trade-offs between batch, stochastic, and mini-batch gradient descent is crucial for selecting the appropriate optimisation approach.

Mechanics of the Batch Gradient Descent Algorithm

Batch gradient descent operates on iteratively updating model parameters to minimise a given cost or loss function. In this section, we delve into the inner workings, elucidating its step-by-step mechanics and mathematical underpinnings.

Mathematical Formulation

Cost Function

  • Let J(θ) denote the cost function, quantifying the discrepancy between the model predictions and the target values.
  • The cost function J(θ) measures the model’s overall error for a given set of parameters θ.

Model Parameters

  • The model’s parameters are represented by the vector θ, which we aim to optimize to minimize the cost function.
  • The vector θ contains the model’s weights and biases, which define the relationship between the input features and the predicted output.

Gradient Computation

Calculate the gradient of the cost function J(θ) concerning the model parameters θ.

Unlike stochastic gradient descent, batch gradient descent simultaneously computes the gradient using all training examples.

Express the gradient computation as

The equation of batch gradient descent

where m is the number of training examples.

Parameter Updates

Update the model parameters θ in the opposite direction of the gradient to minimise the cost function.

Multiply the gradient by a predefined learning rate α to control the size of the parameter updates.

Update the parameters using θ:=θα⋅∇J(θ).

Iterative Optimization

  1. Initialise the model parameters θ with random or predefined values.
  2. Iterate through the dataset, computing gradients and updating parameters until convergence or a stopping criterion is met.
  3. Monitor the change in the cost function or parameter values to determine convergence.

Convergence Analysis

Batch gradient descent guarantees convergence to a local minimum under certain conditions, such as the cost function’s convexity and smoothness.

This stochasticity imbues SGD with the ability to traverse the optimization landscape more dynamically, potentially avoiding local minima and converging to better solutions.

The convergence rate may vary depending on factors such as the learning rate and the curvature of the cost function.

Understanding the mechanics provides insights into its behaviour and performance characteristics. By leveraging the above principles, we can apply it to optimise various machine learning models and achieve superior performance on diverse tasks.

How To Implement Batch Gradient Descent In Python

Below is the Python code for the batch gradient descent algorithm with a simple linear regression example for demonstration purposes.

import numpy as np
import matplotlib.pyplot as plt

# Generate sample dataset
X_data = 2 * np.random.rand(100, 1)  # Generate 100 random samples between 0 and 2
y_data = 4 + 3 * X_data + np.random.randn(100, 1)  # Linear relationship with some noise

# Define the cost function (mean squared error)
def cost_function(theta0, theta1, X, y):
    m = len(y)
    h =[theta0, theta1]))
    J = (1 / (2 * m)) * np.sum((h - y) ** 2)
    return J

# Batch gradient descent implementation
def batch_gradient_descent(X, y, alpha, max_iterations, epsilon):
    theta = np.zeros((2, 1))  # Initialize parameters
    m = len(y)
    cost_history = []

    for iteration in range(max_iterations):
        h =  # Compute predictions
        error = h - y      # Compute error
        gradient = (1 / m) *  # Compute gradient
        theta -= alpha * gradient  # Update parameters
        cost = cost_function(theta[0], theta[1], X, y)  # Compute cost
        cost_history.append(cost)  # Store cost for visualization
        # Check for convergence
        if len(cost_history) > 1 and abs(cost - cost_history[-2]) < epsilon:

    return theta, cost_history

# Add column of ones to X for the bias term
X_data_bias = np.c_[np.ones((len(X_data), 1)), X_data]

# Set hyperparameters
alpha = 0.01
max_iterations = 1000
epsilon = 0.0001

# Run batch gradient descent
theta_optimal, cost_history = batch_gradient_descent(X_data_bias, y_data, alpha, max_iterations, epsilon)

# Visualize cost function convergence
plt.title('Cost Function Convergence')

# Visualize the data and the fitted line
plt.scatter(X_data, y_data, label='Data')
plt.plot(X_data,, color='red', label='Fitted line')
plt.title('Linear Regression with Batch Gradient Descent')
The cost function convergence in batch gradient descent
  • X is the feature matrix (with examples in rows and features in columns)
  • y is the target vector
  • alpha is the learning rate
  • max_iterations is the maximum number of iterations allowed
  • epsilon is the convergence criterion, a small value indicating the acceptable change in cost to consider convergence
  • theta is the parameter vector
  • m is the number of training examples
  • h is the vector of predictions
  • error is the vector of prediction errors
  • cost is the current value of the cost function
  • gradient is the vector of parameter gradients

The algorithm iteratively computes predictions, errors, and the cost function. Then, it calculates the gradient of the cost function with respect to the parameters. Parameters are updated in the opposite direction of the gradient scaled by the learning rate alpha. The process continues until either the maximum number of iterations is reached or the change in cost falls below the convergence criterion epsilon. Finally, the optimised parameter vector theta is returned.

This algorithm demonstrates the basic mechanics, where the entire dataset is used to compute the gradient and update parameters in each iteration.

Advantages and Disadvantages of Batch Gradient Descent

Batch gradient descent is a robust algorithm for machine learning and numerical optimisation. Like any other method, it has advantages and disadvantages. In this section, we explore its strengths and weaknesses.

Advantages of Batch Gradient Descent

  1. Convergence to Global Minimum:
    • It guarantees convergence to the global minimum cost function under certain conditions, such as convexity and smoothness.
    • By considering the entire dataset in each iteration, it ensures a more accurate gradient estimation, leading to convergence to the optimal solution.
  2. Stable Updates:
    • Since it computes gradients using the entire dataset, parameter updates are more stable and less sensitive to noise in the training data.
    • Stable updates result in smoother convergence trajectories, facilitating easier monitoring of the optimisation process.
  3. Guaranteed Improvement:
    • It guarantees a reduction in the value of the cost function with each iteration, ensuring steady progress towards the optimal solution.
    • This property makes it suitable for optimisation tasks where consistent improvement is desired.

Disadvantages of Batch Gradient Descent

  1. High Computational Cost:
    • It requires processing the entire dataset in each iteration, making it computationally expensive, especially for large datasets.
    • The need to store and compute gradients for the entire dataset can strain computational resources and limit scalability.
  2. Memory Requirements:
    • Storing the entire dataset in memory for gradient computation can pose memory constraints, particularly for large datasets too large to fit in memory.
    • Memory limitations may necessitate alternative optimisation algorithms or data preprocessing techniques to handle large-scale datasets effectively.
  3. Susceptibility to Local Minima:
    • It is susceptible to getting trapped in local minima, especially in non-convex optimisation problems.
    • In complex cost landscapes, it may struggle to escape shallow local minima, hindering its ability to find the globally optimal solution.

Understanding the trade-offs between its advantages and disadvantages is crucial for effectively applying this in practical machine learning tasks. While it offers guaranteed convergence and stable updates, careful consideration of computational constraints and optimisation landscape characteristics is essential for successful utilisation.

What is the Difference Between Mini-batch Gradient Descent and Full-batch Gradient Descent?

The main difference between mini-batch gradient descent and full-batch gradient descent lies in the amount of data used to compute the gradient and update the model parameters in each iteration.

Full-Batch Gradient Descent

In full-batch gradient descent, the entire dataset is used to compute the gradient of the cost function with respect to the model parameters.

The gradient is computed by summing the gradients of each data point in the dataset, and then the model parameters are updated once using this aggregated gradient.

This algorithm provides a more accurate estimate of the true gradient. Still, it can be computationally expensive, especially for large datasets, as it requires processing the entire dataset in each iteration.

Mini-Batch Gradient Descent

In mini-batch gradient descent, the dataset is divided into smaller subsets called mini-batches.

During each iteration, one mini-batch (a subset of the data) is randomly sampled from the dataset, and the cost function gradient is computed using only the examples in that mini-batch.

The model parameters are then updated based on this mini-batch gradient.

Mini-batch gradient descent compromises full-batch gradient descent and stochastic gradient descent (SGD). It balances the accuracy of the gradient estimation and the computational efficiency by using a subset of the data for each iteration.

The critical differences between mini-batch gradient descent and full-batch gradient descent are the dataset size used for gradient computation and the corresponding parameter updates. Full-batch gradient descent processes the entire dataset in each iteration, while mini-batch gradient descent operates on smaller subsets (mini-batches) of the data.

Practical Implementation Tips for Batch Gradient Descent

Effective implementation requires attention to various factors, including data preprocessing, hyperparameter tuning, and optimisation strategies. This section provides practical tips to enhance its implementation in machine learning.

Data Preprocessing Techniques

  • Feature Scaling: Normalise or standardise input features to ensure that they have similar scales. This facilitates faster convergence and prevents numerical instability.
  • Handling Missing Values: Address missing values in the dataset through techniques such as imputation or deletion to avoid bias in gradient computations.
  • Feature Engineering: Explore feature transformations and the creation of new features to improve the model’s representational power and aid optimization.

Hyperparameter Tuning

  • Learning Rate Selection: Experiment with different learning rates to find an appropriate value that balances convergence speed and stability.
Illustration of different learning rates in machine learning
  • Regularisation: Incorporate regularisation techniques such as L1 or L2 regularisation to prevent overfitting and improve generalisation performance.
  • Batch Size: Adjust the batch size based on computational resources and dataset characteristics. Larger batch sizes may lead to smoother convergence but require more memory.
  • Utilise learning rate schedules or adaptive learning rate methods to dynamically adjust the learning rate during training.

Optimisation Strategies for Batch Gradient Descent

  • Early Stopping: Monitor the validation loss during training and halt the optimisation process when the loss no longer improves to prevent overfitting.
  • Momentum: Incorporate momentum to accelerate convergence and mitigate oscillations in parameter updates. Experiment with different momentum values to find an optimal setting for the specific task.
  • Initialisation: Initialize model parameters carefully to avoid vanishing or exploding gradients, which can hinder convergence.
Illustration with and without learning rate reduced

Monitoring and Debugging Batch Gradient Descent

  • Training Visualisation: Visualise training metrics such as loss and parameter trajectories to gain insights into optimisation.
  • Gradient Checking: Validate gradient computations using numerical gradient checking to ensure the correctness of the implementation and prevent gradient-related errors.
  • Plot learning curves to monitor convergence and diagnose overfitting or underfitting.

Computational Efficiency

  • Parallelisation: Leverage parallel computing frameworks or distributed training techniques to speed up batch gradient descent on large-scale datasets.
  • Hardware Acceleration: Utilise hardware accelerators such as GPUs or TPUs to expedite gradient computations and parameter updates.

By incorporating these practical implementation tips, we can enhance the effectiveness and efficiency in training machine learning models, ultimately leading to improved performance and faster convergence.

What are Common Applications of Batch Gradient Descent?

Batch gradient descent is a versatile optimisation algorithm widely used across various domains. Its robustness, convergence guarantees, and ability to handle large-scale datasets make it applicable to multiple machine learning and optimisation tasks. Here are some prominent applications:

Linear Regression

It is commonly employed for training linear regression models, where the goal is to fit a linear relationship between input features and target variables.

By minimising the mean squared error (MSE) or other appropriate cost functions, it optimises the model parameters to fit the training data best.

y_actual - y_predicted: mean square error used in batch gradient descent

MSE loss function in linear regression

Logistic Regression

In logistic regression, it is utilised to optimise the parameters of the logistic regression model.

By minimising the logistic loss function or cross-entropy loss, it learns the optimal decision boundary for binary classification problems.

Neural Networks

It is the backbone for training neural networks, including feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

It is used to update the weights and biases of neural network layers by minimising the error between predicted and actual outputs through backpropagation.

In the 1990s, neural networks were used to develop generative AI models which use batch gradient descent

Support Vector Machines (SVMs)

Batch gradient descent can be employed to optimise the parameters of SVMs, such as the weights and biases of the decision hyperplane.

By minimising the hinge loss or other suitable loss functions, it learns the optimal hyperplane that separates classes in the input space.

Support vector Machines (SVM) work with decision boundaries and uses batch gradient descent

Recommender Systems

In collaborative filtering-based recommender systems, it is utilised to learn latent factors for users and items.

By minimising the reconstruction error or other appropriate loss functions, batch gradient descent optimises the latent factor matrices to predict user-item interactions accurately.

how user based collaborative filtering works with batch gradient descent

Natural Language Processing (NLP)

It is used to train models such as recurrent neural networks (RNNs) and transformers in NLP tasks such as sentiment analysis, named entity recognition, and machine translation.

It optimises the model parameters to minimise discrepancies between predicted and ground-truth labels or target sequences.

Social media messages is an example of unstructured data

Computer Vision

Batch gradient descent trains deep learning models such as CNNs in computer vision tasks such as image classification, object detection, and semantic segmentation.

It updates the convolutional kernels and fully connected layers of CNNs to minimise classification or segmentation errors.

Financial Modeling

Batch gradient descent is applied to financial modelling tasks such as stock price prediction, portfolio optimisation, and risk management.

It is used to train models that capture complex relationships in financial data by minimising the prediction error or risk measures.

batch gradient descent is used for stock price predictions

These applications demonstrate the versatility and effectiveness in solving a wide range of machine learning and optimization problems across various domains. Its widespread adoption underscores its importance as a fundamental optimization algorithm in data science and artificial intelligence.

Future Directions and Challenges of Batch Gradient Descent

As batch gradient descent continues to be a fundamental optimisation algorithm in machine learning and optimisation, future research and advancements aim to address existing challenges and explore new directions. Here are some potential future directions and associated challenges:

Scalability to Large-Scale Datasets

Its computational and memory requirements can be prohibitive for large-scale datasets that do not fit into memory or cannot be processed efficiently.

Future Direction: Develop scalable optimisation algorithms and distributed computing frameworks to handle massive datasets distributed across multiple machines or GPUs.

Acceleration Techniques

Batch gradient descent may converge slowly, especially for high-dimensional or non-convex optimisation problems.

Future Direction: Explore novel acceleration techniques such as momentum optimisation variants, adaptive learning rate methods, and second-order optimisation methods to expedite convergence and improve efficiency.

Handling Non-Convex Optimization

Batch gradient descent may struggle to escape local minima in non-convex optimisation landscapes, leading to suboptimal solutions.

Future Direction: Investigate strategies to escape shallow local minima, such as incorporating random restarts, annealing schedules, or meta-learning approaches that adaptively adjust optimisation strategies based on problem characteristics.

Robustness to Noisy or Outlier-Prone Data

Noisy or outlier-prone data can affect batch gradient descent’s performance, leading to suboptimal parameter estimates.

Future Direction: Develop robust optimisation techniques less sensitive to outliers or noise in the data, such as robust loss functions, regularisation techniques, or outlier detection methods integrated into the optimisation process.

Integration with Deep Learning Architectures

Architectural complexities and optimisation challenges inherent in deep neural networks may limit batch gradient descent’s suitability for training deep learning models.

Future Direction: Explore tailored optimisation algorithms and training strategies specifically designed for deep learning architectures, such as adaptive batch size selection, layer-wise optimisation, or gradient clipping techniques.

Incorporation of Domain-Specific Knowledge

It may not fully leverage domain-specific knowledge or constraints in real-world applications.

Future Direction: Integrate domain-specific constraints or prior knowledge into the optimisation process through regularisation techniques, custom loss functions, or hybrid optimisation approaches that combine gradient-based methods with expert knowledge.

Interpretability and Explainability

Batch gradient descent’s black-box nature can hinder the interpretability and explainability of the trained models, especially in critical applications where transparency is crucial.

Future Direction: Develop methods for interpreting and explaining the decisions made by models trained using batch gradient descent, such as feature importance analysis, saliency maps, or surrogate models.

Ethical and Societal Implications

The widespread adoption in decision-making systems raises concerns regarding fairness, bias, and unintended consequences.

Future Direction: Address ethical and societal implications by incorporating principles of fairness, accountability, transparency, and ethics (FATE) into the design and deployment of models trained using it.

By addressing these challenges and exploring new directions, the future holds promise for advancing the state-of-the-art in machine learning, optimisation, and artificial intelligence, enabling the development of more efficient, robust, and interpretable models for diverse applications.


Batch gradient descent is a cornerstone optimization algorithm in machine learning and optimization. It offers a robust framework for training models and solving complex optimization problems. Through its iterative approach of computing gradients and updating parameters, batch gradient descent provides a systematic way to minimise the cost function and find optimal solutions.

In this article, we’ve explored the mechanics of batch gradient descent, its advantages, disadvantages, practical implementation tips, optimisation techniques, applications, and future directions. From linear regression and neural networks to recommender systems and financial modelling, it finds widespread application across diverse domains, driving advancements in artificial intelligence and data science.

As we look towards the future, challenges such as scalability, optimisation speed, robustness to noisy data, and interpretability remain focal points for research and innovation. By addressing these challenges and exploring new directions, it holds promise for enabling the development of more efficient, robust, and interpretable machine learning models, paving the way for transformative applications in areas ranging from healthcare and finance to transportation and sustainability.

In essence, it exemplifies the iterative pursuit of optimisation, embodying the relentless quest for improvement and innovation that defines the field of machine learning. As researchers and practitioners continue to push the boundaries of what is possible, batch gradient descent remains a steadfast companion on the journey towards intelligent systems and data-driven insights, shaping the future of technology and society.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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