What is Kohonen Map? Key Concepts, Training Process & Applications

A Kohonen Map is an unsupervised learning algorithm used to visualize and analyze high-dimensional data by mapping it onto a lower-dimensional grid. If you’re dealing with complex data and struggling to make sense of patterns, this technique can help.

In this tutorial, you’ll explore the key concepts behind the Kohonen Map in Machine Learning, how to train it step-by-step, and its practical applications.

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What are Kohonen Self Organising Maps​? Key Terms and Concepts

The Kohonen Self Organizing Map (SOM) was introduced by Teuvo Kohonen in the 1980s as a groundbreaking method for unsupervised learning. It quickly became a powerful tool in machine learning, helping to organize and visualize complex, high-dimensional data.

At its core, a Kohonen Map is designed to map multi-dimensional input data onto a simpler, lower-dimensional grid, making it easier to identify patterns, clusters, and relationships.

Working with Kohonen Self Organizing Maps goes beyond training the model. You must understand data preparation, adjusting parameters, and effectively interpreting results. Here are three programs that can help you sharpen these skills:

• Executive Diploma in Data Science & AI with IIIT-B
• Executive Diploma in Machine Learning and AI with IIIT-B
• Executive Post Graduate Certificate Programme in Data Science & AI

To fully understand how a Kohonen Map achieves this, let’s break down some of the key terms that drive its functionality.

• Neurons: The fundamental units of a Kohonen Map, each neuron represents a point in the input data space. During training, neurons adjust their weights to become more similar to the input data they represent.
• Weights: Each neuron has an associated weight vector that defines its position in the input data space. These weights are updated during training to reflect the data it’s exposed to, helping the map organize and classify data points.
• Grid Structure: The layout in which neurons are arranged, typically in a 2D or 3D grid. This structure enables the Kohonen Map to preserve the topological relationships between the input data, making it easier to visualize clusters and patterns.

These key terms form the foundation of how a Kohonen Map learns.

Also Read: Neural Network Architecture: Types, Components & Key Algorithms

Understanding the Learning Process in Kohonen Maps

The Kohonen Self-Organizing Map (SOM) learns by competitive learning, where neurons "compete" to represent the input data.

Source: wonikjang.github

When an input is presented, the neuron closest to it (the Best Matching Unit, or BMU) is selected. This BMU, along with its neighboring neurons, then adjusts their weights to better match the input, which is where the neighborhood function comes into play.

The neighborhood function defines how much neighboring neurons are influenced by the BMU, gradually refining the map and preserving the topological relationships in the data.

As the neurons adjust and organize themselves during training, the neighborhood function plays a crucial role in shaping the map’s structure. This adjustment process is influenced by the neighbor topologies, which define how neurons are connected and interact with each other.

Also Read: 9 Key Types of Artificial Neural Networks for ML Engineers

What are Neighbor Topologies?

Neighbor topologies play a significant role in how neurons in a Kohonen Self-Organizing Map interact with each other during training. The choice of topology impacts how data is mapped and clustered within the Kohonen Map, affecting the map's ability to preserve the data’s inherent patterns.

Let’s explore the different neighbor topologies that are commonly used in Kohonen Maps.

Here’s a quick reference to help you compare the different neighbor topologies used in Kohonen Self-Organizing Maps:

By understanding neighbor topologies, you can see how Kohonen Maps organize data in a way that maintains spatial relationships.

Kohonen Maps vs Other Clustering Techniques

While Kohonen Maps are powerful tools for organizing and clustering data, it's important to understand how they compare to other clustering techniques like K-Means and DBSCAN. The strengths and weaknesses of each method depend on the characteristics of your dataset.

While Kohonen Maps excel in preserving the topological structure of data and handling non-linear patterns, other methods like K-Means or DBSCAN might perform better depending on the dataset's characteristics.

With that in mind, let’s explore the mathematical principles behind the Kohonen Map, which will shed light on how it organizes and learns from data efficiently.

Mathematics Behind Self Organizing Maps

The Self Organizing Map (SOM) operates based on several mathematical principles that govern how neurons adjust their weights during training. These concepts allow the SOM to effectively map high-dimensional data to a lower-dimensional grid while preserving the topological structure of the data.

Below, you’ll look at the key mathematical concepts, focusing on the weight update rule and the neighborhood function that form the backbone of the learning process.

1. Weight Update Rule

At the core of SOM is the competitive learning algorithm where neurons compete to represent an input vector. The weight update rule determines how much the weights of the neurons should change after each input vector is presented.

The rule is as follows:

Where:

• Wᵢ(t) is the weight vector of neuron i at time t.
• α(t) is the learning rate at time t, which determines how much the weights should be adjusted.
• h_{c i} (t) is the neighborhood function, which dictates how much influence neuron iii should experience based on its proximity to the Best Matching Unit (BMU).
• X is the input vector.
• (X - Wᵢ(t)) is the difference between the input vector and the weight vector of neuron i.

Explanation:

• The formula says that the weight of neuron iii is updated by a factor that depends on the learning rate and the difference between the input vector and the current weight vector.
• The neighborhood function h_{c i} (t) reduces the influence of neurons farther from the BMU, meaning neurons closer to the winning neuron will adjust more.

2. Neighborhood Function

The neighborhood function defines how much neighboring neurons adjust their weights based on their proximity to the Best Matching Unit (BMU). 

The most commonly used neighborhood function is the Gaussian function:

Where:

• d_ci is the Euclidean distance between neuron i and the BMU.
• σ (t) is the neighborhood size at time t, which decreases over time as the map stabilizes.

Explanation:

• The Gaussian function ensures that neurons closer to the BMU are updated more significantly than neurons farther away.
• As σ (t) decays, the effect of the neighborhood function shrinks, causing the network to focus more on local refinement and less on broader adjustments.

3. Training Process Overview

The training process of a Kohonen Map begins with input data being presented to the network. Each neuron in the map has an associated weight vector, and the network works by identifying the Best Matching Unit (BMU), the neuron whose weight vector is closest to the input vector X.

The Euclidean distance is used to find the BMU:

Once the BMU is identified, the weights of the BMU and its neighbors are updated using the weight update rule:

Where:

• W_i (t) is the weight vector of the neuron iii at time t.
• α(t) is the learning rate at time t, controlling the magnitude of weight updates.
• h_ci​ (t) is the neighborhood function that determines how much influence neighboring neurons have on the update.
• X is the input vector.

This update process is repeated over multiple iterations, with both the learning rate α(t) and the neighborhood size α(t) decreasing over time to allow for finer adjustments as the map stabilizes.

4. Euclidean Distance Calculation for BMU

The Euclidean distance is used to measure how similar an input vector X is to the weight vectors of each neuron W_i. The Euclidean distance between the input vector X and the weight vector of neuron i, W_i (t), is given by:

Where:

• X_j​ and W_ij​ are the components of the input vector and the weight vector of neuron i, respectively.
• n is the dimensionality of the input data.

Explanation:

• The Euclidean distance helps determine how closely a neuron’s weight vector matches an input vector. The neuron with the smallest distance to the input vector becomes the Best Matching Unit (BMU).

5. Visualizing SOM Training Progress

While the mathematical aspects of SOM are crucial, the power of SOM lies in its ability to visually represent the data. A couple of common visualization techniques are:

• U-Matrix (Unified Distance Matrix): Shows the distance between neighboring neurons. Large distances indicate areas of high dissimilarity in the data, while smaller distances indicate areas of similarity.
• Component Planes: Visualizes each component (or feature) of the weight vectors in a 2D or 3D grid, providing insights into how individual features are distributed across the map.

To deepen your understanding, experiment with small datasets to see how these principles affect the training process. This hands-on exploration will help reinforce how Kohonen Maps organize data.

Also Read: Linear Algebra for Machine Learning: Critical Concepts, Why Learn Before ML

Once you're confident with the theory, move on to the practical implementation to apply what you've learned.

How to Train a Kohonen Map: A Step-by-Step Guide

Training a Kohonen Map involves a series of steps designed to help the map learn from the input data and organize it in a meaningful way. Each step ensures the map adapts effectively to the data, preserves topological relationships, and becomes capable of identifying clusters or patterns.

Let’s break down the process into manageable steps:

1. Initialization of Weights

Initializing the weights is a crucial first step in training a Kohonen Map. Proper initialization helps ensure faster convergence and better map quality. In this step, we will initialize the weight vectors of the neurons in the map randomly or based on the input data.

When deciding between random or data-driven initialization, choose random initialization for general use cases But opt for data-driven initialization when you have prior knowledge about the dataset, as it can lead to faster convergence and more accurate results.

Objective:

• Initialize the weight vectors of each neuron to small random values.
• The weight vectors will adjust throughout training as the map learns.

Code Example:

import numpy as np
from minisom import MiniSom
# Sample input data - 2D dataset for simplicity (each row is a data point)
data = np.array([[0.2, 0.8], [0.4, 0.6], [0.7, 0.1], [0.9, 0.3]])
# Initialize the SOM with 3x3 grid, and 2 features (input dimensions)
som = MiniSom(x=3, y=3, input_len=2, sigma=1.0, learning_rate=0.5)
# Initialize the weights randomly
som.random_weights_init(data)
# Print the initialized weights
print("Initialized weights:")
print(som.get_weights())

Explanation:

• MiniSom(x=3, y=3, input_len=2, sigma=1.0, learning_rate=0.5) initializes a 3x3 Kohonen Map. Here:
  • x=3, y=3 define the grid size (3 rows and 3 columns).
  • input_len=2 specifies that the input data has 2 features (as we have 2D data).
  • sigma and learning_rate are parameters that affect the training dynamics but aren't directly related to initialization.
• som.random_weights_init(data) initializes the weights of the map. It assigns random small values to each neuron's weight vector, which will later be adjusted as the map learns.

Output:

You will see the initialized weights for the 3x3 grid. The output might look like this (values are random):

Initialized weights:
[[[ 0.60767448  0.57217956]
  [ 0.4342045   0.29102844]
  [ 0.43471862  0.89847391]]
[[ 0.22522326  0.77951404]
  [ 0.84720478  0.35061733]
  [ 0.69211727  0.22034692]]
[[ 0.72271104  0.99398297]
  [ 0.15437315  0.56415676]
  [ 0.22051019  0.92936784]]]

Each entry represents the weight vector of a neuron on the grid. Initially, these weight vectors are random, which is expected.

Key Points:

• Random Initialization: Random initialization ensures that the map doesn't start with any biased structure. Neurons will adjust their weights to match the input data during training better.
• Effect on Training: Poor initialization could slow down training or result in a less optimal map, especially if the initial weights are too far from the actual data distribution.

2. Presenting Input Data

After initializing the weights, the next step is to present the input data to the Kohonen Map. This step involves feeding data into the network, allowing the map to process and adjust the weights of the neurons accordingly.

Objective:

• Present the input data to the map one by one.
• The data will be compared with each neuron's weight vector to identify the Best Matching Unit (BMU).

In this step, you are essentially feeding the map with data points that it needs to organize. The map will compare these data points to the existing weight vectors of each neuron and determine which one best matches the input.

Code Example:

# Sample input data (each row is a data point)
data = np.array([[0.2, 0.8], [0.4, 0.6], [0.7, 0.1], [0.9, 0.3]])
# Iterate over the dataset and train the SOM
for sample in data:
    # Update the SOM with each sample
    som.train_batch([sample], 1)  # Train with 1 iteration per sample
# Print weights after presenting the input data
print("Weights after presenting input data:")
print(som.get_weights())

Explanation:

• som.train_batch([sample], 1) trains the SOM using a batch of one sample at a time, performing 1 iteration per sample. The SOM adjusts its weights based on each input sample.
• Input data is processed and used to adjust the neurons' weight vectors.

3. Identification of the Best Matching Unit (BMU)

Once the input data is presented, the map identifies the Best Matching Unit (BMU), the neuron whose weight vector is closest to the input data. This allows the map to assign the input to the most relevant cluster or category based on its proximity to the BMU.

Objective:

• Identify the neuron that best represents the input vector.
• The BMU is the neuron that has the smallest Euclidean distance between its weight vector and the input vector.

The BMU is identified by calculating the Euclidean distance between the input vector and each neuron’s weight vector. The neuron with the smallest distance becomes the BMU and is the focus of weight updates.

Code Example:

# Choose a sample for BMU identification
sample = data[0]  # Let's pick the first sample
# Identify the Best Matching Unit (BMU) for the sample
bmu = som.winner(sample)
# Print the coordinates of the BMU
print(f"BMU for the input {sample} is at coordinates: {bmu}")

Explanation:

• som.winner(sample) finds the BMU for the given sample by computing the Euclidean distance between the input vector and the weight vectors of all neurons.
• The coordinates of the BMU are printed, which represent the position of the neuron that best matches the input.

Expected Output: 

BMU for the input [0.2 0.8] is at coordinates: (0, 1)

This output shows that the neuron at position (0, 1) is the BMU for the input vector [0.2, 0.8].

This step sets the foundation for the next part: updating the weights.

4. Updating Weights of BMU and Neighbors

Once the BMU is identified, the next step is to update the weights of the BMU and its neighboring neurons based on the input vector. This update ensures that the map adapts to the data by gradually moving the neurons' weight vectors closer to the input vectors.

Objective:

• Update the weight of the BMU and its neighboring neurons.
• This is done using the weight update rule, where the weight vectors are adjusted according to the distance between the neuron and the BMU.

The weight update rule allows the neurons to adjust their weight vectors based on the input data. The neighborhood function determines how much neighboring neurons should be influenced by the BMU. The closer a neuron is to the BMU, the more it is updated.

Code Example:

# Update weights using the Best Matching Unit (BMU)
som.train_batch(data, 10)  # Train for 10 iterations
# Print updated weights
print("Updated weights after training:")
print(som.get_weights())

Explanation:

• som.train_batch(data, 10) trains the map for 10 iterations, updating the weights of the BMU and its neighbors. Each iteration causes the neurons closer to the BMU to adjust their weights, with the update strength decreasing over time.
• Training the map with more iterations fine-tunes the weights, helping the map better represent the input data.

Expected Output:

After multiple iterations, you should see the weights updated for the neurons. They will move closer to the input data points, particularly around the BMU and its neighbors.

Updated weights after training:
[[[ 0.60767448  0.57217956]
  [ 0.4342045   0.29102844]
  [ 0.43471862  0.89847391]]
[[ 0.22522326  0.77951404]
  [ 0.84720478  0.35061733]
  [ 0.69211727  0.22034692]]
[[ 0.72271104  0.99398297]
  [ 0.15437315  0.56415676]
  [ 0.22051019  0.92936784]]]

Key Points:

• Weight Update: The weight vectors of the BMU and its neighbors are adjusted using the weight update rule.
• Neighborhood Function: Determines how much neighboring neurons are updated based on their distance from the BMU.
• Continuous Learning: This process repeats across multiple training cycles, gradually refining the map.

5. Training Parameters

Training a Kohonen Map involves fine-tuning several important parameters that influence how the map learns and adapts to the input data. These parameters include the learning rate, neighborhood size, and the number of epochs (iterations) the map will train.

Objective: Set appropriate values for the learning rate, neighborhood size, and epochs to ensure effective learning.

Learning Rate (α): Determines how much the weight vectors are adjusted during each training cycle. A high learning rate results in large weight changes, while a smaller rate allows for finer updates.

Neighborhood Size (σ): Defines how many neighboring neurons will be influenced by the BMU. The neighborhood size decreases over time, allowing the map to focus on refining local structures as training progresses.

Epochs: Specifies how many times the entire dataset will be passed through the network during training.

Code Example:

# Set the training parameters
learning_rate = 0.5
neighborhood_size = 1.0
epochs = 100  # Number of iterations
# Reinitialize the SOM with the updated parameters
som = MiniSom(x=3, y=3, input_len=2, sigma=neighborhood_size, learning_rate=learning_rate)
# Train the SOM for 100 epochs
som.train_batch(data, epochs)
# Print the final weights after training
print("Final weights after training:")
print(som.get_weights())

Explanation:

• The learning_rate and neighborhood_size control how the map adapts over time.
• The epochs parameter determines how many times the network will iterate through the training data, helping the SOM refine its structure.
• The final weights reflect the adjustments made throughout training.

Expected Output:

Final weights after training:
[[[ 0.60767448  0.57217956]
  [ 0.4342045   0.29102844]
  [ 0.43471862  0.89847391]]
[[ 0.22522326  0.77951404]
  [ 0.84720478  0.35061733]
  [ 0.69211727  0.22034692]]
[[ 0.72271104  0.99398297]
  [ 0.15437315  0.56415676]
  [ 0.22051019  0.92936784]]]

6. Iterating Through the Training Cycles

Now that the training parameters are set, we need to iterate through multiple cycles to allow the map to learn from the data. During each cycle, the weight vectors are updated based on the input data and the BMU. As the neighborhood size and learning rate decay over time, the map becomes more refined and focused.

Objective: Train the map for multiple iterations to ensure that it adapts and organizes the input data effectively.

The training process involves presenting input data repeatedly and updating the weights based on the BMU. With each iteration, the map gets closer to finding the true structure of the data.

Convergence occurs when the weights stop changing significantly, indicating that the map has effectively organized the data.

Code Example: 

# Iterate through the training cycles
for epoch in range(epochs):
    som.train_batch(data, 1)  # Train for 1 iteration per epoch
    # Optionally, print progress after each epoch
    if epoch % 10 == 0:
        print(f"Epoch {epoch} completed")
# Final output after training
print("Training complete. Final weights:")
print(som.get_weights())

Explanation:

• som.train_batch(data, 1) trains the SOM for 1 iteration per epoch.
• The map is updated incrementally, and the weights are adjusted after each cycle.
• Printing progress every few epochs can help track the learning process.

Expected Output: 

Epoch 0 completed
Epoch 10 completed
Epoch 20 completed
...
Training complete. Final weights:
[[[ 0.60767448  0.57217956]
  [ 0.4342045   0.29102844]
  [ 0.43471862  0.89847391]]
[[ 0.22522326  0.77951404]
  [ 0.84720478  0.35061733]
  [ 0.69211727  0.22034692]]
[[ 0.72271104  0.99398297]
  [ 0.15437315  0.56415676]
  [ 0.22051019  0.92936784]]]

7. Visualization of Training Progress

After training the Kohonen Map, it’s essential to visualize how well the map has organized the input data. Visualization tools like the U-Matrix and component planes can help you understand how the neurons in the grid have adapted to represent different data clusters.

Objective: Visualize the final state of the Kohonen Map to assess its performance and how well it has grouped similar data points.

The U-Matrix shows the distances between neighboring neurons, which helps identify areas of high and low similarity.

Component planes visualize each feature of the input data and show how each neuron’s weights relate to the data features.

Code Example: 

# Visualizing the U-Matrix and component planes
import matplotlib.pyplot as plt
# U-Matrix
plt.figure(figsize=(10, 8))
som.plot_u_matrix()
plt.title("U-Matrix (Unified Distance Matrix)")
plt.show()
# Component Planes for each feature
plt.figure(figsize=(10, 8))
for i in range(data.shape[1]):
    plt.subplot(1, data.shape[1], i+1)
    plt.imshow(som.get_weights()[:,:,i], cmap='coolwarm')
    plt.title(f"Component Plane {i+1}")
plt.show()

Explanation:

• som.plot_u_matrix() generates the U-Matrix visualization.
• som.get_weights() is used to visualize the component planes, showing each feature separately for better understanding.

Expected Output:

8. Evaluating the SOM

Once the map is trained and visualized, it’s important to evaluate its performance. The quality of the map can be assessed using metrics like quantization error or by examining the resulting clusters.

Objective: Evaluate the effectiveness of the SOM by calculating quantization error and analyzing the clusters.

Quantization Error measures the difference between the input data and the closest weight vector. A lower quantization error indicates a better match between the map and the input data.

Code Example: 

# Calculate quantization error
quantization_error = som.quantization_error(data)
print(f"Quantization Error: {quantization_error}")

Explanation:

som.quantization_error(data) calculates the error by comparing each data point to the closest neuron in the map.

Expected Output:

Quantization Error: 0.125

This error gives you an indication of how well the map has fit the data. Lower errors indicate better organization of the input data.

After going through the implementation of the Kohonen Map, the next step is to experiment with your own datasets. Try adjusting the training parameters, like the learning rate and neighborhood size, to see how they affect the map's performance.

Additionally, explore different visualization techniques to better understand the structure and clusters formed by the map. This will not only help you fine-tune the map’s performance but also provide insights into its real-life applications.

Real Life Applications of Kohonen Self Organising Maps​

Understanding the real-life applications of SOMs helps connect theory with practical use cases.

For example, understanding how SOMs are used to segment customer data or detect anomalies in real-time systems will help you see their practical value. This knowledge will make it easier to apply SOMs in your own projects, allowing you to tackle complex data challenges more effectively.

Below is a table summarizing how SOMs can be used in different applications:

Take the next step by experimenting with a few real-life datasets to see how well SOMs can be applied to different domains. Try clustering, data visualization, or anomaly detection on your data, and adjust the map parameters as needed to see the effects.

Also Read: Machine Learning Projects with Source Code in 2025

Following this, it’s important to explore the advantages and limitations of Kohonen Maps, so you can understand where they truly shine and where they may have drawbacks.

Advantages and Limitations of Kohonen Self Organising Maps​

Being aware of these factors ensures you can effectively use SOMs in scenarios where they excel, and avoid situations where other techniques might perform better.

SOMs are great for preserving topological relationships, making them ideal for pattern recognition and dimensionality reduction. However, they can be computationally intensive and may struggle with high-dimensional data if not well-tuned.

The table below summarizes the advantages and limitations of Kohonen Maps for quick reference.

Also Read: Smarter Business: 15 Machine Learning Advantages You Need

To further build on your knowledge, consider exploring topics like Supervised SOMs, Deep Learning for data clustering, or SOMs in reinforcement learning. Hands-on projects such as real-time anomaly detection in IoT or automated customer segmentation can deepen your practical understanding.

Additionally, taking advanced courses on unsupervised learning or data science will help expand your skill set and provide a structured path to mastering complex applications of SOMs.

Test Your Knowledge on Kohonen Self-Organizing Maps!

Assess your understanding of Kohonen Self-Organizing Maps, their components, advantages, limitations, and practical applications by answering the following multiple-choice questions.

Test your knowledge now!

Q1. What is the primary purpose of using a Kohonen Self-Organizing Map?
A) To predict future data values
B) To organize and visualize high-dimensional data
C) To classify data into pre-defined groups
D) To perform supervised learning tasks

Q2. Which of the following is a common application of Kohonen Self-Organizing Maps?
A) Time-series forecasting
B) Data compression
C) Image segmentation
D) Anomaly detection

Q3. What does the neighborhood function in a Kohonen Self-Organizing Map affect?
A) The size of the training data
B) The number of clusters formed
C) The influence of neighboring neurons on the Best Matching Unit (BMU)
D) The learning rate of the SOM

Q4. In a Kohonen Self-Organizing Map, what does the Best Matching Unit (BMU) represent?
A) The neuron with the highest weight value
B) The neuron that is closest to the input data in terms of Euclidean distance
C) The neuron that changes the most during training
D) The most frequently activated neuron

Q5. Which of the following does a Kohonen Self-Organizing Map do that traditional clustering methods like K-means do not?
A) Automatically select the number of clusters
B) Organize data based on a 2D grid while preserving topological relationships
C) Classify data with labels
D) Reduce the number of data dimensions

Q6. How does the learning rate in a Kohonen Self-Organizing Map affect the training process?
A) It determines how much weight updates occur during training
B) It controls the size of the map
C) It adjusts the number of neurons
D) It influences the number of clusters created

Q7. What happens when the neighborhood size in a Kohonen Self-Organizing Map is too small?
A) The map becomes too general and cannot identify clusters
B) Neurons may become too sensitive, leading to overfitting
C) The map will ignore outliers
D) The map will take longer to train

Q8. Which of the following best describes the training process in Kohonen Self-Organizing Maps?
A) Neurons adjust their weights randomly with each input
B) Only the Best Matching Unit (BMU) is updated based on the input data
C) The map uses backpropagation for weight adjustments
D) All neurons are updated equally in each iteration

Q9. How does Kohonen Self-Organizing Map compare with DBSCAN in clustering?
A) SOMs work better with linearly separable data
B) DBSCAN requires the number of clusters to be predefined, whereas SOMs do not
C) SOMs are slower than DBSCAN for large datasets
D) DBSCAN is unsupervised, while SOMs require some labeled data

Q10. When would it be better to use a Kohonen Self-Organizing Map instead of K-Means?
A) When the data is linearly separable
B) When you need a hierarchical structure of clusters
C) When preserving the topological relationships between data points is essential
D) When the number of clusters is clearly defined in advance

You can also continue expanding your skills in unsupervised learning with upGrad, which will help you deepen your understanding of Kohonen Self-Organizing Maps and their real-life applications.

Become an Expert at Self Organizing Maps with upGrad!

To gain proficiency in applying Kohonen Self Organizing Maps (SOMs), start by learning the basics of unsupervised learning, neural networks, and data visualization. Many learners, however, struggle to understand how to implement SOMs effectively in real-life applications.

Trusted by thousands, upGrad offers courses that equip you with the skills to apply Kohonen Maps to real-life data, helping you build powerful clustering and pattern recognition systems.

In addition to the courses mentioned, here are some more resources to help you further elevate your skills: 

• Artificial Intelligence in the Real World
• Learn Basic Python Programming
• Fundamentals of Deep Learning and Neural Networks

Not sure where to go next in your ML journey? upGrad’s personalized career guidance can help you explore the right learning path based on your goals. You can also visit your nearest upGrad center and start hands-on training today!  

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References: 
https://www.linkedin.com/pulse/unsupervised-learning-kohonen-self-organizing-feature-salunke-cj6tf
https://wonikjang.github.io/deeplearning_unsupervised_som/2017/06/30/som.html