This article could also have the title: How to Choose Neurons and Layers in Neural Networks — and Why It Matters for Project Managers and Innovators.
Understanding the basics of neural networks helps managers and decision-makers lead AI initiatives with more confidence. This guide explains, in plain language, how these systems learn, adapt, and scale — and what that means for real-world projects.
The world of machine learning and deep learning can seem intimidating at first glance. Terms like hidden layer, activation function, optimizer, and backpropagation sound overly technical for beginners. But in reality, these concepts can be explained in a very accessible way.
If you’ve ever wondered, “How many neurons should I put in my neural network?” or “How many layers do I need to solve a problem?”, this guide is for you.
Machine Learning vs Deep Learning: What’s the Difference?
Before talking about layers and neurons, we need to clarify one thing: not all machine learning involves neural networks.
Traditional machine learning includes algorithms like linear regression, decision trees, random forests, and SVMs, among others. Many of them don’t even have hidden layers.
Deep learning, on the other hand, is a subfield of machine learning that uses deep neural networks (with multiple hidden layers). It shines in more complex problems such as computer vision, speech recognition, and natural language processing.
👉 Every deep learning model is a form of machine learning — but not every machine learning model is deep learning.
What the Programmer Does (and Doesn’t Do)
When building a neural network with frameworks like TensorFlow/Keras, the programmer’s role is not to “program rules inside the network.” The network learns those on its own.
The programmer defines:
- The input (the format of the input data)
- The model (how many layers and neurons per layer)
- The activation function (ReLU, sigmoid, tanh, softmax)
- The optimizer (Adam, SGD, RMSprop)
- The loss function (MSE, cross-entropy, etc.)
- The number of hidden layers and neurons in each
The framework handles automatically:
- Initializing weights and biases
- Running matrix computations (forward pass)
- Comparing predictions to targets
- Adjusting weights automatically (backpropagation)
- Repeating this until the model learns
👉 In short: you define the structure of the network, and the framework finds the internal parameters that make it learn.
What Happens Inside a Hidden Layer?
A hidden layer is simpler than it looks:
- Receives inputs from the previous layer
- Multiplies each input by a weight
- Sums everything and adds a bias
- Applies an activation function (e.g., ReLU) to introduce nonlinearity
- Passes the result to the next layer
Without an activation function, the network would just be one giant linear regression.
How to Choose the Number of Neurons
There’s no magic number. The choice depends on:
- Problem complexity
- Number of features (inputs)
- Balance between underfitting and overfitting
- Practical rules of thumb (heuristics)
- Computational resources
👉 Golden rule: “The best network is the smallest one that solves the problem.”
How to Choose the Number of Hidden Layers
Here, think about the complexity of the pattern you need to learn:
- Linear problems → 0 or 1 hidden layer is enough
- Moderately complex problems → 1 to 2 hidden layers
- Very complex problems (images, speech, text) → multiple hidden layers (deep learning)
Mathematically, more layers allow the composition of functions. This means the network can learn hierarchical representations:
- Early layers → detect simple patterns
- Middle layers → combine patterns into larger structures
- Final layers → capture abstract concepts
➡️ Depth brings representational efficiency: a deep network can solve a problem with far fewer neurons than a huge shallow one.
Practical Checklist
✅ Understand your problem
✅ Start simple
✅ Test different architectures
✅ Monitor underfitting and overfitting
✅ Adjust neurons and layers based on results
✅ Automate the search if possible
Conclusion
Building a neural network is like assembling a team:
If it’s too small, it can’t handle the work.
If it’s too big, it becomes costly, slow, and inefficient.
The art of machine learning lies in finding the right balance.
So next time someone asks, “How many neurons or layers should I use?”, the answer is: 👉 It depends. Experiment. Observe. Adjust.
That’s the heart of machine learning — learning from data, but also learning from practice.