Neural networks are at the heart of modern AI. Whether it’s image recognition, chatbots, or recommendation systems, they all rely on these interconnected structures to learn patterns from data.
What is a Neural Network?
- A neural network is made up of many interconnected nodes (neurons), each acting like a tiny decision-making unit.
- Every connection between neurons carries a weight, which is a parameter the model learns during training.
- These weights determine how strongly one neuron influences another, and they are gradually adjusted so the network can make better predictions over time.
How Are the Values Estimated?
- When training begins, each neuron starts with random weights.
- These weights are updated through backpropagation, a feedback process that adjusts them based on how wrong the model’s prediction was.
- The update depends on:
- Loss value: how far the prediction was from correct
- Gradient: the direction the weight should move
- Learning rate: how big each step of adjustment should be
- The goal is to reduce the loss and push the gradient closer to zero over time.
- Interpreting the gradient:
- Positive gradient → decrease the weight
- Negative gradient → increase the weight
Why Neural Networks?
- Traditional machine learning works well when data patterns are linear, meaning they follow straight-line relationships.
- Neural networks shine because they use activation functions to introduce non-linearity, enabling them to learn complex patterns — shapes, edges, behaviors, and any relationship that isn’t simple or straight.
- Popular activation functions include ReLU, Sigmoid, and Softplus, each influencing how a neuron activates.
Layers in a Neural Network
Input Layer
- The network begins with an input layer containing multiple nodes.
- These nodes don’t learn anything — they simply pass the raw input into the network.
- They are often mistaken for neurons, but their job is mainly to feed data forward.
- They take the raw data.
- They pass those numbers into the network.
- They do not apply weights, biases, or activations.
- Because of this, these nodes are often mistaken for “neurons” but they’re better understood as data carriers — a doorway into the network rather than processors.
Hidden Layers
- A network may have one or several hidden layers, each filled with neurons.
- These are the actual learning units, transforming the input step by step to capture patterns in the data.
- It receives input from the previous layer
- Applies weights and biases
- Passes the result through an activation function
- And sends the output deeper into the network
- This repeated transformation allows the model to gradually extract patterns — edges in images, sentiment in text, or trends in numerical data.
- Layer by layer, the network builds up a more meaningful internal representation of the input.
Output Layer
- The final layer, responsible for producing the network’s output — a prediction, probability, class label, etc.
- Depending on the problem, this layer may output:
- A single number
- A vector of probabilities
- A class label
- A multi-dimensional prediction
- Like hidden neurons, output neurons also use weights, biases, and sometimes an activation function (e.g., sigmoid or softmax).
- The only difference is that this layer’s output is directly interpreted as the model’s answer.
The 5-Step Life Cycle of a Neuron (Applies to Every Layer Except Input)
Every neuron inside a neural network — whether in a hidden layer or the output layer — follows the exact same processing routine. Understanding this cycle is key to building intuition.
1. It receives inputs (activations).
- These activations come from either the input layer or the previous hidden layer.
2. It multiplies each input by its corresponding weight.
- Weights determine how strongly the neuron should pay attention to each incoming value.
3. It adds a bias.
The bias shifts the weighted sum, giving the neuron flexibility to activate even when inputs are small or zero.
4. It applies an activation function.
- The weighted sum plus bias (called the pre-activation, or z) goes through a function like ReLU, sigmoid, or tanh.
- This step introduces non-linearity — essential for learning complex patterns.
5. It outputs a new activation.
- This final value (a) becomes the input to the next layer, allowing the network to stack transformations and build rich representations.
Final Thoughts
A neural network is essentially a repeated cycle of: input → weighted sum → bias → activation → output layer after layer, until the final prediction is produced.
This is a gentle introduction to neural networks — enough to understand what they are made of and how they learn. From here, you can explore topics like activation functions, optimizers, loss functions, and different neural architectures.
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