I've been doing some research lately and keep seeing "how do neural networks learn" mentioned in various places. Apparently, they work by adjusting "weights" similar to synapses in the human brain. But how exactly does this happen? Is this really what's meant by "learning," or is there another algorithm at play? Does anyone else feel as confused as I do?
How do neural networks work, in simple terms?
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In my experience with a Python course, when I implemented my first simple neural network for digit classification, I noticed that the loss gradually decreased as the algorithm updated the weights through backpropagation—just like how neural synapses in the brain strengthen repeated connections. So what we call "learning" is essentially adjusting weights according to the backpropagation algorithm, nothing more than that.
In reality, learning neural networks is done by adjusting weights in a manner similar to how communication between neurons in our brain works, but this is applied using an algorithm known as *backpropagation* with *gradient descent* optimization. When a dataset (such as images or texts) is fed into the model, its outputs are calculated and compared to the desired value using a loss function. The difference between the desired and actual output is translated into a gradient signal that is propagated backward through the layers to update each weight by a small amount determined by the learning rate. This way, errors gradually shrink, and the network "learns" the patterns in the data.
While working on an image classification project using TensorFlow, I noticed that adjusting the learning rate and batch size significantly impacts training speed and stability—once, I encountered a major error spike when the learning rate was too high, so I reduced it slightly, and the model stabilized again. Experience taught me that "learning" here is merely a statistical optimization process based on mathematical functions, not a literal replication of brain mechanisms, despite the conceptual similarity in the idea of adjusting connections.
Can you explain the details of weight adjustments during backpropagation and how to choose a learning rate? I'd also like to know if there are additional algorithms used alongside backpropagation to speed up the learning process.
Learning in neural networks is like automatically adjusting the volume on a radio based on the error signal: the difference between the expected and actual outputs (the loss) is calculated, and a backpropagation algorithm is used to update the weights. This process is what’s meant by “learning” in models and isn’t replaced by another algorithm.
Imagine the neural network as a collection of interconnected cells, where each connection has a "weight" that determines how strongly the signal passing through it is. When the network is given a set of data (such as images or texts) and asked to predict an outcome (like classifying an image), the process begins by calculating the outputs of the final layer using the current weights. The predicted result is then compared to the actual result using a loss function; the difference is known as the "error."
To minimize this error, the "backpropagation" algorithm is applied. The idea is to calculate the partial derivative of the error with respect to each weight—that is, how the error would change if we slightly adjusted the weight. The weights are then updated by following the negative gradient direction: weight ← weight − learning rate × error derivative. In this way, with each training sample or batch of samples, the weights gradually move toward values that make the error smaller.
From practical experience, I recommend starting with a simple model like a perceptron or a multi-layer network (MLP) on a small dataset (such as Iris or MNIST). Use a library like TensorFlow/Keras to implement the algorithm, and don’t forget to adjust the learning rate and momentum coefficient to avoid getting stuck in local minima or overshooting. Monitor the loss curve during training; if it doesn’t improve, try changing the number of layers, activation functions (ReLU instead of sigmoid), or regularization techniques like dropout.
In the end, what we call "learning" in neural networks is simply iterative optimization of weights through gradient descent. There’s no other magical algorithm behind it; it all comes down to calculating derivatives and systematically updating weights. Once you master these steps and explore the impact of different hyperparameters, you’ll notice tangible improvements in your model’s performance.
In reality, "learning" in neural networks is limited to adjusting the weights between cells (nodes) in a way similar to strengthening or weakening neural connections in the brain. When training data is passed through the network, the outputs are calculated and then compared to the target (ground truth). The difference between them is converted into an error signal, which is used in the backpropagation algorithm to update the weights according to the gradient descent rule or one of its optimizers (like Adam). In short, the network doesn’t "learn" by understanding concepts—it optimizes its weights to statistically minimize the error.
From my experience building an image classification model on a Raspberry Pi, the best practical step is to start by randomly initializing the weights and then testing a simple network with two hidden layers before complicating the architecture. Use a small dataset (e.g., 500 images) to monitor how much the loss decreases with each epoch. If you notice the loss isn’t decreasing or is fluctuating, try adjusting the learning rate or adding a ReLU activation layer between the layers. This way, you can clearly see how weight adjustments are what cause the network to "learn."
Neural network learning is essentially about updating the weights of each layer through backpropagation (gradient descent) after obtaining the output via forward propagation, based on the error—much like how the strength of synapses in the brain changes with experience. In my first hands-on project, I trained a simple MNIST classifier using mean squared error and the Adam optimizer. Seeing the loss gradually decrease and the model's accuracy improve gave me a direct, intuitive sense of how "weight adjustment = learning" actually works.
In artificial neural networks, the concept of "learning" is largely about adjusting the weights of connections between neurons, much like the synapses in the human brain. During training, the backpropagation algorithm is typically used to fine-tune these weights so that the difference (error) between predicted outputs and actual results is minimized. This involves calculating the derivative of the error with respect to each weight and updating it by a small step (learning rate)—hence the idea of "learning from mistakes."
You can compare this method to a decision tree algorithm, where no weights are adjusted; instead, data is split based on fixed rules derived from features. Decision trees produce transparent and easy-to-understand models, but they usually struggle to capture complex patterns as effectively as neural networks, which leverage gradual weight adjustments. So, if you need a model that handles nonlinear and complex data, neural networks with backpropagation weight updates are the "go-to" solution. But if transparency and interpretability are top priorities, a decision tree might be the better choice.