A non-technical explanation of deep learning

Sure, I can explain deep learning using the analogy of mixing paint colors.

When you mix different colors of paint, you can create new colors that are a combination of the original colors. Similarly, in deep learning, you can combine different types of data, such as images, text, and audio, to create a more comprehensive understanding of the information.

In a neural network, which is the basic building block of deep learning, each layer is like a different color of paint. The input layer represents the initial data, which is like a blank canvas. As the data passes through each layer, the network extracts increasingly complex features, similar to how mixing different colors creates more complex hues.

Each layer in a neural network can be thought of as a different color that is mixed with the previous layer's color. As the colors mix, the resulting color becomes more complex and nuanced, just as the data becomes more sophisticated and detailed as it passes through the layers of the neural network.

At the end of the process, the output layer represents the final result, which is like a finished painting. The deep learning algorithm can then use this final output to make predictions or classifications based on the input data.

So, in essence, deep learning is like mixing paint colors to create new and more complex hues, but instead of colors, the mixtures are made up of data and the neural network layers are the different colors being mixed.

Initialization: The weights and biases of the neural network are like the starting colors of the paint mixture. Just like how you need to start with some base colors to mix new ones, the neural network needs some initial weights and biases to begin the training process.

Forward Pass: During the forward pass, the input data is fed into the neural network, and the network produces an output. This output is like the mixed color of paint that results from combining different colors.

Backward Pass: During the backward pass, the error between the predicted output and the actual output is calculated using a loss function. This error is like a measure of how close the mixed color is to the desired color. Just like how you would adjust the mixture of colors to get the desired hue, the neural network adjusts the weights and biases of the neurons to minimize the error between the predicted output and the actual output.

Repeat: Steps 2 and 3 are repeated many times with different input data points, and the weights and biases of the neural network are updated after each iteration. This process is like adding more colors to the mixture and adjusting the amounts until the desired color is achieved.

Validation: After several epochs, the neural network is evaluated using a validation set of data to determine if it's overfitting or underfitting the training data. This step is like checking the mixture of colors to see if it matches the desired color under different lighting conditions.

Testing: Once the neural network has been trained and evaluated, it can be used to make predictions on new data. This step is like using the paint mixture to create a painting.

In summary, just like mixing paint colors to achieve a desired hue, the neural network adjusts its weights and biases to minimize the error between the predicted output and the actual output until it accurately predicts the output for new data.

I actually think that interdisciplinary work like this can shed light into common structures across physics, biology, neuroscience, CS, etc. If anything, I wish there were more attempts to explore the connections between these disciplines.

    > Neural networks have no memory and generally don't seek particular goals, they simply recognize, predict and generate similar instances.