Neural networks in 5 minutes.

A powerful tool to solve complex problems!

Neural networks are a very interesting concept inspired by how the human brain works. NN as they are called, are the tip of the iceberg in cutting-edge technology that is revolutionizing the industry in different verticals. Image and speech recognition, natural language processing, and autonomous vehicles are just part of the beginning. But what exactly are neural networks, and how do they work? Let's check this out in more detail...

At their core, neural networks are mathematical models designed to recognize patterns and make predictions. Just like our brains consist of interconnected neurons, neural networks are composed of artificial neurons called "nodes" or "units." These nodes work together to process information and learn from it.

Imagine you want to teach a computer how to recognize handwritten digits. To accomplish this, you would create a neural network specifically tailored for this task. The network would consist of layers of interconnected nodes, each layer serving a unique purpose.

The first layer, known as the input layer, receives the raw data, such as pixel values of an image. Each node in this layer represents a feature of the input data. In our example, each node could detect the darkness or intensity of a specific pixel.

The intermediate layers, known as hidden layers, are where the real magic happens. These layers extract complex patterns from the input data by combining the information from the previous layer. Each node in the hidden layer receives inputs from multiple nodes in the previous layer, performs calculations, and produces an output.

The final layer, called the output layer, provides the network's prediction or classification. In our case, it could indicate which digit the handwritten image represents. Each node in the output layer corresponds to a possible outcome and gives a probability score for its respective class.

So how does a neural network learn? The process is called "training." It involves feeding the network with a large dataset of known examples, labeled with their correct outputs. The network adjusts its internal parameters, known as "weights," based on the differences between its predictions and the correct labels. The goal is to minimize the error or the difference between predicted and actual outputs.

This adjustment of weights occurs through a process called "backpropagation." It calculates the gradients of the error with respect to each weight in the network, allowing the network to update the weights in a way that reduces the error. Through repeated iterations of presenting input data, comparing predictions with labels, and updating the weights, the neural network gradually improves its accuracy.

The power of neural networks lies in their ability to learn complex and non-linear relationships within data, so they can learn to identify patterns, recognize objects in images, understand human speech, and even generate human-like text. Their capabilities are continually expanding, thanks to ongoing research and advancements in hardware and algorithms.

I hope you enjoy the lecture!

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