I discussed machine learning, data, and the various forms of AI in the previous lesson. This time, I will talk about deep learning and neural networks.
Non-technical explanation of deep learning
Imagine you have a really smart friend who loves solving puzzles. They’re so good that they can figure out the solution to almost any puzzle you give them. Now, think of deep learning as teaching a computer to be like that friend.
Instead of telling the computer exactly how to solve each puzzle, you give it lots and lots of examples to learn from. Each example is like a puzzle with a solution attached to it. For instance, if you want the computer to recognize cats in pictures, you show it tons of cat pictures and tell it, “These are cats.” Then, you show it other pictures with no cats and say, “These aren’t cats.”
The computer starts to look for patterns in the cat pictures, like the shape of the ears or the fur color. It tries to learn what makes a cat a cat and what doesn’t. This process of learning from examples is what we call deep learning.
The “deep” part comes from the layers of artificial neurons inside the computer’s brain, called a neural network. Just like how our brains have layers of neurons that process information, these artificial neural networks have layers too. Each layer helps the computer understand the data a little better, like recognizing simple shapes in the first layer and combining them into more complex features in the next layers.
After seeing lots of examples and adjusting its “thinking” based on feedback (like when it guesses wrong and you correct it), the computer gets better and better at recognizing cats in new pictures it hasn’t seen before.
What is a neural network?
Let’s think of a neural network as a team of specialized friends working together to solve a big problem, like recognizing objects in pictures.
Each friend in the team, called a neuron, has a specific job. Some friends are great at noticing lines, others are good at spotting colors, and some are experts in shapes. They all work together to understand what’s in the picture.
Imagine that these friends are arranged in layers, like floors in a building. Each layer of friends helps the team understand the picture a little bit better.
The first layer of friends looks at the picture and notices simple things, like lines and colors. They report what they have seen to the next layer, where additional friends begin to identify simple shapes like squares and circles using the information provided by the first layer.
Then, the information goes to another layer, where even more friends analyze these shapes and start to recognize more complex patterns, like faces or trees. This process continues through several layers until the team finally figures out what’s in the picture.
But here’s the cool part: the team doesn’t automatically know what everything is in the picture at first. They have to learn from examples (data) to become better at solving problems over time.
How can the neural network look at the picture and figure out what’s in it? Or listen to an audio clip and understand what is said?
I will break down how a neural network looks at a picture or listens to an audio clip and understands what’s in it:
Input Representation: First, the picture or audio clip is converted into a format that the neural network can understand. For a picture, this might involve breaking it down into pixels and their colors (RGB). For an audio clip, it could mean turning the sound waves into numerical data representing the audio frequencies over time.
Passing Through Layers: The converted data is then fed into the neural network. Each neuron receives input from the previous layer, processes it, and passes the result to the next layer.
Feature Extraction: In the initial layers, neurons focus on detecting simple features like edges, colors, or basic sound frequencies. These features are combined and processed in subsequent layers to recognize more complex patterns. For example, in image recognition, early layers might detect edges and textures, while later layers might identify specific objects like faces or cars.
Pattern Recognition: As the data passes through more layers, the neural network learns to recognize higher-level features and patterns. In image recognition, it might learn that a combination of certain edges and textures indicates the presence of a cat, while another combination suggests a dog. In speech recognition, it might learn to associate certain sequences of sound frequencies with particular words or phrases.
Learning from Examples: During training, the neural network is exposed to a large dataset of labeled examples, such as images with corresponding object labels or audio clips with transcribed speech. It adjusts its internal parameters (weights and biases) through a process called backpropagation, gradually improving its ability to correctly classify or understand the input data.
Output Interpretation: Finally, the neural network produces an output based on its learned understanding of the input data. For image recognition, this might be a label indicating the object detected in the picture (e.g., “cat,” “dog”). For speech recognition, it could be a transcribed text representing what was said in the audio clip.
Feedback and Iteration: The performance of the neural network is evaluated based on its output compared to the ground truth labels. Any errors are used to adjust the network’s parameters, allowing it to learn from its mistakes and improve its accuracy over time.
Whether it’s images, audio, text, or any other type of data, neural networks can be trained to understand and interpret a wide range of information.
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