How do you understand Deep Learning and Neural Network?
Visualize you're designing a smartphone app that can tell you whether a photo contains a cat or not. To make this happen, you'll use deep learning, which is the heart of many modern AI applications.
1. Neural Network:
Think of the neural network as a virtual brain within your app. It's made up of layers of interconnected nodes, just like neurons in our brains. These nodes work together to analyze the photo and decide if there's a cat in it.
2. Weights:
Each connection between nodes in the neural network has a weight. These weights are like the strength of connections between nodes. In our cat-detection example, these weights determine which features in the photo are important for recognizing a cat. If the weight is high, that connection between nodes is crucial for spotting a cat.
3. Bias:
Bias is like a baseline preference. It's a value added to each node's calculations. Think of it as a bias towards a particular outcome. In our case, it could be a slight bias towards saying "no cat" if the network tends to be overly cautious.
Here's a simplified formula to show how a neural network might decide if there's a cat in a photo:
Prediction = Weight1 * Feature1 + Weight2 * Feature2 + ... + WeightN * FeatureN + Bias
* Prediction is the final result: does the app think there's a cat or not.
* Weight1, Weight2, ..., WeightN are the weights assigned to different features in the photo (like edges, shapes, and colors).
* Feature1, Feature2, ..., FeatureN are the values that represent these features in the photo.
* Bias adds a little extra nudge to the prediction.
The neural network processes many photos of cats and non-cats, adjusting the weights and biases to minimize mistakes. Over time, it becomes really good at recognizing cats in pictures.
So, in this example, deep learning with neural networks involves a complex mathematical formula that weighs the importance of various photo features to determine if there's a cat or not. It's like training your app to be a cat-spotting expert by showing it lots of cat and non-cat photos.