But what is a neural network? | Chapter 1, Deep learning

Introduction

In this chapter, we delve into the concept of neural networks, which are often shrouded in mystery and technical jargon. Neural networks mimic the human brain's structure and function, transforming how we interpret and process data, especially images.

Understanding Neural Networks

At its core, a neural network consists of layers of interconnected units called neurons. In our example, we focus on a classic case: recognizing handwritten digits. The primary advantage of neural networks lies in their ability to learn from data without being explicitly programmed to do so.

Structure:

• Input Layer: Consisting of a grid of 28 x 28 pixels (784 pixels total), each neuron in this layer represents the grayscale value of a corresponding pixel in the image, with values ranging from 0 (black) to 1 (white).

• Output Layer: This layer comprises 10 neurons, each corresponding to a digit from 0 to 9. The activation level of these neurons signifies the network's confidence in a particular digit.

• Hidden Layers: Between the input and output layers, there are several hidden layers that perform complex transformations to extract features from the input data.

How Neural Networks Work

When an image is fed into the network, it lights up the input layer based on pixel brightness. This pattern reverberates through the hidden layers, leading to a resultant pattern in the output layer. The highest activation in the output layer indicates the network's prediction of the digit represented by the image.

Potential Functionality of Hidden Neurons

The neurons in hidden layers are crucial for recognizing patterns. For instance, they may detect features such as edges, loops, or lines, which contribute to identifying more complex structures like digits. A possible hope is that certain neurons light up for specific patterns, like loops or lines, which can then be converted into digit recognition.

Weights and Biases: Each connection between neurons in subsequent layers has associated weights and biases, parameters that are adjusted during the network’s training phase. During training, the network learns to adjust these weights and biases, aiming to minimize the difference between its predictions and the actual labels of the input data.

Activation Functions

Neurons use activation functions to squish the results of their weighted sums into a range (commonly between 0 and 1). A widely used activation function is the sigmoid function, which takes a real-valued input and squashes it to a range between 0 and 1.

Learning in Neural Networks

Learning in a neural network refers to adjusting the numerous weights and biases. This process involves the network iterating over presented data, refining its parameters to accurately recognize patterns.

A challenging yet fascinating thought experiment is considering manually tuning the 13,000 weights and biases within the network—a daunting task that illustrates the complexity involved in training neural networks.

Conclusion

In summary, a neural network is a complex function that takes inputs, processes them through multiple layers, and outputs predictions. The network's ability to learn and optimize its weights is what allows it to perform tasks like recognizing handwritten digits.

Keywords

• Neural networks
• Deep learning
• Input layer
• Output layer
• Hidden layers
• Weights
• Biases
• Activation functions
• Sigmoid function
• Learning

FAQ

Q: What is a neural network?
A: A neural network is a computational model inspired by the human brain, consisting of interconnected neurons that process data and learn to perform tasks like classification and recognition.

Q: How do neural networks recognize handwritten digits?
A: Neural networks recognize handwritten digits by processing pixel values through multiple interconnected layers, adjusting weights and biases to learn patterns associated with each digit.

Q: What are the components of a neural network?
A: The components of a neural network include input layers (representing raw data), hidden layers (for feature extraction), and output layers (for prediction).

Q: What role do weights and biases play in neural networks?
A: Weights and biases are parameters that determine how the input data is processed in the network. They are adjusted during training to minimize error in predictions.

Q: Why are activation functions used in neural networks?
A: Activation functions are used to introduce non-linearity to the model, allowing the network to learn complex patterns and behaviors in the data.