Introduction to Neural Networks

Introduction

Neural networks are a powerful tool in machine learning inspired by the human brain's structure and function. Just as human brains consist of interconnected neurons that process and transmit information, artificial neural networks are composed of interconnected nodes (also called neurons or units) that perform computations on input data and learn to recognize patterns.

Consider the task of handwritten digit recognition. One could create a neural network and train it by showing it many examples of handwritten digits along with their corresponding labels (0, 1, 2, etc.). The network would learn to extract relevant features from the input images and adjust its weights (internal connections) to map the input patterns to the correct output labels. After training, the network can accurately classify new, unseen handwritten digits.

classification

Neural networks excel at learning complex, non-linear relationships in data and have achieved remarkable success in various fields, such as image recognition, natural language processing, and robotics.

Definition

A neural network is a machine learning model that consists of interconnected nodes organized in layers. The essential components of a neural network are:

Neural Network Architecture

Input Layer: The input layer receives the input data and passes it to the next layer.
Hidden Layers: One or more hidden layers process and transform the input data. Each node in a hidden layer applies a non-linear activation function to the weighted sum of its inputs.
Output Layer: The output layer produces the final predictions or outputs of the network.
Weights and Biases: The connections between nodes have associated weights that determine the strength and importance of each connection. Biases are additional parameters allowing more flexibility in the network's computations.

The general equation for the output of a node in a neural network is:

y = f(\sum_{i=1}^{n} w_i x_i + b)

Where:

$y$ is the output of the node,
$f$ is the activation function (e.g., sigmoid, ReLU),
$x_i$ are the inputs to the node,
$w_i$ are the corresponding weights,
$b$ is the bias term,
$n$ is the number of inputs to the node.

The role of the activation function is to introduce non-linearity into the network, enabling it to learn complex patterns and relationships in the data.

Training a neural network involves adjusting the weights and biases to minimize a loss function that measures the difference between the network's predictions and target values. The most common algorithm used for training neural networks is called backpropagation, which efficiently computes the gradients of the loss function with respect to the network's parameters.

The goal is to find the optimal set of weights and biases that minimize the loss function:

\min_{W, b} L(y, \hat{y})

Where:

$W$ represents the weights of the network,
$b$ represents the biases,
$L$ is the loss function,
$y$ is the true target value,
$\hat{y}$ is the predicted value.

By iteratively updating the weights and biases using gradient descent or its variants, the network gradually learns to map the input data to the desired output.

Applications

Diffusion Models for Generative NFTs: Diffusion models can be used to generate NFTs with the user in loop, providing or combining prompts with the Artist.
Predicting Rugpools: Neural networks can combine complex interactions like twitter followers, holders on chain, locked liquidity, twitter presence and other factors to predict if a newly launched liquidity pool will rug or not.
Image Classification: Neural networks, particularly convolutional neural networks (CNNs), have revolutionized the field of computer vision. They can learn to recognize objects, faces, and scenes in images accurately. For example, neural networks are used in self-driving cars to detect and classify objects on the road, such as pedestrians, traffic signs, and other vehicles.
Natural Language Processing: Neural networks have significantly advanced understanding and human language generation: transformers, a neural network architecture, power modern-day LLMs like ChatGPT and Claude.

Conclusion

Neural networks are a powerful and versatile tool in machine learning, capable of learning complex patterns and relationships from data.

Neural networks operate as a "black box," where the relationships between inputs and outputs are learned through complex transformations, making it challenging to interpret the contribution of individual features directly.

As a machine learning practitioner, understanding the fundamentals of neural networks is essential. With the right architecture, training techniques, and data, neural networks can tackle cognitively challenging problems and deliver state-of-the-art performance.

Resources

Classification Reinforcement Learning