Artificial Intelligence

Neural Networks

Neural networks are a type of algorithm in machine learning that simulates the way human brain neurons work, learning complex patterns and relationships in data through extensive training. Neural network training methods are usually based on backpropagation algorithms and gradient descent optimization algorithms.

Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are characterized by their ability to perform feature extraction and classification on high-dimensional input data (such as images). The core components are the convolutional layer and the pooling layer. The convolutional layer uses a series of convolutional kernels (filters) to perform convolution operations on the input image, extracting different feature information and activating them through nonlinear activation functions (such as ReLU) to generate new feature maps. The pooling layer is used to reduce the size of the feature map, reduce the number of parameters and the amount of computation, with common pooling methods including max pooling and average pooling. In addition to convolutional and pooling layers, CNNs may also include fully connected layers and dropout layers. The fully connected layer is used to convert the feature map into a vector format and perform classification or regression predictions. The dropout layer is used to prevent overfitting.

CNNs have achieved significant breakthroughs in the field of computer vision and are widely used in various image and video processing tasks. In addition to image and video processing, CNNs can also be applied to the classification and recognition of other visual charts, such as recognizing price trend charts and transaction data.

Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are a type of deep learning model capable of processing sequence data. Each neuron in an RNN considers not only the current input but also the output of the previous time step during computation. This design allows RNNs to model dependencies in time series data and share weights between different time steps. In RNNs, the input for each time step is the input data at the current time and the output data from the previous time step, which can be seen as a loop. Therefore, RNNs can be considered as a network structure that reuses the same neuron across different time steps.

The core component of an RNN is the recurrent unit, which takes the output from the previous time step as the input for the current time step and outputs a new hidden state. Activation functions are used within the recurrent unit to introduce nonlinearity, enabling RNNs to learn complex sequence patterns. Traditional RNNs are prone to gradient vanishing or gradient explosion when dealing with long sequences, which is why improved models such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are widely used. These improved models introduce gating mechanisms to control the flow of information, addressing the issues of traditional RNNs and improving model performance.

RNNs can handle sequence data of any length and can be applied to various tasks, such as speech recognition, sentiment analysis, and cryptocurrency price time series prediction.

Machine Learning Models

Clustering Models

Clustering models belong to the unsupervised learning techniques in the field of machine learning. They divide data samples into similar clusters, making data points within the same cluster more similar, and data points between different clusters more diverse, thereby exploring the intrinsic structure and patterns among data, finding connections and similarities, and helping us understand and interpret the data.

Common clustering models include K-means clustering algorithm, hierarchical clustering algorithm, and density clustering algorithm. Clustering algorithms can be used for blockchain project analysis.

Random Forest

Random forest is an ensemble learning algorithm based on decision trees, which uses multiple decision trees for classification and regression. The final result is based on the voting or average prediction of these decision trees.

The core idea of random forest is to construct multiple decision trees by randomly sampling and randomly selecting features, thus reducing the variance and risk of overfitting of the model. In a random forest, each decision tree is constructed by bootstrapping the dataset and randomly selecting features. These decision trees make up the random forest, and the results of each tree are used for voting or averaging predictions.

Random forest is a highly scalable algorithm that can handle large amounts of data and high-dimensional feature spaces. It has excellent generalization ability and robustness, making it suitable for evaluating asset risks and optimizing investment portfolios.

Deep Learning

Deep learning is an important branch of machine learning, which adopts "deep" neural networks to learn high-level abstract feature representations in data and achieve deep learning of artificial neural networks.

Multilayer Perceptron

Multilayer perceptron (MLP) consists of multiple layers of neurons, with each neuron in a layer connected to all neurons in the next layer, and each connection carrying a certain weight.

The input layer of MLP receives raw data, the hidden layers learn the features in the data to recognize patterns, and the output layer outputs the prediction results. MLP calculates output values through forward propagation and adjusts weights through backward propagation, thereby continuously optimizing the model's prediction performance.

During training, MLP uses the gradient descent algorithm to minimize the error function, or objective function, so that the model can predict output values more accurately. Common error functions include mean square error (MSE) and cross-entropy.

MLP is often used to solve classification and regression problems, such as natural language processing, Twitter topic classification, and predicting cryptocurrency prices.

Reinforcement Learning

Reinforcement learning is a machine learning method in which an agent interacts with an environment and learns to maximize cumulative rewards by obtaining rewards from the environment. In reinforcement learning, the agent needs to take a series of actions to maximize cumulative rewards. These actions cause feedback in the environment, including rewards and state transitions. The goal of the agent is to learn a strategy that maximizes expected rewards through interaction with the environment.

The core of reinforcement learning is reward-based learning, in which the agent adjusts its action strategy by receiving reward signals from the environment feedback. The agent's action strategy may change over time to minimize penalties and maximize rewards as it explores the environment. By continuously interacting with the environment and adjusting based on reward signals, the agent gradually learns effective ways to solve problems.

Compared with traditional supervised and unsupervised learning, reinforcement learning has stronger adaptability, can make decisions in complex and dynamic environments, and can optimize its action strategy through continuous learning and adjustment, making it suitable for developing automated trading strategies.