1.Biological Neural Networks
Basic Definition:
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Baidu Encyclopedia:Biological Neural Networks generally refer to the networks composed of neurons, cells, synapses, etc. in biological brains, used to generate consciousness in organisms and assist them in thinking and acting.
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Wikipedia: Biological Neural Networks (Biological Neural Networks) refers to a group of specific neurons linked by synapses within an organism, responsible for transmitting and executing a specific function, and together with other neural circuits, constructing higher-order neural networks in the brain, generating individual consciousness, and assisting organisms in thinking and acting.
Brain Neurons:
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Input Integration: Neurons integrate signals from other neurons and external stimuli.
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Threshold Trigger: When the threshold is reached, neurons trigger action potentials.
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Weight Adjustment: Connection strength can be learned and adjusted.
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Information Storage and Transmission: Neurons are responsible for storing and transmitting information, supporting the perception, thinking, and behavior of organisms.
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Neural Network Composition: Multiple neurons are connected in a specific way to form a neural network.
Structure of Brain Neurons
2.Artificial Neural Networks
Basic Definition:
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Baidu Encyclopedia:Artificial Neural Networks (ANN) have been a research hotspot in artificial intelligence since the 1980s. It abstracts the neural network of the human brain from an information processing perspective and establishes a simple model, forming different networks based on different connection methods. In engineering and academia, it is often referred to simply as neural networks or neuro-like networks.
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Wikipedia:Artificial Neural Networks (artificial neural network, ANN) is abbreviated as neural network (neural network, NN) or neuro-like networks, which is a mathematical model or computational model that mimics the structure and function of biological neural networks (the central nervous system of animals, especially the brain) in the fields of machine learning and cognitive science, used to estimate or approximate functions.
Basic Principles::
Structure of Artificial Neural Networks
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Circular Nodes and Artificial Neurons:
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In artificial neural networks, each circular node represents an artificial neuron.
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These neurons interact with each other through specific connection methods, simulating the working principles of biological neural networks.
Connections and Signal Transmission:
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Arrows indicate connections from the output of one neuron to the input of another.
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Through these connections, signals can be transmitted within the network from one artificial neuron to another.
Weights and Activation Functions:
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Each node represents a specific output function, known as an activation function.
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Each connection between two nodes has an associated weight value that indicates the degree of influence of the previous neuron on the subsequent neuron.
Network Output:
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The output of the network varies based on the network’s connection method, weight values, and activation functions.
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By adjusting these parameters, artificial neural networks can learn and adapt to different input patterns, producing the expected output results.
3.Neural Network Training
Training Steps:
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Forward Propagation:
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Input data starts from the input layer and passes through the hidden layers layer by layer.
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Each layer uses an activation function for nonlinear transformation.
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Finally, the output layer generates prediction results.
Error Calculation:
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Compare the predicted results with the true labels and calculate the error (such as mean square error or cross-entropy loss).
Backward Propagation:
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Using the backpropagation algorithm, propagate the error from the output layer back to the input layer layer by layer.
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During this process, calculate the gradient (the partial derivative of the error with respect to weights and biases) for each layer.
Gradient Descent:
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Update weights and biases using gradient descent or other optimization algorithms based on the calculated gradients.
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The goal is to minimize the error function by gradually adjusting the weights and biases to improve network performance.
Iterative Update:
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Repeat the above steps until the stopping criteria are met (such as reaching the maximum number of iterations or the error being less than a preset threshold).
Core Algorithms:
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Activation Functions:
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Function: Determines whether a neuron is “activated” or “triggered”.
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Common Types: ReLU, Sigmoid, Tanh, etc.
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Importance: Increases the network’s nonlinearity, enabling it to learn complex patterns.
Backpropagation:
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Function: The core algorithm for weight updates in neural networks.
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Process: Calculate the error between the output layer and the true values, and propagate the error backward layer by layer to update the weights.
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Importance: Allows the network to self-adjust based on the error, gradually approaching the target function.
Gradient Descent:
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Function: An optimization algorithm used to minimize the loss function during training.
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Process: Calculate the gradient of the loss function and gradually update the network parameters in the opposite direction of the gradient.
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Importance: Allows network parameters to gradually approach the point of minimum loss.
4.Classification and Applications
Algorithm Classification::
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Feedforward Neural Networks (FNN)
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Characteristics: Data flows unidirectionally from the input layer to the output layer. Multi-layer network structure, where each layer of neurons only receives the output of the previous layer as input.
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Applications: Perceptrons, multilayer perceptrons, logistic regression, etc.
Recurrent Neural Networks (RNN)
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Characteristics: Have a cyclic structure, capable of processing sequential data and temporal dependencies. The output of neurons can serve as their own input, remembering information from previous states.
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Applications: Text generation, speech recognition, machine translation, etc.
Convolutional Neural Networks (CNN)
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Characteristics: Suitable for processing two-dimensional or three-dimensional data such as images and videos. Captures local features through convolutional layers, while pooling layers downsample to reduce the number of parameters.
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Applications: Image recognition, object detection, image generation, etc.
Long Short-Term Memory Networks (LSTM)
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Characteristics: Solve the long-term dependency problem by introducing memory cells and gating mechanisms to control the flow of information.
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Applications: Speech recognition, text generation, sentiment analysis, etc.
Generative Adversarial Networks (GANs)
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Characteristics: Combines the ideas of generative and discriminative models to generate new data similar to real data.
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Applications: Image generation, video generation, and speech synthesis, among others.
Practical Applications::
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Image Processing and Recognition
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Image Classification: Using convolutional neural networks (such as VGG, ResNet) to classify large image datasets like ImageNet, achieving human-level accuracy.
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Image Generation: GANs (Generative Adversarial Networks) are used to generate realistic images of faces, landscapes, etc.
Speech Processing and Recognition
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Speech Recognition: Applications of RNN and LSTM in speech-to-text conversion, such as Google’s speech recognition technology.
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Speech Synthesis: Models like WaveNet are used to generate natural human speech.
Natural Language Processing
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Text Classification: Using RNN or Transformer structures for sentiment analysis, topic classification, etc.
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Machine Translation: Google NMT (Neural Machine Translation) uses Transformer structures for high-quality text translation.