Neural Networks

• Convolutional Neural Networks (CNNs) Inspired by the visual cortex, CNNs excel in image recognition and computer vision tasks. They use convolutional layers to automatically learn spatial hierarchies of features from images.

• Recurrent Neural Networks (RNNs) Mimicking the sequential nature of brain processing, RNNs are used for natural language processing and time series analysis. They maintain a hidden state to capture temporal dependencies in sequential data.

• Long Short-Term Memory (LSTM) Networks Inspired by the brain's ability to store information over time, LSTMs address the vanishing gradient problem in RNNs. They use memory cells to retain information for long periods, making them effective for tasks like language translation and speech recognition.

Cases

• CNNs

import torch
import torchvision

# Load a pre-trained CNN model
model = torchvision.models.resnet50(pretrained=True)

• RNNs

import torch
import torch.nn as nn

# Define a simple RNN model
rnn = nn.RNN(input_size=10, hidden_size=20, num_layers=2)

• LSTMs

import torch
import torch.nn as nn

# Define a simple LSTM model
lstm = nn.LSTM(input_size=10, hidden_size=20, num_layers=2)