Neural Networks, CNNs, and LSTMs: Core Architectures and Training
Summary
This tutorial introduces feedforward neural networks, convolutional neural networks, recurrent neural networks, and LSTMs. It explains how perceptrons combine features with learned weights and an activation function, how layers build representations, and how backpropagation uses loss gradients and the chain rule to update parameters. It also describes regularization and dropout as ways to limit overfitting, and outlines CNN parameter sharing, convolution, pooling, channels, and padding.
For sequential data, the tutorial presents RNNs as shared-parameter units applied across ordered observations. It notes that repeated gradient propagation can cause vanishing gradients, then explains how LSTM gates regulate retained memory and new information. The material is conceptual, with illustrative network dimensions and equations rather than trading experiments or performance evidence. It does not specify how to prepare financial data, prevent leakage, choose architectures for a trading task, or validate signals out of sample, so it is an introduction to model mechanics rather than a trading recipe.
Key ideas
- A perceptron forms a weighted sum of inputs and passes it through an activation function.
- Feedforward networks stack fully connected layers and are trained by propagating loss gradients backward.
- Regularization and dropout can help control overfitting in parameter-heavy neural networks.
- CNNs reuse kernel parameters across locations, while pooling reduces feature-map dimensions.
- RNNs share parameters across sequence steps, and LSTM gates help retain or discard information over time.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.