Python Neural Network Class

Training a neural network is the process of adjusting its weights and biases so that the network's outputs come to match the desired targets as closely as possible. Starting from an initial set of parameters, the network computes an output via forward propagation, compares that output to the target using a loss (error) function, and then updates the weights and biases based on the resulting error signal, typically via backpropagation combined with a gradient-based optimization step. This iterative fine-tuning of weights and biases from the training data, repeated until the network's performance converges, is what is meant by "training" a neural network.

Training Neural Networks

In this context, deep learning is another name for neural-network models.

Deep Learning as a Name for Neural Networks

In this setting, a small neural network is one that has a relatively small number of hidden units, layers, or learned parameters.

Defining a Small Neural Network

The structure of a neural network, including choices like depth, width, and how layers are connected, can have a major effect on how well it learns. For that reason, architecture design has been an active area of rapid improvement.

Network Design Can Strongly Influence Model Performance

The most common types of neural networks are: - Feedforward neural networks - Convolutional neural networks - Recurrent neural networks

Types of Neural Networks

A neural network is a model built from connected layers of simple processing units.

- The **input layer** receives the raw data.
- One or more **hidden layers** transform that data.
- The **output layer** produces the final prediction.

Each unit, often called a neuron, receives values through weighted connections. It combines those values, applies a rule or activation function, and passes the result forward if enough signal is present. By repeating this across many linked units, the network can learn patterns that help it make predictions or classifications.

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- Neural Networks
- Forward Propagation
- Backward Propagation
- Activation Functions

Foundational Concepts of Deep Learning

Neural Network

In simple terms, an activation function is added in the neural network in order to help the network learn complex patterns in the data. When comparing this to the brain a neuron sends signals to other neurons but what signals being sent depends on the conditions. The activation function is at the end taking in the data from the node and transforms it into an input that a new node (neuron) can take into the new layer.

- And of course, typically math is involved with coming up with these functions.

Activation Functions

As the name suggests, it simply means that the input data is pushed in the forward direction of the network. The data will never flow in the reverse direction during the output generation.

To help further this idea: consider using a feed-forward neural network.


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