– However, label encoding may introduce an ordinal relationship between categories that doesn’t exist, potentially leading to incorrect interpretations.
2. One-Hot Encoding:
– One-hot encoding is a popular technique for representing categorical variables in a neural network.
– Each category is transformed into a binary vector, where each element represents the presence or absence of a particular category.
– One-hot encoding ensures that each category is equally represented and removes any implied ordinal relationships.
– It enables the neural network to treat each category as a separate feature.
3. Embedding:
– Embedding is a technique that learns a low-dimensional representation of categorical variables in a neural network.
– It maps each category to a dense vector of continuous values, with similar categories having vectors closer in the embedding space.
– Embedding is particularly useful when dealing with high-dimensional categorical variables or when the relationships between categories are important for the task.
– Neural networks can learn the embeddings during the training process, capturing meaningful representations of the categorical data.
4. Entity Embeddings:
– Entity embeddings are a specialized form of embedding that takes advantage of the relationships between categories.
– For example, in recommendation systems, entity embeddings can represent user and item categories in a joint embedding space.
– Entity embeddings enable the neural network to learn relationships and interactions between different categories, enhancing its predictive power.
5. Feature Hashing:
– Feature hashing, or the hashing trick, is a technique that converts categorical variables into a fixed-length vector representation.
– It applies a hash function to the categories, mapping them to a predefined number of dimensions.
– Feature hashing can be useful when the number of categories is large and encoding them individually becomes impractical.
The choice of technique for dealing with categorical variables depends on the nature of the data, the number of categories, and the relationships between categories. One-hot encoding and embedding are commonly used techniques, with embedding being particularly powerful when capturing complex category interactions. Careful consideration of the appropriate encoding technique ensures that categorical variables are properly represented and can contribute meaningfully to the neural network’s predictions.
Part II: Building and Training Neural Networks
Feedforward Neural Networks
Structure and Working Principles
Understanding the structure and working principles of neural networks is crucial for effectively utilizing them. In this chapter, we will explore the key components and working principles of neural networks:
1. Neurons:
– Neurons are the basic building blocks of neural networks.
– They receive input signals, perform computations, and produce output signals.
– Each neuron applies a linear transformation to the input, followed by a non-linear activation function to introduce non-linearity.
2. Layers:
– Neural networks are composed of multiple layers of interconnected neurons.
– The input layer receives the input data, the output layer produces the final predictions, and there can be one or more hidden layers in between.
– Hidden layers enable the network to learn complex representations of the data by extracting relevant features.
3. Weights and Biases:
– Each connection between neurons in a neural network is associated with a weight.
– Weights determine the strength of the connection and control the impact of one neuron’s output on another’s input.
– Biases are additional parameters associated with each neuron, allowing them to introduce a shift or offset in the computation.
4. Activation Functions:
– Activation functions introduce non-linearity to the computations of neurons.
– They determine whether a neuron should be activated or not based on its input.
– Common activation functions include sigmoid, tanh, ReLU (Rectified Linear Unit), and softmax.
5. Feedforward Propagation:
– Feedforward propagation is the process of passing the input data through the network’s layers to generate predictions.
– Each layer performs computations based on the inputs received from the previous layer, applying weights, biases, and activation functions.
– The outputs of one layer serve as inputs to the next layer, progressing through the network until the final predictions are produced.
6. Backpropagation:
– Backpropagation is an algorithm used to train neural networks.
– It calculates the gradients of the loss function with respect to the network’s weights and biases.
– Gradients indicate the direction and magnitude of the steepest descent, guiding the network’s parameter updates to minimize the loss.
– Backpropagation propagates the gradients backward through the network, layer by layer, using the chain rule of calculus.
7. Training and Optimization:
