Introduction
In this section, we will explore the fundamentals of machine learning
Terminology
- Model: Representation of the learned patterns in the data.
Learning Methods for Machine Learning
- Supervised Learning: Learning a mapping from inputs to outputs using
Convolutional Neural Networks
CNN (Convolutional Neural Networks) are a class of deep learning models
Problems
- Underfitting: The model is too simple to capture the underlying
Neuron
Neuron or Perceptron is the fundamental building block of neural networks.
Pooling
Pooling is a technique used to reduce the spatial dimensions of feature map
Transformer
The ubiquitous transformer was introduced in the paper
Data
Data is a crucial component in deep learning, as it serves as the
Transfer Learning
Transfer learning is a machine learning technique where a model
ML Components
Each machine learning algorithm consists of the following components:
Normalization
Normalization is a technique used to scale input features to a
Regression
It's a statistical method used in machine learning to model the