When it comes to machine learning, understanding the fundamental architectures is crucial for building effective models. At the heart of it all are the linear and convolutional layers (1D, 2D, etc.), which are the building blocks for more complex architectures.
These bigger architectural families include ResNets for image classification, UNet for segmentation, and many others. But what are the standard architectures that everyone ought to know?
In this post, we’ll explore the essential architectures in machine learning, covering their applications and uses. Whether you’re a beginner or an experienced practitioner, this primer will help you understand the foundation of machine learning models.
## Linear and Convolutional Layers
These are the fundamental units of machine learning architectures. Linear layers are used for tasks like image classification, while convolutional layers are used for image and signal processing.
## ResNets and Image Classification
ResNets, or Residual Networks, are a type of neural network architecture that have revolutionized image classification tasks. They’re composed of residual blocks, which allow the model to learn more complex features.
## UNet and Image Segmentation
The UNet architecture is specifically designed for image segmentation tasks, where the goal is to assign a class label to each pixel in an image. UNet’s unique architecture allows it to learn both local and global features.
## Other Architectures
There are many other architectures that are essential to know, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformers.
## Conclusion
Understanding the standard architectures in machine learning is crucial for building effective models. By knowing the fundamentals, you can tackle more complex tasks and build innovative solutions.
*Further reading: [Machine Learning Architectures](https://www.machinelearningmastery.com/machine-learning-architectures/)*