Preliminary Machine Learning Concepts

Mastering the core principles of neural networks and their variants is crucial for designing large-scale GenAI systems capable of tasks like text, image, speech, and video generation. In this lesson, we will explore the following foundational concepts:

• Neural networks

• Convolutional neural networks (CNNs)

• Recurrent neural networks (RNNs)

• Transformers network

• Attention mechanisms

These machine-learning concepts are the backbone of modern GenAI systems, which allow machines to learn patterns, generate creative outputs, and scale efficiently. By understanding these concepts, we can better design and optimize the complex System Designs required for real-world GenAI applications.

Let’s describe each of the above concepts, starting with neural networks.

Neural network architecture

Neural networks are computational models inspired by the human brain. They are designed to recognize patterns and make predictions by processing data through interconnected layers (discussed below) of nodes (also known as neurons). Neural network architecture refers to the structure and organization of a neural network, including the arrangement of its layers, nodes (neurons), and connections. It defines how the data flows through the network, learns, and makes predictions or decisions.

Let’s discuss the essential components of a neural network.

Components of neural network

Here are the key components of neural network architecture, though we will focus on only a few in this discussion:

• Neurons: The basic processing unit of a neural network is a neuron or a node. Each neuron takes a feature vector such as $(\text{x}_{1}, \text{x}_{2},\text{ . . . } ,\text{x}_{\text{m}})$, multiplies it with corresponding weights such as $(\text{w}_{1}, \text{w}_{2},..., \text{w}_{\text{m}})$, adds a bias, and sums all of them, and the result is passed through an activation function ($\sigma$) to produce nonlinearityNon-linearity in a neural network refers to the ability of the model to capture and represent complex relationships in data that cannot be described by a straight line or simple equation. It allows the network to learn patterns, such as curves or interactions, which are essential for tasks like image recognition and natural language processing.. The mathematical formulation is as follows: