1.3 AI, machine learning and deep learning

Key points

1. A neural network is a model built from layers of simple connected units. NVIDIA says 'deep' represents the many layers of algorithms, or neural networks, used to recognize patterns in data.

   What NVIDIA says (1)

   “The word "deep" in deep learning represents the many layers of algorithms, or neural networks, that are used to recognize patterns in data.”

   — What Is Deep Learning and Why Does It Matter?

2. Artificial intelligence (AI) is the broad field. NVIDIA describes machine learning as a subset of AI that uses algorithms to parse data, learn from it, and make predictions. Deep learning is a subset of machine learning that can learn representations from data such as images, video or text without human domain knowledge.

   What NVIDIA says (2)

   “As a subset of AI, machine learning in its most elemental form uses algorithms to parse data, learn from it, and then make predictions or determinations about something in the real world.”

   — What is Machine Learning and Why Does It Matter?

   “Deep learning is a subset of machine learning, with the difference that DL algorithms can automatically learn representations from data such as images, video, or text, without introducing human domain knowledge.”

   — What Is Deep Learning and Why Does It Matter?

3. Generative AI models use neural networks to identify patterns and structures in existing data. They then generate new and original content. NVIDIA says inputs and outputs can include text, image, audio, video and code.

   What NVIDIA says (2)

   “Generative AI models use neural networks to identify the patterns and structures within existing data to generate new and original content.”

   — What is Generative AI and How Does it Work?

   “Generative AI models can take inputs such as text, image, audio, video, and code and generate new content into any of the modalities mentioned.”

   — What is Generative AI and How Does it Work?

4. A parameter is one of the learned numbers inside a model. NVIDIA says LLMs are trained on internet-scale datasets with hundreds of billions of parameters. That scale has unlocked the ability to generate human-like content.

   What NVIDIA says (1)

   “However, large language models, which are trained on internet-scale datasets with hundreds of billions of parameters, have now unlocked an AI model’s ability to generate human-like content.”

   — What are Large Language Models?

5. NVIDIA describes training as passing data through layered connections where each neuron assigns weights to its input. The weights are adjusted based on feedback about whether the output was right or wrong. The trained model is, in effect, that balanced set of weights.

   What NVIDIA says (2)

   “Training a neural network, unlike human learning, involves passing data through layered connections where each neuron assigns weights and adjusts based only on “right” or “wrong” feedback.”

   — What's the Difference Between Deep Learning Training and Inference?

   “Now you have a data structure and all the weights in there have been balanced based on what it has learned as you sent the training data through.”

   — What's the Difference Between Deep Learning Training and Inference?

6. A transformer is the neural network design behind most modern LLMs. NVIDIA says transformers use attention, or self-attention, to detect how even distant elements in a series influence each other. The math transformers use lends itself to parallel processing, so they run fast on parallel hardware like GPUs.

   What NVIDIA says (2)

   “Transformer models apply an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other.”

   — What Is a Transformer Model?

   “In addition, the math that transformers use lends itself to parallel processing, so these models can run fast.”

   — What Is a Transformer Model?

Key terms

• Artificial intelligence: Software that performs tasks that normally need human intelligence; machine learning and deep learning are subsets of it.
• Machine learning: A subset of AI where algorithms learn from data to make predictions instead of following hand-written rules.
• Deep learning: A subset of machine learning that uses neural networks with many layers to learn patterns directly from data such as images or text.
• Generative AI: AI models that learn patterns in existing data and use them to create new content such as text, images or code.
• Large language model: A very large neural network trained on huge text datasets that can generate human-like text.
• Transformer: A neural network design that uses attention to relate every part of an input to every other part, and runs well in parallel.

Try it

• AI, ML & DL Foundations: simulator lab

Sample question

What does the word 'deep' in deep learning refer to?