What Is Deep Learning? Neural Networks Explained

Deep learning is the branch of machine learning behind most of today's advances in language, vision and speech. This guide explains what neural networks are, how they learn, what the main architectures are for, and when a simpler method is the better choice.

What is deep learning?

The standard textbook by Goodfellow, Bengio and Courville describes the idea this way: the solution is "to allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts." They add that "the hierarchy of concepts enables the computer to learn complicated concepts by building them out of simpler ones." Drawn as a graph, that hierarchy has many layers, which is where the word "deep" comes from.

Key takeaways

• Deep learning is machine learning that uses neural networks with many layers.
• Its main advantage is learning useful features from raw data, instead of people designing them by hand.
• It usually needs large amounts of data and computing power.
• It is strongest on unstructured data such as images, audio and text.
• Its main drawbacks are cost, data hunger and difficulty explaining individual answers.

How does a neural network work?

A neural network is made of layers of simple units. Each unit takes numbers in, multiplies them by weights, adds them up and passes the result through a simple function. The first layer receives the raw input, such as the pixels of an image or the tokens of a sentence. Each following layer builds on the one before, so early layers pick up simple patterns and later layers combine them into more complex ones.

How does a network learn?

• Forward pass: the network makes a prediction from an example.
• Loss: a number measures how wrong the prediction was.
• Backpropagation: the error is traced backwards through the layers to see how each weight contributed.
• Gradient descent: each weight is nudged in the direction that reduces the error.
• Repeat: over many examples and many passes, until performance on unseen data stops improving.

How is deep learning different from other machine learning?

In classic machine learning, people often design the features a model looks at. The Deep Learning textbook points out why that matters: a representation learning algorithm "can discover a good set of features for a simple task in minutes, or for a complex task in hours to months," whereas "manually designing features for a complex task requires a great deal of human time and effort; it can take decades for an entire community of researchers." Deep learning automates much of that step. The broader field is covered in What Is Machine Learning?.

What are the main types of neural network?

• Convolutional networks: designed for grid-like data, most often images.
• Recurrent networks: designed to read sequences step by step, such as text or audio, and used widely before newer designs.
• Transformers: introduced in 2017 in "Attention Is All You Need", which proposed a network "based solely on attention mechanisms". They underpin today's large language models (see LLM).

Where is deep learning used?

• Recognizing objects and faces in images, and reading documents.
• Speech recognition and speech synthesis.
• Machine translation, summarization and question answering.
• Generating text, images and code (see What Is Generative AI?).
• Search and recommendation, where meaning matters more than exact keywords (see embeddings).

What are the limits of deep learning?

• Data and compute: large models need large datasets and specialized hardware.
• Opacity: it can be hard to explain why a network gave a particular answer.
• Brittleness: a model can fail on inputs unlike its training data.
• Cost and energy: training and running large models is expensive.
• Not always necessary: for small, structured datasets, simpler methods are often easier to build, explain and maintain.

How do you decide whether you need deep learning?

• Is your data mostly unstructured (images, audio, text)?
• Do you have enough examples, or can you start from an existing pretrained model?
• Does an answer need to be explained, and if so, is that possible with this model?
• Would a simpler model be good enough, and cheaper to run?

Where Zunkiree Labs fits

Zunkiree Labs builds custom AI systems (including RAG pipelines, LLM integration and intelligent automation), data systems, custom software, and web and mobile applications. Whether a project needs deep learning, a language model with retrieval, or something simpler is a design question we start with. The related RAG and fine-tuning approaches are explained in our glossary. The full list is on the services page, and the AI Development service covers custom AI systems in more detail.

Keep reading

• What Is Machine Learning? — the foundation: how systems learn from data, with NLP, neural networks and LLMs explained
• What Is Generative AI? — how it works, where it helps, and where it fails
• Ethical AI — principles, risks and how organizations apply them
• Natural Language Processing Fundamentals — our guide to NLP
• How to Build a RAG Pipeline — a step-by-step engineering guide

Sources

• Goodfellow, Bengio and Courville, Deep Learning, chapter 1 (Introduction)
• Vaswani et al., Attention Is All You Need (arXiv, 2017)