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Lesson 7 of 27By Avijeet Biswal
Every deep learning system you interact with, whether it recommends a show, understands a voice command, or generates an image, runs on one of a handful of core algorithm types. Knowing which type does what makes it far easier to understand how modern AI actually works, and which approach fits a given problem.
This guide walks through what a deep learning algorithm actually is, the 10 types worth knowing, real named examples of each, how the underlying process works step by step, and where these algorithms are headed in 2026.
A deep learning algorithm is a neural network built from many stacked layers, which is where the word "deep" comes from. Each layer takes the previous layer's output and extracts a slightly more abstract feature. In an image, the first layer might pick up edges, the next layer might combine those edges into shapes, and a later layer might recognize a full object.
The defining trait of deep learning is that it skips manual feature engineering. Traditional machine learning usually needs a person to decide which characteristics of the data matter before training even starts. Deep learning algorithms learn those characteristics on their own, directly from raw images, audio, or text, which is why they scale so well with large, messy, real-world datasets.
Training happens in one of two broad modes.
Each deep learning algorithm is built around a different kind of data or problem.
CNNs are the standard choice for image and video data. They slide small filters across an image to detect low-level patterns like edges and textures, then stack those patterns into increasingly complex shapes across layers. This architecture powers facial recognition, X-ray analysis, and object detection in self-driving cars. AlexNet and ResNet are two of the most well-known CNN-based models.
RNNs handle sequential data, meaning data where order matters, such as text, audio, or time series. They carry a form of memory forward from one step to the next, so a prediction can depend on what came earlier in the sequence. RNNs work well for short sequences such as basic speech commands or short-term trend forecasting, but they lose track of information over long sequences.
LSTMs are a variant of RNNs built specifically to solve the long sequence problem. They use internal gates that decide what information to keep and what to discard as a sequence gets longer, which lets them retain context across many steps. LSTMs are common in machine translation, text generation, and time series forecasting such as stock price prediction.
A GAN is actually two networks trained against each other. One network, the generator, tries to produce realistic fake data. The other, the discriminator, tries to tell real data from fake. As they compete, both improve, and the generator eventually produces highly realistic output. GANs power AI art tools, image upscaling, and synthetic data generation for training other models.
Transformers process an entire sequence at once instead of one step at a time, using a mechanism called attention to weigh which parts of the input matter most to each other. This makes them far better than RNNs at understanding long-range relationships in language. BERT, GPT, and T5 are all transformer-based, and together they power most of today's chatbots, translation tools, and AI writing assistants.
An autoencoder compresses input data down into a smaller representation and then reconstructs it back to the original. The compression step forces the network to learn only the most important features of the data. This makes autoencoders useful for denoising images, detecting anomalies, and extracting features to feed into other models.
A VAE works like a standard autoencoder but adds a generative twist: instead of just compressing and reconstructing, it learns a smooth, structured representation of the data that can be sampled from to generate entirely new examples. VAEs are used in image generation, anomaly detection, and recommendation systems where understanding the underlying structure of the data matters.
GNNs are built for data structured as a network of connections rather than a flat table or sequence; think users and their relationships, or molecules and their bonds. Instead of looking at individual data points in isolation, GNNs learn from how nodes are connected. They show up in fraud detection, drug discovery, and the recommendation systems behind social platforms.
DBNs stack several simpler networks and train each layer separately before fine-tuning the whole thing together. This layer-by-layer, mostly unsupervised approach was one of the earliest demonstrations that stacking layers could help a model learn genuinely complex patterns, and it laid groundwork for much of modern deep learning.
An MLP is the most basic deep learning architecture: layers of neurons, each fully connected to every neuron in the next layer. It's not built specifically for images or sequences. Still, it's a solid, simple starting point for classification and prediction tasks like spam detection or sales forecasting when the data isn't especially complex.
Algorithm
Data Type It Handles Best
Real World Example
CNN
Images and video
Facial recognition, medical imaging, self-driving object detection
RNN
Short sequences
Basic speech commands, short-term forecasting
LSTM
Long sequences
Translation, text generation, stock price prediction
GAN
Generating new data
AI art tools, synthetic training data, image upscaling
Transformer
Language and long-range context
BERT and GPT-powered chatbots and translators
Autoencoder
Compression and anomaly detection
Image denoising, feature extraction
VAE
Generative modeling
Image generation, recommendation systems
GNN
Connected or relational data
Fraud detection, drug discovery, social recommendations
DBN
Layer-wise pretraining
Foundational architectures, early deep learning research
MLP
Simple structured data
Spam filtering, basic sales prediction
An algorithm type is a blueprint. A model is what you get once that blueprint is trained on real data for a real task, and this distinction is worth understanding because "algorithm" and "model" get used almost interchangeably even though they aren't quite the same thing.
Here's what that looks like with actual named systems:
The main types are Convolutional Neural Networks for images, Recurrent Neural Networks and LSTMs for sequential data, Generative Adversarial Networks and Variational Autoencoders for generating new data, Transformers for language, Graph Neural Networks for connected data, Deep Belief Networks, and Multilayer Perceptrons.
AlexNet and ResNet are named CNN models used for image classification. BERT and GPT are named transformer models used in chatbots and language tools. Each is a trained instance of a broader algorithm type.
An algorithm is the underlying architecture and learning method, like a CNN or a Transformer. A model is that algorithm after it has been trained on a specific dataset for a specific task, such as ResNet or GPT.
Multilayer Perceptrons are the simplest starting point, since they use a straightforward layered structure without the added complexity of convolution, attention, or recurrence.
Convolutional Neural Networks are the standard choice, since they are built specifically to detect visual patterns like edges, textures, and shapes across layers.
Traditional machine learning usually requires a person to select which data features matter before training. Deep learning algorithms learn those features directly from raw data.
Deep learning algorithms are a subset of neural networks, specifically the ones with many stacked layers. A basic neural network with only one or two layers is not usually considered deep learning.
Generally yes, since most architectures improve with more training data. Transfer learning, where a model is pretrained on a large dataset and then fine-tuned on a smaller one, is the common workaround when large datasets aren't available.
Backpropagation, gradient-based optimization, dropout, batch normalization, and transfer learning are among the most widely used techniques across nearly all deep learning architectures.
Start with the data type: images point toward CNNs, sequences toward RNNs or LSTMs, language toward Transformers, and connected or relational data toward GNNs. Begin with the simplest architecture that fits the data, and move to a more complex one only if performance genuinely requires it.
Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.
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