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Top Deep Learning Algorithms

A directory of ten deep learning algorithms, from the basic neural network to the Transformer, BERT and GPT, with 15 reference links to the original papers. Links go to each vendor's site.

24 topics•6 categories•Updated October 2026

The Ten Algorithms at a Glance

10 algorithms

Summarised from the source LinkedIn post; the reference links below point to the original papers.

#AlgorithmCore ideaBest for
1Artificial Neural Network (ANN)The basic building block: layers of weighted nodes trained by adjusting weights.General-purpose foundation for the other architectures.
2Convolutional Neural Network (CNN)Applies filters across spatial data to detect edges, textures and patterns.Images and video.
3Recurrent Neural Network (RNN)Passes information from one step to the next.Time series, speech and text; struggles with long-range dependencies.
4LSTMGating selectively retains or forgets information across a sequence, easing the vanishing-gradient problem in RNNs.Sequences with long-term dependencies.
5GRUA simpler, faster gated unit with fewer parameters and similar performance on many sequence tasks.Sequence tasks where efficiency matters.
6AutoencoderAn encoder compresses the input and a decoder reconstructs it.Dimensionality reduction, anomaly detection and generative tasks.
7GANA generator and a discriminator compete in an adversarial loop.Generating realistic synthetic data.
8TransformerSelf-attention relates every position of the input to every other at once.The architecture behind most modern language models.
9BERTEncoder-only Transformer trained to fill in masked words, reading in both directions.Understanding text; not generating it.
10GPTDecoder-only Transformer trained to predict the next token.Generating text.

The Building Block

1 entries

The network every other architecture on this page builds on.

01Artificial Neural Network (ANN)Layers of weighted nodes that learn patterns as training adjusts the weights. The 1986 Nature paper by Rumelhart, Hinton and Williams describes back-propagation, the procedure that adjusts weights to reduce the error at the output.

Vision

2 entries

Networks designed for images and other spatial data.

02Convolutional Neural Network (CNN)Applies filters across spatial data to detect edges, textures and patterns. LeCun and colleagues' 1998 paper on gradient-based learning for document recognition is a classic reference. 03AlexNet (ImageNet Classification with Deep CNNs)The 2012 NeurIPS paper by Krizhevsky, Sutskever and Hinton that trained a large deep CNN for ImageNet classification.

Sequence Models

4 entries

Networks that carry information from one step of a sequence to the next.

04Recurrent Neural Network (RNN)Handles sequential data by passing information from one step to the next, for time series, speech and text. Elman's 1990 paper "Finding structure in time" is a classic reference. The publisher blocks automated link checks. 05Long Short-Term Memory (LSTM)Uses gating to selectively retain or forget information, which helps with the long-range dependencies that plain RNNs struggle with. Hochreiter and Schmidhuber, 1997. The publisher blocks automated link checks. 06Gated Recurrent Unit (GRU)A simpler gated unit with fewer parameters than the LSTM, introduced by Cho and colleagues in 2014 in an RNN encoder-decoder for machine translation. 07Empirical Evaluation of Gated Recurrent Neural NetworksChung and colleagues' 2014 comparison, which found gated units such as LSTM and GRU better than traditional tanh units on music and speech modelling, and GRU comparable to LSTM.

Representation and Generation

3 entries

Networks that learn compact representations or generate new data.

08AutoencoderAn encoder compresses the input and a decoder reconstructs it, used for dimensionality reduction, anomaly detection and generative tasks. Hinton and Salakhutdinov, 2006. The publisher blocks automated link checks. 09Auto-Encoding Variational Bayes (VAE)Kingma and Welling's 2013 paper introducing the variational autoencoder, the generative form of the autoencoder. 10Generative Adversarial Network (GAN)Two networks compete: a generator creates data and a discriminator tries to tell it from real data. Goodfellow and colleagues, 2014.

The Transformer Family

4 entries

Self-attention models, and the two ways of using the architecture that underlie most language models.

11TransformerUses self-attention to relate every position of the input to every other, instead of processing step by step. "Attention Is All You Need" (Vaswani et al., 2017) proposed it, dispensing with recurrence and convolutions. 12BERTAn encoder-only Transformer pre-trained to predict masked words using context from both directions, which suits understanding tasks. Devlin and colleagues, 2018. 13GPT (Improving Language Understanding by Generative Pre-Training)A decoder-only Transformer pre-trained to predict the next token, which suits text generation. OpenAI's 2018 paper introduced generative pre-training followed by task fine-tuning. 14Language Models are Few-Shot Learners (GPT-3)The 2020 OpenAI paper on scaling a decoder-only Transformer to 175 billion parameters and prompting it with a few examples.

Source Post

1 entries

The LinkedIn post this page is based on.

15GPT, BERT and the Transformer are different things (LinkedIn)A LinkedIn post, by Arynn Raj according to the link, that maps ten deep learning algorithms (ANN, CNN, RNN, LSTM, GRU, autoencoder, GAN, Transformer, BERT and GPT) and separates the Transformer architecture from the BERT and GPT models built on it.

Related Pages

9 entries

Related directories on this site.

16Deep LearningFrameworks, platforms and datasets for deep learning. 17Machine Learning AlgorithmsSupervised, unsupervised and ensemble methods. 18Deep Learning FrameworksPyTorch, TensorFlow, JAX and others. 19Deep Learning PlatformsPlatforms for training and deploying models. 20Large Language ModelsDirectory of language models. 21Reinforcement Learning AlgorithmsAlgorithms for learning from rewards. 22Time Series Forecasting AlgorithmsSequence models for forecasting. 23Tuning the Temporal Fusion TransformerA Transformer variant for forecasting. 24Datasets for ML and DLDatasets for training models.

Frequently Asked Questions

Are GPT, BERT and the Transformer the same thing?

No. The Transformer is the architecture, built on self-attention. BERT and GPT are both models built on it but used differently: BERT is an encoder-only Transformer trained to fill in masked words and is strong at understanding text, while GPT is a decoder-only Transformer trained to predict the next token and is strong at generating text.

Which deep learning algorithm suits which data?

CNNs are the usual choice for images and video. RNNs, LSTMs and GRUs handle sequential data such as time series, speech and text, and Transformers handle sequences using self-attention. Autoencoders suit compression, dimensionality reduction and anomaly detection, and GANs suit generating realistic synthetic data. ANNs are the general-purpose foundation.

Why did Transformers take over from RNNs for language?

The Transformer paper proposed relying on attention alone, dispensing with recurrence, and reported that this was more parallelisable and faster to train. Recurrent models process a sequence step by step, which is harder to parallelise and struggles with long-range dependencies. RNN-family models are still used, especially where sequences are streamed or models must be small.

What is the difference between LSTM and GRU?

Both use gating to control what information is kept or forgotten. The GRU is simpler, with fewer parameters, and a 2014 empirical comparison found GRU comparable to LSTM on the tasks it tested, with both better than plain tanh units. The practical choice often comes down to efficiency and testing on your own data.

Is CNN still the dominant vision architecture?

The source post calls CNNs the dominant architecture for computer vision, and they remain widely used. Transformer-based vision models, such as Vision Transformers, are also common, so the right choice depends on data size, compute and the task.

What does this list leave out?

The post covers ten algorithms, so it does not cover other widely used approaches such as diffusion models for image generation, graph neural networks, state-space sequence models or mixture-of-experts Transformers. Treat the ten as a foundation, not the whole field.

How current is this page?

Links were checked on 23 October 2026. The publisher sites for the RNN, LSTM and autoencoder papers block automated checks, so those links may need a manual click. The algorithm descriptions follow the source post and are checked against the cited papers.