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Everything about Deep learning
Branch of machine learning
In machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and revolves around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be supervised, semi-supervised or unsupervised. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Deep learningA 5-minute orientation
- Skim a recent paper: Robust deep learning–based protein sequence design using ProteinMPNNInfluential work since 2022
- Pick up a classic book: Deep LearningThe most-reprinted book on the subject
- Read the landmark paper: Deep learningCited 79,303 times
- Go sideways: Long short-term memoryA closely related topic
- Go sideways: Recurrent neural networkA closely related topic
📖 Definitions
Noun A form of machine learning based on the interpretation of the structure of data.
🏆 Landmark papers (most cited)
Deep learning
Human-level control through deep reinforcement learning
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Deep learning in neural networks: An overview
A survey on deep learning in medical image analysis
A Fast Learning Algorithm for Deep Belief Nets
A survey on Image Data Augmentation for Deep Learning
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
🆕 Influential since 2022
📚 Books worth reading
Deep LearningIan Goodfellow, Yoshua Bengio · 2016- 📕Deep LearningShriram K. Vasudevan, Siniraj Pulari · 2021
Deep Learning in PracticeMehdi Ghayoumi · 2021
Deep Learning with RFrancois Chollet, J. J. Allaire · 2018
Machine TranslationThierry Poibeau · 2017
Hands-On Machine Learning with Scikit-Learn and PyTorchAurélien Géron · 2025
Introduction to AI Safety, Ethics, and SocietyDan Hendrycks · 2025
Fundamentals of Deep LearningNikhil Buduma · 2017
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Long short-term memory Long short-term memory (LSTM) is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem commonly encountered by traditional…
- Recurrent neural network In artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series, where…
- Types of artificial neural networks Types of neural networks (NN) include a family of techniques. The simplest types have static components, including number of units, number of layers, unit…
- History of artificial neural networks Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. While the computational implementations of ANNs…
- Jürgen Schmidhuber Jürgen Schmidhuber (born 17 January 1963) is a German computer scientist in the field of artificial intelligence, specifically artificial neural networks…
- Neural network (machine learning) In machine learning, a neural network (NN) or artificial neural network (ANN) is a computational model inspired by the structure and functions of biological…
- Echo state network An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer (with typically…
- Convolutional neural network A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep…