Computer engineering – discipline that integrates several fields of electrical engineering and computer science required to develop computer hardware and software. Computer engineers usually have training in electronic engineering (or electrical engineering), software design, and hardware-software integration instead of only software engineering or electronic engineering. Computer engineers are involved in many hardware and software aspects of computing, from the design of individual microcontrollers, microprocessors, personal computers, and supercomputers, to circuit design. This field of engineering not only focuses on how computer systems themselves work, but also how they integrate into the larger picture.
- Main articles on computer engineering
- History of computer engineering
- Hardware
- Software
- System design
- Interdisciplinary fields
- Time line of computing 2400 BC - 1949 - 1950-79 - 1980-89 - 1990-99 - 2000-09
- History of computing hardware up to third generation (1960s)
- History of computing hardware from 1960s to current
- History of computer hardware in Soviet Bloc countries
- History of personal computers
- History of laptops
- History of software engineering
- History of compiler writing
- History of the Internet
- History of the World Wide Web
- History of video games
- History of the graphical user interface
- Timeline of computing
- Timeline of operating systems
- Timeline of programming languages
- Timeline of artificial intelligence
- Timeline of cryptography
- Timeline of algorithms
- Timeline of quantum computing
- Timeline of DOS operating systems
- Classic Mac OS
- History of macOS
- History of Microsoft Windows
- Timeline of Apple II family
- Timeline of Apple products
- Timeline of file sharing
- Timeline of OpenBSD
- Digital electronics
- Very-large-scale integration
- Hardware description language
- Application-specific integrated circuit
- Electrical network
- Microprocessor
- Scikit-learn: Machine Learning in Python
- Caffe: Convolutional Architecture for Fast Feature Embedding
- MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
- TensorFlow: A system for large-scale machine learning
- Adversarial Machine Learning at Scale
- Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
- AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
- Politeness Transfer: A Tag and Generate Approach
- DeepFaceLab: Integrated, flexible and extensible face-swapping framework
- XGBoost: A Scalable Tree Boosting System
- Neural Machine Translation by Jointly Learning to Align and Translate
- Adam: A Method for Stochastic Optimization
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Towards a Human-like Open-Domain Chatbot
- Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
- Generative Adversarial Nets
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Large-scale Video Classification with Convolutional Neural Networks
- A Review on Multi-Label Learning Algorithms
- Mastering the game of Go with deep neural networks and tree search