AI Geek Programmer Blog
Machine learning, computer vision, deep learning and artificial intelligence engineering.
Machine learning uses algorithms; deep learning uses neural networks
- AI Geek Programmer
- 14 November 2026
What is the real difference between machine learning and deep learning?
Read article
AIMA defines AI as rational agents maximizing expected utility
- AI Geek Programmer
- 14 November 2026
AIMA does define AI through rational agents, and the cleanest short version is this: an AI system is an agent that acts to get the best expected outcome under.
Read article
AI systems are prime targets for new cyber threats
- AI Geek Programmer
- 13 November 2026
AI systems are prime targets for new cyber threats because they hold valuable data and make decisions through complex software.
Read article
Neural networks learn by adjusting connection weights
- AI Geek Programmer
- 13 November 2026
Neural networks learn by adjusting connection weights.
Read article
Neural networks use layers to process data
- AI Geek Programmer
- 12 November 2026
A neural network works by taking raw data and passing it through layers that each do one small job.
Read article
AI systems require specialized software development skills
- AI Geek Programmer
- 12 November 2026
AI systems require specialized software development skills. A model is only one part of the system.
Read article
No detector can prove AI content from text alone in every case
- AI Geek Programmer
- 11 November 2026
Detecting artificial intelligence means looking for signs that a model created or changed content.
Read article
Michael Nielsen’s Neural Networks Book Explains Deep Learning
- AI Geek Programmer
- 11 November 2026
Michael Nielsen’s Neural Networks and Deep Learning does explain deep learning, and it does it in a plain way that still respects the math.
Read articleTags
- machine learning
- artificial intelligence
- deep learning
- shape recognition
- python
- keras
- convolutional neural networks
- numpy
- cifar10
- convnets
- tensorflow
- ai
- overfitting
- anaconda
- mnist
- pytorch
- data augmentation
- development environment
- dropout
- GPU
- nlp
- scikit-learn
- transformer architecture
- batch normalization
- blockchain
- digits dataset
- google colab
- huggingface
- internal covariate shift
- k-nn