Atlas · GenAI 2026
Deep Learning
Deep learning fundamentals (backprop, regularization)
conceptPeak: 2016Deep Learning FundamentalsAI consensus: 2/3
Prerequisites
Backpropagation IS the chain rule of calculus applied through a computation graph — without understanding optimization, DL is a black box
- hardLinear Algebra
Neural networks are compositions of matrix multiplications, vector transformations, and nonlinearities — linear algebra is their native language
Recommended reference
Prince, S. (2023) Understanding Deep Learning — free at udlbook.com; most modern comprehensive DL textbook. Alt: Goodfellow et al. (2016) Deep Learning
Notes from AI deep research
Anthropic Opus
Prince (2023) UDL najnowoczesniejszy podrecznik. Fundamenty ktore nie starzeja sie
OpenAI Deep Research
Debugowanie fine-tuningu [OA#4]
Related skills
- ← is subcategory of: Convolutional Neural Networks(3/3)
- → is subcategory of: Machine Learning(3/3)
- ← is subcategory of: Diffusion Models(3/3)
- ← is subcategory of: Graph Neural Networks(3/3)
- ← is subcategory of: Large Language Models (LLM)(3/3)
- ← is subcategory of: Mixture of Experts(3/3)
- ← is an instance of: PyTorch(3/3)
- ← is subcategory of: Recurrent Neural Networks(3/3)
- ← is subcategory of: State Space Models(3/3)
- ← is an instance of: TensorFlow(3/3)
- ← is subcategory of: Transformer Architecture(3/3)
- → is subcategory of: Data Science(2/3)
- ← is part of: Linear Algebra(2/3)
- ← is an instance of: Graph Neural Networks(1/3)
- ← is subcategory of: Computer Vision(1/3)
- ← is subcategory of: Edge AI(1/3)
- ← is part of: Information Theory(1/3)
- ← is subcategory of: Multimodal AI(1/3)
- ← is subcategory of: NLP(1/3)
- ← is part of: Mathematical Optimization(1/3)