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26 Funktionsweise von KI

26 Funktionsweise von KI

64 items under this folder.

Oct 16, 2026
History of AI: from Turing to language models
Oct 16, 2026
AI evaluation: tests, benchmarks and LLM-as-judge
Oct 16, 2026
Federated learning and differential privacy
Oct 16, 2026
Interpretability: what happens inside the network?
Oct 16, 2026
Training data, copyright and privacy in AI
Oct 16, 2026
How AI understands text: NLP tasks
Oct 16, 2026
Computer vision: tasks and methods
Oct 16, 2026
Deepfakes and AI detection
Oct 16, 2026
Recommender systems
Oct 16, 2026
Autoencoders and VAE
Oct 16, 2026
GANs: generator versus discriminator
Oct 16, 2026
In-context learning: zero-shot and few-shot
Oct 16, 2026
Word2Vec and the history of embeddings
Oct 16, 2026
Inference servers: batching, PagedAttention and vLLM
Oct 16, 2026
AI frameworks: PyTorch, TensorFlow and JAX
Oct 16, 2026
Why GPUs for AI?
Oct 16, 2026
Maths basics for AI: vectors, matrices, tensors
Oct 16, 2026
Probability, entropy and KL divergence
Oct 16, 2026
EU AI Act and rules for AI
Oct 16, 2026
Limits of AI: what models cannot do
Oct 16, 2026
Prompt injection
Oct 16, 2026
Safety, alignment and jailbreaks
Oct 16, 2026
Vector databases and similarity search
Oct 16, 2026
Tool use, agents and MCP
Oct 16, 2026
Image generation with diffusion models
Oct 16, 2026
Model compression: distillation and pruning
Oct 16, 2026
Speech and audio: speech recognition and synthesis
Oct 16, 2026
Training at scale: GPUs, parallelism and memory
Oct 16, 2026
Vision transformer and CLIP
Oct 16, 2026
Encoder, decoder and the model families (BERT, GPT, T5)
Oct 16, 2026
Instruction tuning and chat format
Oct 16, 2026
Reasoning models and chain of thought
Oct 16, 2026
RLHF and preference optimisation
Oct 16, 2026
Scaling laws
Oct 16, 2026
Tokenisation: BPE and vocabulary
Oct 16, 2026
How a language model generates text: step by step
Oct 16, 2026
How a language model is trained
Oct 16, 2026
The transformer block in detail
Oct 16, 2026
Convolutional networks (CNN) for images
Oct 16, 2026
Initialisation, normalisation and dropout
Oct 16, 2026
Multi-head attention and variants
Oct 16, 2026
Positional encoding and RoPE
Oct 16, 2026
Recurrent networks and LSTM
Oct 16, 2026
Self-attention in detail
Oct 16, 2026
Skip connections and residual networks
Oct 16, 2026
Forward and backward pass in detail
Oct 16, 2026
Batch, epoch and learning rate
Oct 16, 2026
Decision trees and random forests
Oct 16, 2026
Principal component analysis (PCA)
Oct 16, 2026
k-nearest neighbours, k-means and clustering
Oct 16, 2026
Metrics: accuracy, precision, recall and F1
Oct 16, 2026
Neurons, layers and activation functions
Oct 16, 2026
Optimisers: SGD, momentum and Adam
Oct 16, 2026
Softmax and probabilities
Oct 16, 2026
Loss functions: MSE and cross-entropy
Oct 16, 2026
Reinforcement learning
Oct 16, 2026
Bias, fairness and data quality
Oct 16, 2026
Data, features and labels
Oct 16, 2026
Classification and regression
Oct 16, 2026
Kinds of learning: supervised, unsupervised, reinforcement
Oct 16, 2026
Linear and logistic regression
Oct 16, 2026
Training, validation and test
Oct 16, 2026
What is machine learning?
Oct 16, 2026
How AI works: overview and map

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