Deep Learning в игровых системах

Deep Learning — subset ML на многослойных нейросетях. Требует много данных и compute, но даёт state-of-art в complex tasks.

Архитектуры

MLP: baseline для tabular. CNN: images, video. RNN/LSTM: sequences (transaction history). Transformers: dominant для последовательностей и текста. Graph Neural Networks: relational data (fraud rings). Autoencoders: anomaly detection.

iGaming applications

Complex fraud detection: LSTM транзакций даёт 10–20% lift vs XGBoost. Recommendation: neural collaborative filtering, sequence-based. Live stream analysis: CNN для card recognition. Chat: LLMs для support. Voice: streaming в audio-based games.

Trade-offs vs classical ML

Pros: better на complex patterns, unstructured data (text, images). Cons: требует много данных (millions of examples). Compute expensive: GPUs. Longer training. Harder interpretability. Not always beneficial — tabular tasks обычно лучше XGBoost.

Infrastructure

GPUs: NVIDIA A100, H100 для training. CPUs для inference часто достаточно. Framework: PyTorch dominant. TensorFlow для production. ONNX для portability. Cloud training: AWS SageMaker, GCP Vertex. On-prem или reserved GPUs если постоянные workloads.