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- Autorius: Jesus Rogel-Salazar
- Leidėjas: Taylor & Francis Ltd
- Metai: 2026
- Puslapiai: 632
- ISBN-10: 1041102852
- ISBN-13: 9781041102854
- Formatas: 19.1 x 23.5 x cm, minkšti viršeliai
- Kalba: Anglų
- Kaina galioja įvedus kodą: ENG
Advanced Data Science and Analytics with Python (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
The second edition of Advanced Data Science and Analytics with Python reflects the rapid transformation of artificial intelligence in recent years. While preserving its practical, modular structure, this edition significantly expands coverage of the techniques shaping modern AI practice.
The deep learning chapter has been substantially broadened to include reinforcement learning and generative adversarial networks, alongside a fully developed exploration of transformer architectures. Generative AI now takes centre stage, with dedicated coverage of self-attention, BERT, GPT, large language model evaluation and API-based interaction. Emerging agentic systems are introduced as part of the evolving AI landscape. Natural language processing has been enhanced with word embeddings, contextual representations and vector search, while network analysis now includes graph representation learning and embedding techniques. The chapter on data product deployment has been strengthened with modern Core ML workflows and new coverage of on-device Foundation Models, bridging experimentation and production.
Fully updated for the contemporary Python ecosystem, this edition equips practitioners with the tools and architectural understanding required to design, build and deploy intelligent systems in today's AI-driven world.
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- Autorius: Jesus Rogel-Salazar
- Leidėjas: Taylor & Francis Ltd
- Metai: 2026
- Puslapiai: 632
- ISBN-10: 1041102852
- ISBN-13: 9781041102854
- Formatas: 19.1 x 23.5 x cm, minkšti viršeliai
- Kalba: Anglų
The second edition of Advanced Data Science and Analytics with Python reflects the rapid transformation of artificial intelligence in recent years. While preserving its practical, modular structure, this edition significantly expands coverage of the techniques shaping modern AI practice.
The deep learning chapter has been substantially broadened to include reinforcement learning and generative adversarial networks, alongside a fully developed exploration of transformer architectures. Generative AI now takes centre stage, with dedicated coverage of self-attention, BERT, GPT, large language model evaluation and API-based interaction. Emerging agentic systems are introduced as part of the evolving AI landscape. Natural language processing has been enhanced with word embeddings, contextual representations and vector search, while network analysis now includes graph representation learning and embedding techniques. The chapter on data product deployment has been strengthened with modern Core ML workflows and new coverage of on-device Foundation Models, bridging experimentation and production.
Fully updated for the contemporary Python ecosystem, this edition equips practitioners with the tools and architectural understanding required to design, build and deploy intelligent systems in today's AI-driven world.
Atsiliepimai