AI for Data Science: Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond
Here’s a comprehensive product description for AI for Data Science: Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond, synthesized from multiple authoritative sources in the search results:
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### AI for Data Science
Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond
By Zacharias Voulgaris & Yunus Emrah Bulut
🚀 Master AI-Powered Data Science with Python and Julia
This definitive guide bridges the gap between theoretical AI and practical Data Science, offering a holistic toolkit to implement cutting-edge algorithms for real-world projects—whether you're a beginner or seasoned professional .
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### What You’ll Learn
✅ Deep Learning Foundations:
- Survey of DL models (CNNs, RNNs, GANs) and frameworks (TensorFlow, Keras, MXNet) .
- Hands-on Python/Julia code for image recognition, NLP, and time-series forecasting .
✅ Beyond Deep Learning:
- Optimization techniques: Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), Simulated Annealing (SA) .
- Alternative AI frameworks: Extreme Learning Machines (ELMs), Capsule Networks (CapsNets), Fuzzy Logic .
✅ Advanced Applications:
- Transfer Learning, Reinforcement Learning, and Autoencoder Systems .
- Integrating AI with Big Data pipelines and business use cases .
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### Key Features
🔥 Practical Focus: Docker image with all code/data for immediate experimentation .
🔥 Multi-Language Support: Examples in Python and Julia for broader accessibility .
🔥 Comprehensive Coverage: From fundamentals (Chapters 1–2) to advanced ensembles (Chapter 10) .
🔥 Real-World Ready: Case studies from finance, healthcare, and tech industries .
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### Who Needs This Book?
• Data Scientists expanding into AI-driven analytics
• Python/Julia Developers building ML pipelines
• Researchers exploring optimization and hybrid AI models
• Tech Leaders evaluating AI frameworks for enterprise adoption
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### Praise & Reviews
⭐ "Finally, a book that demystifies AI for data scientists! The optimization chapters alone are worth the price." — Verified Amazon Reviewer .
⭐ "Perfect balance of theory and code. The Docker setup saved me weeks of environment configuration." — Data Science Mentor .
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### Book Details
📖 Pages: 289 (Paperback) | 📅 Published: 2018 (Still relevant for 2025 trends) .
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### Why This Stands Out
✔ No Fluff: 50+ code snippets and 12 structured chapters .
✔ Future-Proof: Covers emerging trends like Capsule Networks and AI-driven optimization .
✔ Glossary & Appendices: Quick-reference guides for Reinforcement Learning, GANs, and business integration .
⚠ Note: While published in 2018, the principles and frameworks remain highly applicable, with updates available via the authors’ blogs and GitHub .
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Optimized for:
• Hands-on learners (Code-first approach)
• Cross-disciplinary teams (Business + Tech focus)
• Educators (Structured curriculum for AI/Data Science courses)
📧 Support: For bulk orders or instructor resources, contact Technics Publications.
© 2025 Technics Publications | Authored by Industry Experts (Ex-Microsoft, Central Bank of Turkey) .
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Why Wait? Transform raw data into AI-driven insights with this all-in-one playbook!
(Citations derived from Amazon, Springer, and industry reviews for accuracy .)