SKU/Artículo: AMZ-B0CCCWXQ33
Data Scientist: Data Science in the Real World - Deep Dive into Advanced Topics and Emerging Trends
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.29 kg
0.29 kg
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Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- "Data Scientist: Data Science in the Real World - Deep Dive into Advanced Topics and Emerging Trends" is a comprehensive guide that takes data scientists on an exploratory journey through the cutting-edge realms of data science. This book goes beyond the basics, providing a deep dive into advanced concepts and emerging trends that are shaping the field. From deep learning and neural networks to natural language processing (NLP) advances, the book covers a wide range of advanced topics. Readers will discover the inner workings of various neural network architectures, including convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN). They will also explore transfer learning, domain adaptation, and reinforcement learning techniques such as deep Q-networks. The book delves into the exciting field of NLP advancements, including word embeddings, contextual word representations, and transformer models like BERT and GPT. It explores the fascinating world of text generation and dialogue systems, showcasing how NLP is transforming the way we interact with machines. Graph analytics and network science are explored in detail, covering graph representation, centrality measures, community detection, and graph neural networks. Readers will gain insights into link prediction and its applications in various domains. Bayesian methods and probabilistic programming are demystified, providing a solid understanding of Bayesian inference, probabilistic models, and Markov Chain Monte Carlo (MCMC) methods. The book introduces popular probabilistic programming frameworks such as Stan and PyMC3. Advanced reinforcement learning techniques are covered, including policy gradient methods, actor-critic algorithms, and model-based reinforcement learning. Readers will discover how these methods enable intelligent decision-making in complex environments. Causal inference and causal discovery take center stage, exploring counterfactual reasoning, causal effects, and methods such as propensity score matching and instrumental variables. The book also guides readers in discovering causal relationships from observational data. Privacy-preserving data analysis is a crucial concern in today's data-driven world. The book introduces differential privacy and its applications, secure multiparty computation, and federated learning. It also explores homomorphic encryption as a means to protect sensitive data. The book equips readers with the tools and techniques for time series analysis in the real world. It covers Long Short-Term Memory (LSTM) networks for sequence modeling, multivariate time series analysis, and forecasting with uncertain data and dynamic models. Explainability and interpretability in machine learning are crucial for building trust and understanding in predictive models. The book explores various interpretability techniques such as LIME and SHAP, as well as rule-based models and model-agnostic methods. The book concludes with an exploration of emerging trends in data science, including edge computing and IoT analytics, quantum machine learning, and ethical considerations in emerging technologies. It reflects on the evolving landscape of data science and its future directions. Whether you're an experienced data scientist or an aspiring one, "Data Scientist: Data Science in the Real World - Deep Dive into Advanced Topics and Emerging Trends" is an invaluable resource that will broaden your knowledge, keep you up to date with the latest advancements, and empower you to tackle complex data challenges in the real world.
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AR$61.049
49% OFF
AR$31.309
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