Pramod Singh

Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges. You'll start by reviewing PySpark fundamentals, such as Spark’s core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github. What You'll LearnDevelop pipelines for streaming data processing using PySpark Build Machine Learning & Deep Learning models using PySpark latest offerings Use graph analytics using PySpark Create Sequence Embeddings from Text data Who This Book is For Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data.

Build Python-based Machine Learning and Deep Learning Models

Learn PySpark

Kusum Deep

Millie Pant

Atulya K. Nagar

Jagdish C. Bansal

The book is a collection of high quality peer reviewed research papers presented in Seventh International Conference on Bio-Inspired Computing (BIC-TA 2012) held at ABV-IIITM Gwalior, India. These research papers provide the latest developments in the broad area of "Computational Intelligence". The book discusses wide variety of industrial, engineering and scientific applications of nature/bio-inspired computing and presents invited papers from the inventors/originators of novel computational techniques.

Volume 1

Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012)

Yaochu Jin

Hisao Ishibuchi

Ke Tang

Peter Whigham

Kay Chen Tan

Yuhui Shi

Xiaodong Li

Mengjie Zhang

Lam Thu Bui

Will N. Browne

Grant Dick

This volume constitutes the proceedings of the 10th International Conference on Simulated Evolution and Learning, SEAL 2012, held in Dunedin, New Zealand, in December 2014. The 42 full papers and 29 short papers presented were carefully reviewed and selected from 109 submissions. The papers are organized in topical sections on evolutionary optimization; evolutionary multi-objective optimization; evolutionary machine learning; theoretical developments; evolutionary feature reduction; evolutionary scheduling and combinatorial optimization; real world applications and evolutionary image analysis.

10th International Conference, SEAL 2014, Dunedin, New Zealand, December 15-18, Proceedings

Simulated Evolution and Learning

The book lays emphasis on rural reconstruction which is the need of the date as 76 per cent of India's population dwells in rural areas. For the sustainable development we should integrate both science and spritualism. Natural and human resources are swiftly depleting due to lack of proper planning and non-cooperation amongst the scientists, society and NGO's.

Rural Reconstruction Ecosystem and Forestry

Pramod Singh

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