BOOKS - TECHNICAL SCIENCES - Machine Learning Applications in Non-conventional Machin...
Machine Learning Applications in Non-conventional Machining Processes - Goutam Kumar Bose (Editor), Pritam Pain (Editor) 2021 PDF Igi Global BOOKS TECHNICAL SCIENCES
ECO~15 kg CO²

1 TON

Views
73714

Telegram
 
Machine Learning Applications in Non-conventional Machining Processes
Author: Goutam Kumar Bose (Editor), Pritam Pain (Editor)
Year: 2021
Pages: 338
Format: PDF
File size: 14.7 MB
Language: ENG



Pay with Telegram STARS
''

You may also be interested in:

Applications of Machine Learning in Wireless Communications
Machine Learning with Python Theory and Applications
Machine Learning Techniques and Industry Applications
Methodologies, Frameworks, and Applications of Machine Learning
Innovative Machine Learning Applications for Cryptography
Metaheuristics for Machine Learning Algorithms and Applications
Statistical Machine Learning for Engineering with Applications
Metaheuristics for Machine Learning Algorithms and Applications
Methodologies, Frameworks, and Applications of Machine Learning
Methodologies, Frameworks, and Applications of Machine Learning
Machine Learning for Real World Applications
Machine Learning Applications: From Computer Vision to Robotics
Machine Learning Applications From Computer Vision to Robotics
Handbook of Research on Machine Learning Foundations and Applications
Machine Learning Applications From Computer Vision to Robotics
Machine Learning and IoT Applications for Health Informatics
Machine Learning and Data Science Fundamentals and Applications
Fundamentals of Optimization Theory With Applications to Machine Learning
Machine Learning Hybridization and Optimization for Intelligent Applications
Machine Learning Refined Foundations, Algorithms, and Applications
Machine Learning for Healthcare Systems Foundations and Applications
Machine Learning in Transportation Applications with Examples and Codes
Machine Learning with Python Foundations and Applications ML, Volume 1
Machine Learning Hybridization and Optimization for Intelligent Applications
Python Machine Learning The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science
Debugging Machine Learning Models with Python: Develop high-performance, low-bias, and explainable machine learning and deep learning models
Python for Machine Learning From Fundamentals to Real-World Applications
Data Science and Machine Learning Applications in Subsurface Engineering
Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch
Machine Learning for High-Risk Applications (3d Early Release)
Cybernetics, Human Cognition, and Machine Learning in Communicative Applications
Building Machine Learning Powered Applications (Early Release)
Fundamentals of Supervised Machine Learning With Applications in Python, R, and Stata
Applications of Deep Machine Learning in Future Energy Systems
Applications of Optimization and Machine Learning in Image Processing and IoT
Applications of Deep Machine Learning in Future Energy Systems
Real-Time Cloud Computing and Machine Learning Applications
Fundamentals of Supervised Machine Learning With Applications in Python, R, and Stata
Data Science and Machine Learning Applications in Subsurface Engineering
An Introduction to Optimization With Applications to Machine Learning, 5th Edition