Liquet B Mathematical Engineering of Deep Learning 2025
Liquet B Mathematical Engineering of Deep Learning 2025 | 30.26 MB
Title: Mathematical Engineering of Deep Learning
Author: Benoit Liquet
Description:
Drives next generation path with latest design techniques and methods in the fields of AI and Deep Learning
KEY FEATURES
● Extensive examples of Machine Learning and Deep Learning principles.
● Includes graphical demonstrations and visual tutorials for various libraries, configurations, and settings.
● Numerous use cases with the code snippets and examples are presented.
DESCRIPTION
'Essentials of Deep Learning and AI' curates the essential knowledge of working on deep neural network techniques and advanced machine learning concepts. This book is for those who want to know more about how deep neural networks work and advanced machine learning principles including real-world examples.
This book includes implemented code snippets and step-by-step instructions for how to use them. You'll be amazed at how SciKit-Learn, Keras, and TensorFlow are used in AI applications to speed up the learning process and produce superior results. With the help of detailed examples and code templates, you'll be running your scripts in no time. You will practice constructing models and optimise performance while working in an AI environment.
Readers will be able to start writing their programmes with confidence and ease. Experts and newcomers alike will have access to advanced methodologies. For easier reading, concept explanations are presented straightforwardly, with all relevant facts included.
WHAT YOU WILL LEARN
● Learn feature engineering using a variety of autoencoders, CNNs, and LSTMs.
● Get to explore Time Series, Computer Vision and NLP models with insightful examples.
● Dive deeper into Activation and Loss functions with various scenarios.
● Get the experience of Deep Learning and AI across IoT, Telecom, and Health Care.
● Build a strong foundation around AI, ML and Deep Learning principles and key concepts.
WHO THIS BOOK IS FOR
This book targets Machine Learning Engineers, Data Scientists, Data Engineers, Business Intelligence Analysts, and Software Developers who wish to gain a firm grasp on the fundamentals of Deep Learning and Artificial Intelligence. Readers should have a working knowledge of computer programming concepts.
AUTHOR BIO
Shashidhar Soppin has two decades of experience in the IT industry. At present he is working as an Enterprise Architect in Zeta. Earlier, he has worked as DMTS (Distinguished Member of Technical Staff) with progressive history of defining vision, strategy, and technical roadmap, architecting solutions, recommending new approaches via technology enablement and managing large-scale & complex IT projects as part of Wipro.
Dr. Manjunath Ramachandra has a blend of experience over twenty five years in the verticals of Healthcare, Telecom and consumer electronics spanning the technologies such as signal & Image processing, Artificial intelligence, cloud computing, IoT, wireless communication and quantum computing. He worked for the companies such as Tata Consultancy Services, Royal Philips and was placed in Wipro Limited as principal consultant. He has filed about 90 patents, authored 210 Research papers in international conferences and journals and a book. He was a member of the embedded systems special interest group at NASSCOM.
B N Chandrashekar, a principal consultant by designation is a veteran of Wipro Technologies Limited and has industry experience of around 20 years. He has toned his credibility by owning and getting molded in the roles of Researcher, Consultant, Technical Director and Full Stack AI Consultant.
DOWNLOAD:
https://rapidgator.net/file/274a6b1543f14ccb0843fafbbb540abf/Liquet_B._Mathematical_Engineering_of_Deep_Learning_2025.rar
https://filestore.me/ojitakji9b4w/Liquet_B._Mathematical_Engineering_of_Deep_Learning_2025.rar
Drives next generation path with latest design techniques and methods in the fields of AI and Deep Learning
KEY FEATURES
● Extensive examples of Machine Learning and Deep Learning principles.
● Includes graphical demonstrations and visual tutorials for various libraries, configurations, and settings.
● Numerous use cases with the code snippets and examples are presented.
DESCRIPTION
'Essentials of Deep Learning and AI' curates the essential knowledge of working on deep neural network techniques and advanced machine learning concepts. This book is for those who want to know more about how deep neural networks work and advanced machine learning principles including real-world examples.
This book includes implemented code snippets and step-by-step instructions for how to use them. You'll be amazed at how SciKit-Learn, Keras, and TensorFlow are used in AI applications to speed up the learning process and produce superior results. With the help of detailed examples and code templates, you'll be running your scripts in no time. You will practice constructing models and optimise performance while working in an AI environment.
Readers will be able to start writing their programmes with confidence and ease. Experts and newcomers alike will have access to advanced methodologies. For easier reading, concept explanations are presented straightforwardly, with all relevant facts included.
WHAT YOU WILL LEARN
● Learn feature engineering using a variety of autoencoders, CNNs, and LSTMs.
● Get to explore Time Series, Computer Vision and NLP models with insightful examples.
● Dive deeper into Activation and Loss functions with various scenarios.
● Get the experience of Deep Learning and AI across IoT, Telecom, and Health Care.
● Build a strong foundation around AI, ML and Deep Learning principles and key concepts.
WHO THIS BOOK IS FOR
This book targets Machine Learning Engineers, Data Scientists, Data Engineers, Business Intelligence Analysts, and Software Developers who wish to gain a firm grasp on the fundamentals of Deep Learning and Artificial Intelligence. Readers should have a working knowledge of computer programming concepts.
AUTHOR BIO
Shashidhar Soppin has two decades of experience in the IT industry. At present he is working as an Enterprise Architect in Zeta. Earlier, he has worked as DMTS (Distinguished Member of Technical Staff) with progressive history of defining vision, strategy, and technical roadmap, architecting solutions, recommending new approaches via technology enablement and managing large-scale & complex IT projects as part of Wipro.
Dr. Manjunath Ramachandra has a blend of experience over twenty five years in the verticals of Healthcare, Telecom and consumer electronics spanning the technologies such as signal & Image processing, Artificial intelligence, cloud computing, IoT, wireless communication and quantum computing. He worked for the companies such as Tata Consultancy Services, Royal Philips and was placed in Wipro Limited as principal consultant. He has filed about 90 patents, authored 210 Research papers in international conferences and journals and a book. He was a member of the embedded systems special interest group at NASSCOM.
B N Chandrashekar, a principal consultant by designation is a veteran of Wipro Technologies Limited and has industry experience of around 20 years. He has toned his credibility by owning and getting molded in the roles of Researcher, Consultant, Technical Director and Full Stack AI Consultant.
DOWNLOAD:
https://rapidgator.net/file/274a6b1543f14ccb0843fafbbb540abf/Liquet_B._Mathematical_Engineering_of_Deep_Learning_2025.rar
https://filestore.me/ojitakji9b4w/Liquet_B._Mathematical_Engineering_of_Deep_Learning_2025.rar