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For R programmers, the R interface to the Keras deep learning library is a powerful head start on building deep learning models without switching to Python. It... Læs mere
The Streamlit framework empowers Python developers to build web applications quickly without writing a single line of HTML, CSS, or JavaScript. There’s no need to switch stacks just... Læs mere
Mathematics is the foundation of machine learning algorithms. To understand the shortcomings of existing algorithms and develop more effective methods, it is essential to understand the mathematical concepts underlying these algorithms and their operational principles.
Programmers have a unique superpower when it comes to managing financial stock analysis, price forecasting, and strategic investing. Using popular open-source Python libraries... Læs mere
This concise textbook is intended as a guide for programming-language designers and users to better help them understand consequences of design decisions.... Læs mere
Presents the new VHDL'92 for the VHDL designer. New features are explained and classified. Examples are also provided, each new feature is given a rationale and its impact on design methodology and performance is analyzed. Where appropriate, pitfalls and traps are explained.
A basic, practical, introductory textbook for professionals and students, this text explains how a designer can be more effective through the use of the Verilog hardware description language to simulate and document a design.
Prescriptions for Quant Traders Using R is a practical, hands-on guide for turning data into trading decisions. This book is written for... Læs mere
This book offers a comprehensive discussion of the growing importance of ethical considerations in algorithmic systems to introduce practical frameworks and tools for identifying ethical risks, ensuring fair outcomes, and maintaining transparency in algorithmic processes.
The interior-point method failed to shake the domination of the simplex method.After years of research and exploration, the author proposes to break out of the simplex and interior-point methods.