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Generative programming (Computer science)

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A common application of generative programming is building high performance computational kernels highly tuned to the problem at hand. A typical linear algebra kernel is specialized to the numerical domain (rational, float, double, etc.), loop unrolling factors, array layout and a priori knowledge (e.g., the matrix being positive definite). It is tedious and error prone to specialize by hand, writing numerous variations of the same algorithm. The widely used generators such as ATLAS and SPIRAL reliably produce highly tuned specialized code but are difficult to extend. In ATLAS, which generates code using printf, even balancing parentheses is a challenge. According to the ATLAS creator, debugging is nightmare. A typed staged programming language such as MetaOCaml lets us state a general, obviously correct algorithm and add layers of specializations in a modular way. By ensuring that the generated code always compiles and letting us quickly test it, MetaOCaml makes writing generators less daunting and more productive. The readers will see it for themselves in this hands-on tutorial. Assuming no prior knowledge of MetaOCaml and only a basic familiarity with functional programming, we will eventually implement a simple domain-specific language (DSL) for linear algebra, with layers of optimizations for sparsity and memory layout of matrices and vectors, and their algebraic properties. We will generate optimal BLAS kernels. We shall get the taste of the "Abstraction without guilt".

Generative programming (Computer science)

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Generative programming (Computer science) --- Software engineering

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Master the innovative world of deepfakes and generative AI for face replacement with this full-color guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Understand what deepfakes are, their history, and how to use the technology ethically Get well-versed with the workflow and processes involved to create your own deepfakes Learn how to apply the lessons and techniques of deepfakes to your own problems Book Description Applying Deepfakes will allow you to tackle a wide range of scenarios creatively. Learning from experienced authors will help you to intuitively understand what is going on inside the model. You'll learn what deepfakes are and what makes them different from other machine learning techniques, and understand the entire process from beginning to end, from finding faces to preparing them, training the model, and performing the final swap. We'll discuss various uses for face replacement before we begin building our own pipeline. Spending some extra time thinking about how you collect your input data can make a huge difference to the quality of the final video. We look at the importance of this data and guide you with simple concepts to understand what your data needs to really be successful. No discussion of deepfakes can avoid discussing the controversial, unethical uses for which the technology initially became known. We'll go over some potential issues, and talk about the value that deepfakes can bring to a variety of educational and artistic use cases, from video game avatars to filmmaking. By the end of the book, you'll understand what deepfakes are, how they work at a fundamental level, and how to apply those techniques to your own needs. What you will learn Gain a clear understanding of deepfakes and their creation Understand the risks of deepfakes and how to mitigate them Collect efficient data to create successful deepfakes Get familiar with the deepfakes workflow and its steps Explore the application of deepfakes methods to your own generative needs Improve results by augmenting data and avoiding overtraining Examine the future of deepfakes and other generative AIs Use generative AIs to increase video content resolution Who this book is for This book is for AI developers, data scientists, and anyone looking to learn more about deepfakes or techniques and technologies from Deepfakes to help them generate new image data. Working knowledge of Python programming language and basic familiarity with OpenCV, Pillow, Pytorch, or Tensorflow is recommended to get the most out of the book.

Generative programming (Computer science) --- Deepfakes. --- Generative art.

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This monograph is a comprehensive review of the current state-of-the-art in DVAEs. It gives the reader an accessible summary of the technical aspects of the different DVAE models, their connections with classical models, their cross-connections, and their unification in the DVAE class in a concise, easy-to-read book.

Neural networks (Computer science). --- Artificial intelligence. --- Generative programming (Computer science).