We are excited to announce the launch of our free ebook Machine Learning for Human Beings, authored by researcher in the field of computer vision and machine learning Mohit Deshpande, in collaboration with Pablo Farias Navarro, founder of Zenva.
In general, data generation methods exist in a big variety of modern deep learning applications, from computer vision to natural language processing. At this point, we are able to produce nearly indistinguishable generative data by the human eye. Generative learning can be broadly divided into two main categories: a) Variational AutoEncoders (VAE) and b) generative adversarial networks (GAN).
Download ebook free Generative Deep Learning:
This book is my attempt to provide a brief but comprehensive introduction to graph representation learning, including methods for embedding graph data, graph neural networks, and deep generative models of graphs.
This book is recommended reading for all practitioners wanting to adopt recent and disruptive breakthroughs in debugging, explainability, fairness, and interpretability techniques for machine learning.Where you can get it: You can download for free here.
The book begins with an overview of the ethics, security and privacy issues, future directions, and challenges in machine learning as well as a systematic review of deep learning techniques and provides an understanding of building generative adversarial networks. Chapters explore predictive data analytics for health issues. The book also adds a macro dimension by highlighting the industrial applications of machine learning, such as in the steel industry, for urban information retrieval, in garbage detection, in measuring air pollution, for stock market predictions, for underwater fish detection, as a fake news predictor, and more.
Even so, we were able to pinpoint a few general-purpose generative projects; for example, for aiding the generation of typography [11,12,13]. More relevant to our project, we highlight generative projects for creating gd layouts. While several projects are more limited in terms of functionalities, e.g., by not allowing the setting of concept-wise parameters [14,15], others, such as the work of Ferreira et al. (2019) [16] or Cleveland (2010) [17], stand out by allowing the user to freeze certain intended parameters and allow the system to vary others. In this way, the user is able to preserve an intended style. Further EvoDesigner exchanges must use a similar strategy. Additionally, we highlight the work of Rebelo et al. (2020) [18] on creating web pages based on the semantic characteristics of their inner text content. In addition, it is necessary to implement methods for generating designs that can visually represent given semantic concepts. Lastly, we highlight the work of Feiner (1988) [14] and Cleveland (2010) [17], which make use of grid systems to generate layouts.
2ff7e9595c
Comments