الصفحة 1
الصفحة 1
Modern deep learning for tabular data : Novel approaches to common modeling problems
Synthesizes and presents novel deep learning approaches to a seemingly unlikely domain - tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data - an incredibly ubiquitous form of structured data. Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks - through both their 'default' usage and their application to tabular data. Part III compounds the power of the previously covered methods by surveying strategies and techniques to supercharge deep learning systems: autoencoders, deep data generation, meta-optimization, multi-model arrangement, and neural network interpretability.
Introduction to Machine Learning with Applications in Information Security
Provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn't prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec.
FitBuddy : An artificial intelligence powered personal trainer
FitBuddy App is a sports application that employs artificial intelligence in its job as a personal trainer that enables users to exercise anywhere with convenience, tremendous benefit, and high accuracy. The user can exercise with or without weights, in addition to cycling and running. The user must first provide the application with the personal data it has asked for in order to create an appropriate sports program for the user. After that, the user may explore the sports program's weeks and day's sections.
Fashionity
This project is an AI fashion design system to generate fashion images based on user textual description. The proposed system incorporates advanced technology for dissemination and machine translation with the aim of facilitating a seamless user experience for input in both Arabic and English languages. Moreover, the project encompasses the incorporation of a recommendation system that proposes appropriate visual content based on user style. The primary objective of this project is to develop a robust framework capable of generating high-quality images based on textual descriptions, providing recommendations for similar clothing items, and facilitating the retrieval of photographic and apparel articles through image search.
Fashion forge
"Fashion forge" revolutionizes clothing shopping with a cutting-edge mobile application. This AI-powered platform empowers users to describe their dream garment and visualize it instantly, bridging the gap between imagination and reality for fashion-forward users and designers. A recommendation system tailors clothing suggestions based on user preferences, while stores leverage a dedicated social platform for effective marketing. "Fashion Forge" fosters a connected fashion community, empowering users, designers, and stores alike.
CMOS Active Inductors and Transformers : Principle, Implementation, and Applications
CMOS Active Inductors and Transformers: Principle, Implementation, and Applications provides a systematic presentation and a detailed examination of the operation principles and implementations, and discusses their emerging applications in high-speed analog signal processing and data communications.
AI in drug discovery
Constitutes the refereed proceedings of the First international workshop on ai in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.
AI home decorator
Presents the development of “DesignMate”, an innovative AI home decorator application designed to revolutionize interior design. With three main features powered by artificial intelligence, DesignMate simplifies and enhances the process of home decoration. The first feature leverages an Autoregressive transformer model trained on the extensive 3Dfront dataset to suggest room decor based on room layouts. The second feature employs Generative Adversarial Networks (GANs) to enhance the colors of specific room layouts. The third feature introduces an expert system that tailors decor options to user-entered conditions. DesignMate also introduces an integrated e-commerce platform dedicated to furniture, offering users a wide selection of high-quality items that perfectly complement their preferred room designs.
