الصفحة 1
الصفحة 1
Neuroscribe = نيوروسكرايب
Neuroscribe is a cutting-edge deep learning framework designed to address the complexities and inefficiencies encountered in existing frameworks like PyTorch and TensorFlow. Aimed at streamlining model development and enhancing performance across diverse hardware environments, NeuroScribe offers a lightweight and flexible solution. The framework features a robust tensor library, an auto-differentiation engine, a comprehensive neural network module, and advanced optimization algorithms. With built-in visualization tools and a user-friendly interface, NeuroScribe simplifies both beginner and advanced workflows. Its cross-platform compatibility, supported by CUDA and Metal Performance Shaders (MPS), ensures optimal performance, and in some scenarios, NeuroScribe demonstrates superior speed compared to leading frameworks. Additionally, NeuroScribe introduces unique libraries and features not found in other frameworks, further enhancing its versatility and appeal. The modular architecture and automatic system detection further enhance its adaptability, making NeuroScribe a versatile and powerful tool for deep learning practitioners.
Multifunctional Pharmaceutical Nanocarriers
Various pharmaceutical nanocarriers, such as nanospheres,nanocapsules, liposomes, micelles, cell ghosts, lipoproteins and some others are widely used for experimental (and already clinical) delivery of therapeutic and diagnostic agents. The use of nanoparticulate pharmaceutical carriers to enhance the in vivo efficiency of many drugs well established itself over the past decade both in pharmaceutical research and clinical setting.
Modern Deep Learning Design and Application Development : Versatile Tools to Solve Deep Learning Problems
Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking. You will: Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization. Compress deep learning models while maintaining performance. Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them. Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches.
Inside deep learning : Math, algorithms, models
Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology Adapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped--you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English.
From decoding turbulence to unveiling the fingerprint of climate change : Klaus Hasselmann—Nobel Prize Winner in Physics 2021
Serves as a reference for the key elements and their significance of Klaus Hasselmann's work on climate science and on ocean wave research, all based on a rigorous and deeply physical thinking. It summarizes the original articles (mostly from the 1970 and 1980s; some of which are hard to find nowadays) and brings them in a present-day context.
Environmental Crises
This book studies the art and science of analyzing, assessing and anticipating environmental change. Among the issues considered are the observational evidence, statistical analysis and dynamic modeling as well as visioning of not-implausible changes in the environment, the changing public perception of the environment, functions of the environment and its use. Coverage also reviews a series of four prominent cases, namely climate change, the emissions of gasoline lead into the atmosphere and water bodies, fisheries policies and the management of marine oil pollution.
Developing Services for the Wireless Internet
This book is for developers of wireless Internet services. It addresses the technical issues that can get in the way of the production of a successful service: variability of terminals, unstable technology, incomplete testing environment, variable bandwidth and quality of service. Useful techniques and methods when handing these issues are proposed using two case studies: a mobile game and a mobile trading service.
Deep Learning with PyTorch Lightning : Build and train high-performance artificial intelligence and self-supervised models using Python
You’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide. In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning.
Congenital torch infections : torch panel
TORCH is a group of infections that can be passed from the pregnant mother to her fetus through the placenta. TORCH, includes Toxoplasmosis, Other (syphilis, varicella-zoster, parvovirus B19, Hepatitis B), Rubella, Cytomegalovirus (CMV), and Herpes infections are some of the most common infections associated with congenital anomalies. Most of the TORCH infections have serious fetal consequences and there has no impact on fetal outcome. In the present article, we wanted to discuss about the causative agents/organism, mode of infection, symptoms, treatment, vaccination, available molecular biological techniques and public awareness regarding this infection, Our objective in this project is to assess the awareness of and knowledge about mother-to-child infections and prevention
Congenital infections : Toxoplasmosis and rubella
Torch syndrome is caused by a uterine infection with one of the factors that cause Torch syndrome, which disrupts the development of the fetus. Torch syndrome can be prevented by treating the infected pregnant woman, and thus preventing transmission of the infection to the fetus. And some of the factors that cause Torch Syndrome can give the mother permanent immunity, preventing the arrival of the disease and thus fetal malformations. They include a group of symptoms caused by a congenital infection such as toxoplasmosis, rubella, CMV and herpes, or due to other organisms such as syphilis, small viruses, and varicella zoster virus. The Toxoplasma parasite and the rubella virus can provide lasting immunity if the mother was infected before pregnancy in both or if she was vaccinated before pregnancy in Rubella.
