top of page

Divine Headboards

Public·95 members

Pc Virtual Assistant Denise 110

BMAC is asking that students and instructors work together to accommodate virtual exams that will be taken remotely or in the virtual classroom environment. If you have any questions about alternative testing accommodations for your online exams, please contact Support Services at

Pc Virtual Assistant Denise 110

AllCare Alexandria is a sister clinic serving the Alexandria, VA area 7 days per week and evenings. This kind of access to high-quality primary care eliminates the need for in-area urgent care for our patients. Online appointments for both in-office and virtual care are available and walk-in are always welcome.

DescriptionThis course is designed to give students an opportunity to design experiments and analyze data. Under the guidance of the instructor and teaching assistant, students work in small teams to design experiments and test their designs online. The objective is to teach students basic techniques in molecular biology including recombinant polymerase chain reaction (PCR), cloning and protein expression in bacteria. Students assemble synthetic genes from parts and analyze the contribution of these parts in the regulation of gene expression, from transcription to translation. They develop analytical skills, learn how to design experiments, and how to work on open-ended questions. By the end of the course, students present a research paper detailing their findings. Students also submit a weekly description of their experimental designs. Relevant readings from reviews and primary literature are assigned.

DescriptionIn this course we explore advanced practical data science practices. The course is divided into three major topics, beginning with how to scale a model from a prototype (often in Jupyter notebooks) to the cloud. In this module, we cover virtual environments, containers, and virtual machines before learning about microservices and Kubernetes. Along the way, students are exposed to Dask. We move on to how to use existing models for transfer learning. Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks. This can be very important, given the vast compute and time resources required to develop neural network models on these problems and given the huge jumps in skill that these models can provide to related problems. In this part of the course, we examine various pre-existing models and techniques in transfer learning. In the third part we introduce a number of intuitive visualization tools for investigating properties and diagnosing issues of models. We demonstrate a number of visualization tools ranging from the well-established (like saliency maps) to recent ones that have appeared in Prerequisites: CSCI E-109b.

DescriptionCloud computing infrastructure has become a mainstay of the information technology industry, opening the possibility for on-demand, highly elastic, and infinite computer power with scalability and supporting the delivery of mission-critical secure enterprise applications and services. This course provides ground-up coverage on the high level concepts of cloud landscape, architectural principles, development techniques, design patterns, and real-world security best practices as applied to cloud service providers and consumers. It also addresses regulatory compliance requirements critical to design, implement, deliver, and manage secure cloud-based services. The course delves into the secure cloud-based application development processes that build on DevOps and DevSecOps processes, proactively identifying and mitigating risks with threat models, protection, and isolation of physical and logical infrastructures including computer storage (cloud-hosted virtualization, containerization using Docker and Kubernetes) and network topologies; comprehensive data protection with applied cryptography; end-to-end identity management and access control; monitoring, auditing, intrusion detection, and incident response processes; fraud detection (using machine learning and artificial intelligence techniques); and complying with industry and regulatory mandates. The course leverages cloud computing security guidelines set forth by the International Organization for Standardization (ISO), National Institute of Standards and Technology (NIST), European Union Agency for Network and Information Security (ENISA), and Cloud Security Alliance (CSA).Prerequisites: One of the following courses: CSCI E-7, CSCI E-12, CSCI E-33a, CSCI E-45a, CSCI E-45b, CSCI E-46, CSCI E-90, CSCI E-94, or the equivalent. Additional web application development and/or systems administration knowledge will be very helpful.

DescriptionThis course covers the fundamentals of computer systems programming. It provides a solid background in data representation, systems programming, operating systems, and machine organization and design. The course centers on C++ programming, with some assembly language. Topics include data representation, assembly and machine programming, storage hierarchy and caching, kernel programming and virtual memory, process management, and concurrency (including threads and networking).Prerequisites: CSCI E-26, CSCI E-50, or some experience programming in C++ or C.

DescriptionThe goal of this course is to learn core principles of building highly distributed, highly available systems for processing large volumes of data with historical and near real-time querying capabilities. We cover the stages of data processing that are common to most real-world systems, including high-volume, high-speed data ingestion, historical and real-time metrics aggregation, techniques to address unique counts, data de-duplication and reprocessing, storage options, distributed data indexing, and search. We review approaches to solving common challenges of such systems and get hands-on experience implementing some of them. We look at trends and the evolution of data processing and analytics with special attention to the modern data stack and the resulting advances in data warehousing, data lakes, and data mesh solutions. The focus of this course is on understanding the challenges and core principles of big data processing, not on specific frameworks or technologies used for implementation. We review a few notable technologies for each area with a deeper dive into a few select ones. The course is structured as a progression of topics covering the full, end-to-end data processing pipeline typical in real-world scenarios.Prerequisites: Students must be comfortable with intermediate programming in at least one language, preferably Java, Python, or Scala. Students should be comfortable with basic data structures, functions, and build and dependency management tools (Maven or Gradle for Java, virtualenv for Python). Familiarity with the basic multi-threading is helpful. Most of the examples in lectures are in Java and Python. Students should be comfortable with basic usage, package/software installations, and administration and troubleshooting on Unix-like systems (Linux, any flavor, MacOS). Students should be comfortable with cloud environments like Amazon web services (AWS) cloud and container frameworks like Docker (or VMware, VirtualBox). Their laptops should have 64-bit operating systems and have at least eight central processing units (CPU) and 8G random-access memory (RAM). Students should complete the self-assessment assignment, available on the syllabus, to determine if they are ready to take the course. Courses such as CSCI E-7, CSCI E-10a, CSCI E-88a, and CSCI E-90, or equivalents, are also recommended.

DescriptionThe broad goal of this course is to introduce students to the foundations of key sectoral and thematic knowledge for important challenges to sustainable development, including food and nutritional security, social service delivery, energy policy, water resource management, urbanization, infrastructure, human rights, biodiversity, adaption to climate change, mitigating greenhouse gas emissions (GHGs), sustainable business, ethics, governance, and education. Through a global classroom, lectures are attended virtually with academic partners of the Global Association from around the world. The course consists of weekly live broadcasts featuring international experts. Broadcasts are facilitated and recorded live by Lehigh University and available for asynchronous viewing; however, live participation is encouraged. Topics presented in the broadcasts are discussed during weekly web conference sessions. 350c69d7ab


Welcome to the group! You can connect with other members, ge...

bottom of page