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Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks, and (3) Recurrent Neural Networks. You will be receiving around 4 hours of materials on detailed discussion, mathematical description, and code walkthroughs of the three common families of neural networks. The descriptions of each section is summarized below.Section 1 - Neural Network1.1 Linear Regression1.2 Logistic Regression1.3 Purpose of Neural Network1.4 Forward Propagation1.5 Backward Propagation1.6 Activation Function (Relu, Sigmoid, Softmax)1.7 Cross-entropy Loss Function1.8 Gradient DescentSection 2 - Convolutional Neural Network2.1 Image Data2.2 Tensor and Matrix2.3 Convolutional Operation2.4 Padding2.5 Stride2.6 Convolution in 2D and 3D2.7 VGG162.8 Residual NetworkSection 3 - Recurrent Neural Network3.1 Welcome3.2 Why use RNN3.3 Language Processing3.4 Forward Propagation in RNN3.5 Backpropagation through Time3.6 Gated Recurrent Unit (GRU)3.7 Long Short Term Memory (LSTM)3.8 Bidirectional

Thank you for noting that this is not an interactive course but a test bank for practice and evaluation.Are you ready to pass the LSSGB Lean Six Sigma Green Belt Certification Exam?Use this course to assess your readiness for the actual LSSGB exam.These practice tests will help you determine if you are prepared to take the exam or if you need to spend more time on specific exam topics.These tests cover material from each of the exam topics, and with time limits on each test, you will learn time management for the actual exam.What you will receive from this course:200 unique practice questions for the LSSGB Lean Six Sigma Green Belt Certification Exam.Practice tests created by Subject Matter Experts who stay up-to-date with the actual exam.100% verified answers and explanations for each question.After taking the practice test, aim to achieve a minimum of 80% on the main exam.Official Exam Details:Exam Name: LSSGB Lean Green Belt Certification Exam.Total Questions: 200.Question: Multiple choiceTime Allowed: 60 minutes for every 40 questions.Passing Score: 70%This course is the first in a series of courses offered by us on Lean Six Sigma.Additionally, you have the option to study and prepare for the LSSBB (Lean Six Sigma Black Belt) Enjoy your Lean Six Sigma journey 100% Money-Back Guarantee:We offer a 100% Money-Back Guarantee for our course. If you are not satisfied with your subscription for any reason, you can request a refund within 30 days without needing to provide a justification. Disclaimer:This unofficial practice test is meant to complement exam preparation and does not ensure certification. We do not provide real exam questions or dumps. Our aim

الوصف:تهدف هذه الدورة إلى تعليم المبتدئين كيفية تطوير تطبيقات الهواتف الذكية التي تعمل بنظام الأندرويد، مع التعريف ببيئة العمل الخاصة ببرمجة الأندرويد باستخدام Eclipse. تتضمن الدورة مزيجًا من الأمثلة العملية والشرح النظري لفهم بيئة العمل هذه، مما يساعد على بناء أساس قوي في برمجة تطبيقات الأندرويد.كما تشمل الدورة تعلم تقنيات متقدمة لتطوير تطبيقات أندرويد عالية المستوى، باستخدام الأدوات والخدمات المتاحة في الهواتف الذكية مثل البرمجة باستخدام قواعد البيانات، الشبكات، الإنترنت، الرسائل القصيرة، الخرائط، تحديد المواقع، واستخدام الكاميرات. يتم أيضًا استكشاف طرق الإرسال والاستقبال بين الأجهزة، مثل نقل البيانات من هاتف إلى آخر أو من هاتف إلى جهاز آخر، مما يعزز مهارات المبرمج ويُحسن كفاءته في البرمجة العملية والتطبيقية.الفئة المستهدفة:جميع طلاب كلية علوم الحاسوب.المتطلبات السابقة:لا توجد.نبذة عن الأندرويد:الأندرويد هو نظام تشغيل مجاني ومفتوح المصدر يعتمد على نواة لينكس، تم تصميمه خصيصًا للأجهزة ذات الشاشات اللمسية مثل الهواتف الذكية والأجهزة اللوحية. تم تطويره بواسطة التحالف المفتوح للهواتف النقالة الذي تديره شركة جوجل. يعتمد الأندرويد على واجهة مستخدم تدعم التفاعل بالإيماءات اللمسية مثل النقر، المسح، وضم الأصابع، بالإضافة إلى لوحة مفاتيح افتراضية لإدخال النصوص. يُستخدم الأندرويد أيضًا في أجهزة الحاسوب المحمولة، وأجهزة الألعاب، والكاميرات الرقمية، والأجهزة الإلكترونية الأخرى.يُعد الأندرويد النظام الأكثر انتشارًا بين أنظمة التشغيل، ويملك مجتمعًا كبيرًا من المبرمجين الذين يقومون بتطوير التطبيقات باستخدام لغتي جافا وكوتلن.

This course is the third part of the lecture series on ANSYS Workbench software. It takes you through various modules of ANSYS Workbench like Static Structural, Modal, Random Vibration and Eigenvalue Buckling Analysis. Various numerical are solved to explain various concepts and features of the software and for practice so that expertise can be gained in handling the software. We start with Topology Optimization, how to draw basic geometry using Design Modeler and analyze for in ANSYS Workbench.Second section deals with Modal Analysis. Various numericals are solved for compression spring, hollow pipe and truss bridge subjected to load to find their natural frequencies.Third section is all about the Structural Analysis of 2D truss bridge and 3D beam bridge. The procedure for solving such type of problem is explained.Fourth section deals with Non-Linear Static Structural Analysis of rectangular beam and column.Fifth section is about the Non-Linear Buckling Analysis of 3D column and 2D circular tube.Lastly we study about Random Vibration Analysis module which deals with problems on hollow rectangular column and helical spring.Basic version of this course is available in the previous part of this series ANSYS Workbench Tutorials and ANSYS Workbench Tutorials Part-II. Join those course to learn the basic concepts of these modules and more.

This course provides an in-depth exploration of the conceptual electrical design considerations crucial for modern data centers. With a focus on high-level strategies rather than detailed design practices, students will gain a solid understanding of the principles required to design resilient, efficient, and scalable power systems tailored for critical data center applications. The course covers the foundational aspects of data center design, including defining functionality requirements, evaluating power system resilience, and considering redundancy strategies.Students will learn about key design inputs such as uptime requirements, IT process criticality, service continuity levels, cooling loads, and modularity. The course also delves into advanced topics like grid connection strategies, UPS system technologies, generator reliability, and MV/LV power distribution architectures. Emphasis is placed on comparing redundancy topologies (e.g., N, 2N, N+1), optimizing load balance, and integrating innovative design features for both medium and low-voltage systems.Through this course, participants will develop a comprehensive understanding of how to optimize data center electrical designs while balancing reliability, scalability, and total cost of ownership (TCO). While the course focuses on conceptual design considerations, it lays the groundwork for making informed decisions critical to ensuring high availability and performance in data center environments. Note: Detailed design tasks such as equipment sizing, calculations, and software simulations are outside the course scope.

This "hands-on" course teaches one how to use the R software lavaan package to specify, estimate the parameters of, and interpret covariance-based structural equation (SEM) models that use latent variables. "lavaan" (note the purposeful use of lowercase "L" in 'lavaan') is an acronym for latent variable analysis, and the name suggests the long-term goal of the developer, Yves Rosseel: "to provide a collection of tools that can be used to explore, estimate, and understand a wide family of latent variable models, including factor analysis, structural equation, longitudinal, multilevel, latent class, item response, and missing data models." The course uses and executes many "live" examples (with included R scripts and datasets) using no-cost R and RStudio software to demonstrate and teach how to: (1) specify a SEM model in lavaan syntax; (2) fit and then evaluate your model; (3) perform a CFA; (4) impute and replace missing data; (5) estimate mediating and other indirect effects; (6) estimate and evaluate multigroup models, simultaneously establishing measurement invariance; and (7) specifying and estimating latent (growth) curve models, including the use of random (and latent) intercepts and slopes. The R lavaan package is world-class 'professional-grade' SEM software, used by thousands of SEM experts, graduate students, and college and university faculty around the world.