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24 Algorithms courses in Caterham
Sketchup and Stable Diffusion Rendering
By London Design Training Courses
Why Learn Sketchup and Stable Diffusion Rendering Course? Course Link SketchUp and Stable Diffusion Rendering Course. An AI image creation course designed to explore AI image creation techniques and master the use of advanced AI technology. You'll learn Ai 3D modeling, advanced rendering, and lighting techniques. Duration: 16 hrs. Method: 1-on-1 Online Over Zoom is also available. Schedule: Tailor your own schedule by pre-booking a convenient hour of your choice, available from Mon to Sat between 9 am and 7 pm. The Sketchup and Stable Diffusion Rendering Course equips students with comprehensive skills for visually stunning Ai (Artificial intelligence) 3D models and renderings. Master Sketchup's user-friendly interface, advanced rendering techniques, and stable diffusion rendering. Hands-on exercises and real-world projects enhance learning. Ideal for architecture, interior design, product development, and visualization careers. The Sketchup and Stable Diffusion Rendering Course equips students with comprehensive skills for visually stunning Ai (Artificial intelligence) 3D models and renderings. Master Sketchup's user-friendly interface, advanced rendering techniques, and stable diffusion rendering. Hands-on exercises and real-world projects enhance learning. Ideal for architecture, interior design, product development, and visualization careers. Sketchup and Stable Diffusion Rendering Course (16 hours) Module 1: Introduction to Sketchup (2 hours) Overview of Sketchup software and interface navigation Basic drawing tools and geometry creation techniques Module 2: Texturing and Materials (2 hours) Applying textures and customizing materials Exploring texture mapping and material libraries Module 3: Lighting and Shadows (2 hours) Understanding lighting principles and light placement Creating realistic shadows and reflections Module 4: Advanced Modeling Techniques (3 hours) Creating complex shapes and utilizing advanced tools Working with groups, components, and modifiers Module 5: Stable Diffusion Rendering (2 hours) Introduction to stable diffusion rendering Configuring rendering settings for optimal results Module 6: Scene Composition and Camera Setup (2 hours) Exploring composition principles and camera perspectives Managing scenes and creating walkthrough animations Module 7: Rendering Optimization (2 hours) Optimizing models for faster rendering Using render passes and post-processing techniques Module 8: Project Work and Portfolio Development (1 hour) Applying skills to complete a real-world project Showcasing work in a professional portfolio Optional: Installing Stable Diffusion and Python (Additional 10 hours) Module 1: Introduction to Stable Diffusion and Python Overview of Stable Diffusion and Python's significance Module 2: System Requirements Hardware and software prerequisites for installation Module 3: Installing Python Step-by-step installation process for different OS Module 4: Configuring Python Environment Setting up environment variables and package managers Module 5: Installing Stable Diffusion Downloading and installing the Stable Diffusion package Module 6: Setting Up Development Environment Configuring IDEs for Python and Stable Diffusion Module 7: Troubleshooting and Common Issues Identifying and resolving common installation errors Module 8: Best Practices and Recommendations Managing Python and Stable Diffusion installations Module 9: Practical Examples and Projects Hands-on exercises demonstrating usage of Stable Diffusion and Python Module 10: Advanced Topics (Optional) Exploring advanced features and techniques Stable Diffusion UI v2 | A simple 1-click way to install and use https://stable-diffusion-ui.github.io A simple 1-click way to install and use Stable Diffusion on your own computer. ... Get started by downloading the software and running the simple installer. Learning Outcomes: Upon completing the Sketchup and Stable Diffusion Rendering Course, with a focus on AI image rendering, participants will: Master AI Image Rendering: Gain expertise in using AI-powered rendering techniques to create realistic and high-quality visualizations. Utilize Sketchup for 3D Modeling: Navigate the software, proficiently use drawing tools, and create detailed 3D models. Optimize Renderings: Apply AI-based rendering to optimize model visuals, achieving faster rendering times and superior image quality. Implement AI-driven Lighting and Shadows: Utilize AI algorithms for lighting placement, shadows, and reflections, enhancing realism in renderings. Create Professional Portfolio: Showcase AI-rendered projects in a professional portfolio, highlighting advanced image rendering skills. Note: The course focuses on AI image rendering using Sketchup and Stable Diffusion techniques, empowering participants with cutting-edge skills for creating exceptional visual representations.
Sketchup Artificial Intelligence Training Course
By ATL Autocad Training London
Who is this course for? Sketchup Artificial Intelligence Training Course. Mastering SketchUp Artificial Intelligence (AI) is essential for designers, offering automation, efficiency, and innovative solutions. It saves time, enhances visualizations, fosters collaboration, and future-proofs skills, ensuring a competitive edge in the design industry. Click here for more info: Website How to Book? 1-on-1 training. Customize your schedule from Mon to Sat from 9 am to 7 pm Call to book Duration: 16 hours. Method: In-person or Live Online Sketchup and (Artificial Intelligence) Stable Diffusion Rendering Course (16 hours) Module 1: Sketchup Fundamentals (2 hours) Sketchup software overview and interface navigation Introduction to basic drawing tools and fundamental geometry creation techniques Module 2: Texturing and Material Mastery (2 hours) Application of textures and customization of materials Exploration of texture mapping and comprehensive material libraries Module 3: Illumination and Shadows (2 hours) Comprehending lighting principles and strategic light placement Crafting realistic shadows and reflections Module 4: Advanced Modeling Techniques (3 hours) Creating intricate shapes and harnessing advanced modeling tools Efficiently managing groups, components, and modifiers Module 5: Stable Diffusion Rendering (2 hours) Initiating stable diffusion rendering Optimizing rendering settings for superior outcomes Module 6: Scene Composition and Camera Configuration (2 hours) Exploring composition principles and camera perspectives Scene management and creation of captivating walkthrough animations Module 7: Rendering Optimization Strategies (2 hours) Techniques for optimizing models to expedite rendering Application of render passes and post-processing methods Module 8: Real-World Projects and Portfolio Building (1 hour) Application of acquired skills in completing authentic projects Professional portfolio presentation techniques Optional: Stable Diffusion and Python Installation (Additional 10 hours) Module 1: Introduction to Stable Diffusion and Python Comprehensive understanding of Stable Diffusion and Python's significance Module 2: System Prerequisites Hardware and software requirements for successful installation Module 3: Python Installation Guide Step-by-step installation process for various operating systems Module 4: Configuring Python Environment Configuration of environment variables and package managers Module 5: Stable Diffusion Installation Downloading and installing the Stable Diffusion package Module 6: Setting Up the Development Environment Configuration of integrated development environments (IDEs) for Python and Stable Diffusion Module 7: Troubleshooting and Common Issues Identification and resolution of common installation errors Module 8: Best Practices and Recommendations Effective management of Python and Stable Diffusion installations Module 9: Practical Applications and Projects Hands-on exercises exemplifying the practical usage of Stable Diffusion and Python Module 10: Advanced Topics (Optional) Exploration of advanced features and techniques Stable Diffusion https://stablediffusionweb.com https://stable-diffusion-ui.github.io https://stability.ai/stable-diffusion Upon successful completion of the Sketchup and Stable Diffusion Rendering Course with a focus on AI image rendering, participants will achieve the following: 1. Mastery of AI Image Rendering: Attain expertise in employing AI-powered rendering techniques to produce realistic and top-quality visualizations. 2. Proficiency in Sketchup for 3D Modeling: Navigate the software adeptly, utilize drawing tools with proficiency, and craft intricate 3D models. 3. Enhanced Rendering Optimization: Implement AI-based rendering to enhance model visuals, resulting in faster rendering times and superior image quality. 4. Application of AI-driven Lighting and Shadows: Employ AI algorithms for precise lighting placement, shadows, and reflections, elevating the realism of renderings. 5. Development of a Professional Portfolio: Present AI-rendered projects within a polished professional portfolio, highlighting advanced image rendering capabilities. 1. Mastering Sketchup: Attain proficiency in Sketchup, a renowned and user-friendly 3D modeling software, equipping you with the skills needed to adeptly create and manipulate 3D models. 2. Advanced Rendering Expertise: Explore stable diffusion rendering, an avant-garde technique that simplifies the creation of realistic and high-quality renderings. Broaden your rendering capabilities, producing visually stunning representations of your designs. 3. Practical Industry Applications: Cultivate practical skills relevant to diverse industries, encompassing architecture, interior design, product development, and visualization. Elevate your professional portfolio with captivating renderings that showcase your design prowess. 4. Interactive Learning: Participate in hands-on exercises and projects that promote active learning and the practical application of concepts. Benefit from personalized feedback and expert guidance, ensuring your continuous progress throughout the course. 5. Career Advancement: Elevate your career prospects by adding valuable skills to your toolkit. Proficiency in crafting detailed 3D models and impressive renderings through stable diffusion techniques opens doors to diverse job opportunities within the design and visualization sector. 6. Flexibility and Convenience: Access course materials online and learn at your own pace. Enjoy the flexibility of tailoring the coursework to your schedule, allowing you to harmonize your learning journey with other commitments. Course Advantages: Tailored Learning: Enjoy personalized 1-on-1 sessions, accommodating your schedule from Monday to Saturday, 9 am to 7 pm. Mastery of Sketchup: Develop proficiency in the widely-used and user-friendly 3D modeling software, enabling efficient creation and manipulation of 3D models. Advanced Rendering Proficiency: Acquire expertise in stable diffusion rendering for producing realistic, high-quality renderings that enhance the visual appeal of your designs. Practical Applicability: Develop practical skills applicable across diverse domains, including architecture, interior design, product development, and visualization, enriching your professional portfolio. Interactive Practical Experience: Engage in hands-on exercises with personalized guidance from seasoned instructors, ensuring consistent progress in your skillset. Career Progression: Boost your career opportunities by gaining valuable skills in 3D modeling and generating impressive renderings through stable diffusion techniques. Comprehensive Support: Benefit from free portfolio reviews, mock interviews, and career advice, providing additional resources to enhance your professional journey.
Twinmotion Training Course
By ATL Autocad Training London
Who is this course for? Twinmotion Training Course. The Twinmotion course is designed for architects, interior designers and urban planners. Learn how to create architectural visualizations and real-time animations. Students interested in exploring the world of architectural rendering and visualization using Twinmotion software. Click here for more info: Website Duration: 10 hours Approach: 1-on-1 training with tailored content. Schedule: Book a time that suits you, Mon to Sat, from 9 am to 7 pm, and create your own schedule. Course Outline for Twinmotion Real-time 3D Architecture Visualization: Module 1: Twinmotion Introduction Explore the real-time 3D visualization capabilities of Twinmotion Master efficient scene manipulation through the Twinmotion interface Prepare 3D files for import and manage the workflow effectively Module 2: Materials and Realism Enhancement Enhance scenes with realistic materials and elevate visual quality Utilize videos as materials and apply glowing effects Create grunge materials to add texture and realism Employ the X-ray material for specific visualization needs Module 3: Landscape and Vegetation Integration Integrate vegetation using Twinmotion's scatter, HQ trees, and growth features Customize grass and utilize HQ vegetation assets for realistic landscapes Create time-lapse effects for dynamic vegetation growth Module 4: Library and Object Management Access the Twinmotion library for furniture and objects Seamlessly import external 3D objects into your scenes Obtain high-quality 3D assets from the internet and add them to your user library Utilize BIM-objects for additional assets Module 5: Lighting and Animation Techniques Work with various lighting options, including area light and volumetric light, to enhance scenes Add animated characters and vehicles to infuse dynamism into your visuals Incorporate construction vehicles and aircraft for realistic scenes Module 6: Volumes and Interactive Elements Utilize volumes and the animator feature to create dynamic scenes Implement urban elements and measurement tools for precision and accuracy Module 7: Nature and Weather Effects Utilization Harness Twinmotion's nature features, such as physical sky and weather effects, to create lifelike outdoor scenes Establish realistic lighting for outdoor environments Add ocean effects to enhance water elements in your scenes Module 8: Advanced Rendering Techniques Master rendering processes, from creating images to exporting panoramas and animations Implement depth of field improvements to create visually captivating scenes Create BIM motion virtual reality phases for immersive experiences Utilize the Twinmotion presenter feature for seamless presentations Module 9: Final Project Apply acquired knowledge and skills to complete a final rendering project in Twinmotion Download Twinmotion https://www.twinmotion.com ⺠download Learn how to download and install Twinmotion, the fast, easy real-time archvis tool. Learning Outcomes of Real-Time Rendering: Efficient Scene Creation: Rapid development of immersive 3D environments using essential tools and techniques. Realistic Material Application: Expertise in applying authentic textures and shaders for visually convincing scenes. Dynamic Lighting: Understanding dynamic lighting effects for visually compelling, realistic scenes. Interactive Elements: Integration of animations and dynamic objects for engaging user experiences. Performance Optimization: Optimizing assets and scenes for smooth real-time rendering across diverse hardware. Library Utilization: Efficient use of asset libraries to enhance scene complexity without compromising performance. Visual Effects: Application of weather conditions, particle systems, and post-processing effects for stunning environments. Collaborative Rendering: Mastering real-time collaborative rendering techniques for teamwork and live interactions. Future Trends in Real-Time Rendering: Ray Tracing Advancements: Real-time ray tracing for realistic lighting, shadows, and reflections. AI-Powered Rendering: AI-driven predictive rendering for faster, high-quality outcomes. Immersive Experiences: Real-time rendering enabling immersive simulations in gaming, education, and architecture. Cross-Platform Compatibility: Seamless experiences across devices, from gaming consoles to VR headsets. Blockchain Integration: Blockchain tech ensuring asset authenticity and ownership verification. Real-Time Cinematography: Revolutionizing film and animation with efficient real-time visual effects. Environmental Sustainability: Focus on energy-efficient algorithms for reduced environmental impact. Collaborative Design: Real-time rendering facilitating interactive design reviews and creative collaboration. Twinmotion Course: Expertise in Real-Time Visualization: Develop mastery in Twinmotion, enabling the creation of captivating architectural environments in real-time. Improved Design Communication: Enhance concept communication by utilizing dynamic visualizations, elevating the quality of design presentations. Access to High-Quality Assets: Utilize an extensive library of premium assets to enrich scenes, adding realism and creativity to your projects. Crafting Lifelike Environments: Learn to create realistic landscapes, water effects, weather conditions, and special effects, providing immersive and lifelike experiences. Flexible Learning Options: Choose between in-person or live online sessions, ensuring a personalized and convenient learning experience tailored to your needs. Lifetime Access and Support: Benefit from perpetual access to lesson recordings and receive continuous support through lifetime email assistance, allowing you to stay connected with knowledgeable instructors for ongoing guidance. Course Advantages: Mastery of Real-Time Visualization: Acquire expertise in Twinmotion for creating captivating architectural environments. Enhanced Design Communication: Improve concept communication through dynamic visualizations, elevating design presentations. Abundant High-Quality Assets: Utilize an extensive library of top-notch assets, enriching scenes with realism and creativity. Creation of Lifelike Environments: Craft realistic landscapes, water effects, weather conditions, and special effects for immersive experiences. Flexible Learning Options: Choose between in-person or live online sessions, ensuring a personalized and convenient learning experience. Lifetime Access and Support: Enjoy perpetual access to lesson recordings and receive continuous support through lifetime email assistance, staying connected with knowledgeable instructors.
Python Classes in Abu Dhabi
By Time Training Center
Upskill your knowledge with Time Training Center’s Python Classes in Abu Dhabi.In this course you will master Python’s fundamental skills and web frameworks.Learn the core concepts of python such as data structures, algorithms and its implementation. Enroll now! Call us: 97126713828 Mail : info@timetraining.ae Learn more:https://www.timetraining.ae/course/python-training-course Address: Office 203, ADCP Tower - B,Behind City Seasons, Electra Street, Abu Dhabi United Arab Emirates
Online Options
Show all 317Learning Algorithms in JavaScript from Scratch
By Packt
Make your code and programs faster and more efficient by using algorithms
Python 3: Project-based Python, Algorithms, Data Structures
By Packt
Learn to program with Python 3, visualize algorithms and data structures, and implement them in Python projects
Graph Theory Algorithms
By The Teachers Training
Demystify the power of graphs and unlock their potential with our Graph Theory Algorithms Course. Master fundamental algorithms like Depth-First Search and Breadth-First Search, explore pathfinding techniques, and delve into advanced graph concepts. Enrol now to sharpen your skills and become proficient in graph theory.
Data Structures and Algorithms: The Complete Masterclass
By Packt
This course takes you through all the important topics of data structure and algorithms from scratch. You will learn how to solve real-world problems with linked lists, stacks, queues, sorting algorithms, and a lot more using Python.
Overview This comprehensive course on Computer Science: Graph Theory Algorithms will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Computer Science: Graph Theory Algorithms comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Computer Science: Graph Theory Algorithms. It is available to all students, of all academic backgrounds. Requirements Our Computer Science: Graph Theory Algorithms is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 17 sections • 44 lectures • 08:37:00 total length •Promo: 00:03:00 •Introduction: 00:14:00 •Common Problem: 00:10:00 •Depth First Search: 00:11:00 •Breadth First Search: 00:08:00 •Breadth First Search Shortest Path on a Grid: 00:17:00 •Storage and Representation of Trees: 00:10:00 •Beginner Tree Algorithms: 00:10:00 •Rooting Tree: 00:05:00 •Center(s) of a Tree: 00:06:00 •Isomorphisms in Trees: 00:11:00 •Isomorphisms in Trees Source Code: 00:10:00 •Lowest Common Ancestor: 00:17:00 •Topological Sort: 00:14:00 •Shortest and Longest Paths on DAGs: 00:10:00 •Khan's Algorithm: 00:13:00 •Dijkstra's Shortest Path Algorithm: 00:25:00 •Dijkstra's Shortest Path Algorithm Source Code: 00:09:00 •Bellman-Ford Algorithm: 00:15:00 •Floyd-Warshall Algorithm: 00:16:00 •Floyd-Warshall Algorithm Source Code: 00:09:00 •Algorithm to Find Bridges and Articulation Points: 00:20:00 •Algorithm to Find Bridges and Articulation Points Source Code: 00:09:00 •Tarjan's Algorithm for Finding Strongly Connected Components: 00:17:00 •Tarjan's Algorithm for Finding Strongly Connected Components Source Code: 00:07:00 •Travelling Salesman Problem (TSP) with Dynamic Programming: 00:21:00 •Travelling Salesman Problem (TSP) with Dynamic Programming Source Code: 00:14:00 •Existence of Eulerian Paths and Circuit: 00:10:00 •Finding Eulerian Paths and Circuits: 00:16:00 •Eulerian Paths Source Code: 00:08:00 •Prim's Minimum Spanning Tree Algorithm (Lazy Version): 00:15:00 •Prim's Minimum Spanning Tree Algorithm ( Eager Version): 00:15:00 •Prim's Minimum Spanning Tree Algorithm Source Code ( Eager Version): 00:09:00 •Max Flow Ford-Fulkerson Method: 00:13:00 •Max Flow Ford-Fulkerson Method Source Code: 00:17:00 •Network Flow: Unweighted Bipartite Graph Matching: 00:11:00 •Network Flow: Mice and Owls: 00:08:00 •Network Flow: Elementary Math: 00:11:00 •Network Flow: Edmond-Karp Algorithm: 00:06:00 •Network Flow: Edmond-Karp Algorithm Source Code: 00:10:00 •Network Flow: Capacity Scaling: 00:10:00 •Network Flow: Capacity Scaling Source Code: 00:06:00 •Network Flow: Dinic's Algorithm: 00:12:00 •Network Flow: Dinic's Algorithm Source Code: 00:09:00
Register on the Graph Theory Algorithms today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion. The Graph Theory Algorithms is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The Graph Theory Algorithms Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the Graph Theory Algorithms, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Course Content Module 01: Introduction Introduction 00:14:00 Module 02: Common Problem Common Problem 00:10:00 Module 03: Depth First Search Depth First Search 00:11:00 Module 04: Breadth First Search Breadth First Search 00:08:00 Module 05: Breadth First Search Shortest Path on a Grid Breadth First Search Shortest Path on a Grid 00:17:00 Module 06: Trees Storage and Representation of Trees 00:10:00 Beginner Tree Algorithms 00:10:00 Rooting Tree 00:05:00 Center(s) of a Tree 00:06:00 Isomorphisms in Trees 00:11:00 Isomorphisms in Trees Source Code 00:10:00 Lowest Common Ancestor 00:17:00 Module 07: Topological Sort Topological Sort 00:14:00 Shortest and Longest Paths on DAGs 00:10:00 Khan's Algorithm 00:13:00 Module 08: Dijkstra Dijkstra's Shortest Path Algorithm Source Code 00:09:00 Dijkstra's Shortest Path Algorithm 00:25:00 Module 09: Bellman-Ford Algorithm Bellman-Ford Algorithm 00:15:00 Module 10: Floyd-Warshall Algorithm Floyd-Warshall Algorithm 00:16:00 Floyd-Warshall Algorithm Source Code 00:09:00 Module 11: Bridge and Algorithm Points Algorithm to Find Bridges and Articulation Points 00:20:00 Algorithm to Find Bridges and Articulation Points Source Code 00:09:00 Module 12: Tarjan Algorithm Tarjan's Algorithm for Finding Strongly Connected Components 00:17:00 Tarjan's Algorithm for Finding Strongly Connected Components Source Code 00:07:00 Module 13: Travelling Salesman Problem (TSP) Travelling Salesman Problem (TSP) with Dynamic Programming 00:21:00 Travelling Salesman Problem (TSP) with Dynamic Programming Source Code 00:14:00 Module 14: Eulerian Paths and Circuits Existence of Eulerian Paths and Circuit 00:10:00 Finding Eulerian Paths and Circuits 00:16:00 Eulerian Paths Source Code 00:08:00 Module 15: Prim's Minimum Spanning Tree Algorithm Prim's Minimum Spanning Tree Algorithm (Lazy Version) 00:15:00 Prim's Minimum Spanning Tree Algorithm ( Eager Version) 00:15:00 Prim's Minimum Spanning Tree Algorithm Source Code ( Eager Version) 00:09:00 Module 16: Network Flow Max Flow Ford-Fulkerson Method 00:13:00 Max Flow Ford-Fulkerson Method Source Code 00:17:00 Network Flow: Unweighted Bipartite Graph Matching 00:11:00 Network Flow: Mice and Owls 00:08:00 Network Flow: Elementary Math 00:11:00 Network Flow: Edmond-Karp Algorithm Source Code 00:06:00 Network Flow: Edmond-Karp Algorithm Source Code 00:10:00 Network Flow: Capacity Scaling 00:10:00 Network Flow: Capacity Scaling Source Code 00:06:00 Network Flow: Dinic's Algorithm 00:12:00 Network Flow: Dinic's Algorithm Source Code 00:09:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.
100 JavaScript Algorithms Challenge
By Packt
This video course takes you through the basic and advanced JavaScript methods, enabling you to understand and implement them in a correct way. The course is filled with tips and tricks that will help you tackle tough interview questions to get a job.
Quantum Algorithms for Computational Finance
By Qureca
About the course “Quantum Computing for Finance” is an emerging multidisciplinary field of quantum physics, finance, mathematics, and computer science, in which quantum computations are applied to solve complex problems. “Quantum Algorithms for Computational Finance” is an advanced course in the emerging field of quantum computing for finance. This technical course will develop an understanding in quantum algorithms for its implementation on quantum computers. Through this course, you will learn the basics of various quantum algorithms including: Grover’s and Rudolf’s algorithm, Quantum amplitude Estimation (QAE) algorithm envisioned as a quadratic speed-up over Classical Monte-Carlo simulations, Combinatorial optimization algorithms namely Quantum Approximate Optimization Algorithm (QAOA), and Variational Quantum Eigensolver (VQE), and Quantum-inspired optimization algorithms – Simulated Coherent Ising Machine (Sim-CIM), and Simulated Bifurcation Algorithm (SBA). This course is meant for all those learners who want to explore the long-term employability of quantum computing in finance, assuming that you are familiar with the concepts of quantitative and computational finance. In addition, the course contains several Python based programming exercises for learners to practice the algorithms explained throughout the course. This course is the second part of the specialised educational series: “Quantum Computing for Finance”. What Skills you will learn Ability to perform quantum arithmetic operations and simulations. An understanding of the Quantum Amplitude Estimation algorithm and its variants. The computational and modelling techniques for option pricing and portfolio optimization on a quantum computer. The skills for a career in quantum finance including Quantum Algorithmic Research, Quantitative Asset Management and Trading, financial engineering, and risk management, using quantum computing technology. Course Prerequisites All potential learners must have prior knowledge or familiarity with basic quantum algorithms/basic quantum programming. Before enrolling this course, we recommend all learners to complete the first course “Introduction to Quantitative and Computational Finance” of the series “Quantum Computing for Finance”, if they have no previous experience with the concepts of quantitative and computational finance. Duration The estimated duration to complete this course is approximately 6 weeks (~4hrs/week). Course assessment To complete the course and earn the certification, you must pass all the quizzes at the end of each lesson by scoring 80% or more on each of them. Instructors QuantFiQuantFi is a French start-up research firm formed in 2019 with the objective of using the science of quantum computing to provide solutions to the financial services industry. With its staff of PhD's and PhD students, QuantFi engages in fundamental and applied research in in the field of quantum finance, collaborating with industrial partners and universities in seeking breakthroughs in such areas as portfolio optimisation, asset pricing, and trend detection.
Data Structures in JavaScript - Master the Fundamentals
By Packt
This course covers essential topics required for working with data structures and algorithms using JavaScript. From basics of data structures and algorithms to key concepts, such as arrays, lists, Big O time-space asymptomatic analysis, trees, and maps, this course will teach you everything with the help of engaging examples and activities.
Garbage Collection Algorithms
By Packt
There are quite a few issues with manual memory management. Therefore, to avoid memory leaks and optimally use your memory, automatic memory management is essential. In this course, we'll learn about garbage collection as a form of automatic memory management.
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