Another day, another achievement to be celebrated! 🎉 Ruslan Karmanny recently earned a Graduate Certificate in Computer Science through Stanford Online, balancing an average of 15-20 hours of coursework per week. His journey included tackling courses like "Introduction to the Theory of Computation" and "Deep Learning."
Read about Ruslan's inspiring journey and how he achieved this milestone: https://lnkd.in/gRQeDBsU
Chief Technology Officer | Stanford University | INSEAD
Great educational opportunities provided by Stanford Online dramatically helped me change my professional domain and finally "get into tech". My learning journey with Stanford Online began in 2019 with the Natural Language Processing with Deep Learning Course (XCS224N). I was so impressed by the course that I went on to complete the Artificial Intelligence Professional Program in 2020. This was followed by two graduate programs: Data Models and Optimization in 2021, and Introductory Programming in 2023.
Reflecting on this journey, I believe my experiences might be useful to share with others considering a similar path. As Paul Graham eloquently stated, "Viewed as an asset, education has some attractive qualities. It naturally tends to compound. You can carry it with you easily. No one can steal or tax it. And unlike most assets, it makes life more interesting."
I encourage anyone to embrace the journey of lifelong learning—it’s an investment that pays dividends in both personal and professional growth.
#stanfordonline
Congratulations, Ruslan Karmanny - your article presented a fascinating dive into computer science. As someone who recently completed Stanford's 'Code in Place' program, I'm considering whether to pursue CS more deeply. As someone roughly your age, this is inspiring. Much appreciated and well done.
Great educational opportunities provided by Stanford Online dramatically helped me change my professional domain and finally "get into tech". My learning journey with Stanford Online began in 2019 with the Natural Language Processing with Deep Learning Course (XCS224N). I was so impressed by the course that I went on to complete the Artificial Intelligence Professional Program in 2020. This was followed by two graduate programs: Data Models and Optimization in 2021, and Introductory Programming in 2023.
Reflecting on this journey, I believe my experiences might be useful to share with others considering a similar path. As Paul Graham eloquently stated, "Viewed as an asset, education has some attractive qualities. It naturally tends to compound. You can carry it with you easily. No one can steal or tax it. And unlike most assets, it makes life more interesting."
I encourage anyone to embrace the journey of lifelong learning—it’s an investment that pays dividends in both personal and professional growth.
#stanfordonline
Excited to share that I've successfully completed the Algorithms course at Stanford University! 🎓 Delved deep into algorithm design and analysis, equipping myself with invaluable problem-solving skills. Ready to apply this knowledge to tackle complex challenges head-on. #Stanford#Algorithms#ProblemSolving"
I'm thrilled to share that I recently completed Stanford University's "Divide and Conquer, Sorting and Searching, and Randomized Algorithms" course! This course has been an incredible deep dive into some of the core principles of algorithms and data structures, and I'm excited about the new skills and knowledge I've gained.
Algorithms and data structures are the foundation of efficient problem-solving in computer science, and I'm committed to mastering them. This course was just the beginning of my journey. I'll be continuing my learning with more advanced courses and hands-on projects to solidify my understanding and keep pushing my boundaries.
#LearningJourney#Algorithms#DataStructures#StanfordUniversity#ContinuousImprovement#TechEducation#ComputerScience#CareerGrowth
Many individuals frequently ask me how to transition into fields like quantum computing. Here’s my advice:
1. Identify your interest area (not the technology itself): Choose an industry, field, or specific problem that you are passionate about solving. Your motivation will drive your learning and innovation.
2. Evaluate technological suitability: Assess whether quantum computing is currently or soon applicable to your chosen field. Understanding the technology's current and potential impact is crucial.
3. Start with accessible resources: Avoid diving straight into advanced textbooks like Nielsen and Chuang’s on quantum computation. Instead, begin with accessible resources on platforms like Coursera, Udemy, EdX, or even YouTube to build your foundational knowledge.
4. Engage with practical tools: Test your skills using popular Python SDKs such as Qiskit, PennyLane, Cirq, or other beginner-friendly packages. Hands-on experience is invaluable.
5. Formal education is optional: While a master’s degree or PhD can be beneficial, it is not a prerequisite for progress. The key is continuous learning and practical application.
6. Innovate with your knowledge: Apply a creative approach to what you learn. Go beyond classical tutorials and focus on solving specific problems. This will help you carve out a niche in your chosen field.
These steps are equally applicable to those exploring LLMs and neuromorphic approaches. Strive to become a specialist rather than a generalist. Often, people ask me about sensors in quantum or other areas of this technology, and I admit my focus is on quantum machine learning (QML), so I'm not fully aware of all that involves quantum. Choose your path, stay passionate, and focus on problem-solving.
#qml#datascience#quantum#llms#neuromoprhic
PhD in Quantum Computing | Quantum Machine Learning Researcher | Credit Scoring Modeler | Co-author of "Financial Modeling using Quantum Computing" | Linkedin Quantum Top Voice | LLM Researcher for Cases in Finance
Many individuals frequently ask me how to transition into fields like quantum computing. Here’s my advice:
1. Identify your interest area (not the technology itself): Choose an industry, field, or specific problem that you are passionate about solving. Your motivation will drive your learning and innovation.
2. Evaluate technological suitability: Assess whether quantum computing is currently or soon applicable to your chosen field. Understanding the technology's current and potential impact is crucial.
3. Start with accessible resources: Avoid diving straight into advanced textbooks like Nielsen and Chuang’s on quantum computation. Instead, begin with accessible resources on platforms like Coursera, Udemy, EdX, or even YouTube to build your foundational knowledge.
4. Engage with practical tools: Test your skills using popular Python SDKs such as Qiskit, PennyLane, Cirq, or other beginner-friendly packages. Hands-on experience is invaluable.
5. Formal education is optional: While a master’s degree or PhD can be beneficial, it is not a prerequisite for progress. The key is continuous learning and practical application.
6. Innovate with your knowledge: Apply a creative approach to what you learn. Go beyond classical tutorials and focus on solving specific problems. This will help you carve out a niche in your chosen field.
These steps are equally applicable to those exploring LLMs and neuromorphic approaches. Strive to become a specialist rather than a generalist. Often, people ask me about sensors in quantum or other areas of this technology, and I admit my focus is on quantum machine learning (QML), so I'm not fully aware of all that involves quantum. Choose your path, stay passionate, and focus on problem-solving.
#qml#datascience#quantum#llms#neuromoprhic
Many individuals frequently ask me how to transition into fields like quantum computing. Here’s my advice:
1. Identify your interest area (not the technology itself): Choose an industry, field, or specific problem that you are passionate about solving. Your motivation will drive your learning and innovation.
2. Evaluate technological suitability: Assess whether quantum computing is currently or soon applicable to your chosen field. Understanding the technology's current and potential impact is crucial.
3. Start with accessible resources: Avoid diving straight into advanced textbooks like Nielsen and Chuang’s on quantum computation. Instead, begin with accessible resources on platforms like Coursera, Udemy, EdX, or even YouTube to build your foundational knowledge.
4. Engage with practical tools: Test your skills using popular Python SDKs such as Qiskit, PennyLane, Cirq, or other beginner-friendly packages. Hands-on experience is invaluable.
5. Formal education is optional: While a master’s degree or PhD can be beneficial, it is not a prerequisite for progress. The key is continuous learning and practical application.
6. Innovate with your knowledge: Apply a creative approach to what you learn. Go beyond classical tutorials and focus on solving specific problems. This will help you carve out a niche in your chosen field.
These steps are equally applicable to those exploring LLMs and neuromorphic approaches. Strive to become a specialist rather than a generalist. Often, people ask me about sensors in quantum or other areas of this technology, and I admit my focus is on quantum machine learning (QML), so I'm not fully aware of all that involves quantum. Choose your path, stay passionate, and focus on problem-solving.
#qml#datascience#quantum#llms#neuromoprhic
PhD in Quantum Computing | Quantum Machine Learning Researcher | Credit Scoring Modeler | Co-author of "Financial Modeling using Quantum Computing" | Linkedin Quantum Top Voice | LLM Researcher for Cases in Finance
Many individuals frequently ask me how to transition into fields like quantum computing. Here’s my advice:
1. Identify your interest area (not the technology itself): Choose an industry, field, or specific problem that you are passionate about solving. Your motivation will drive your learning and innovation.
2. Evaluate technological suitability: Assess whether quantum computing is currently or soon applicable to your chosen field. Understanding the technology's current and potential impact is crucial.
3. Start with accessible resources: Avoid diving straight into advanced textbooks like Nielsen and Chuang’s on quantum computation. Instead, begin with accessible resources on platforms like Coursera, Udemy, EdX, or even YouTube to build your foundational knowledge.
4. Engage with practical tools: Test your skills using popular Python SDKs such as Qiskit, PennyLane, Cirq, or other beginner-friendly packages. Hands-on experience is invaluable.
5. Formal education is optional: While a master’s degree or PhD can be beneficial, it is not a prerequisite for progress. The key is continuous learning and practical application.
6. Innovate with your knowledge: Apply a creative approach to what you learn. Go beyond classical tutorials and focus on solving specific problems. This will help you carve out a niche in your chosen field.
These steps are equally applicable to those exploring LLMs and neuromorphic approaches. Strive to become a specialist rather than a generalist. Often, people ask me about sensors in quantum or other areas of this technology, and I admit my focus is on quantum machine learning (QML), so I'm not fully aware of all that involves quantum. Choose your path, stay passionate, and focus on problem-solving.
#qml#datascience#quantum#llms#neuromoprhic
🎓 Exciting milestone achieved! 🌟 Just wrapped up the Algorithms on Strings course at the University of California San Diego with an impressive 93.60%. 🚀 Coursera has certified my successful completion of this 18-hour journey, where I delved into intricate skills such as Suffix Trees, Suffix Arrays, and the Knuth–Morris–Pratt (KMP) Algorithm.
🔍 Grateful for the immersive experience, UCSD has equipped me with a deep understanding of algorithms on strings, enhancing my problem-solving capabilities in the realm of computational linguistics.
💡 With a 4.5 rating from 1,035 peers and a robust enrollment of 82K students, this course has been a dynamic learning environment. Now proficient in advanced string algorithms, I'm excited to apply this expertise to real-world challenges.
🛠️ Eager to contribute to projects that demand a nuanced understanding of strings, I'm open to exciting opportunities in the field of computational linguistics. Let's connect and explore how my skills in Suffix Trees, Suffix Arrays, and the KMP Algorithm can elevate your team's capabilities!
#AlgorithmsonStrings#UniversityOfCaliforniaSanDiego#CourseraCertification#TechSkills#ComputationalLinguistics#ProblemSolving#ProfessionalDevelopment#LinkedInLearning#CareerGrowth#AlwaysLearning 🚀
I am excited to share that - as a 4th-year student in the Faculty of Electronics, Telecommunications, and Information Technology - I am gearing up to submit my thesis in July 2024. The focus of my study is “Real-time audio signal recognition system using Machine Learning techniques for security applications”. 🚀
To adequately prepare for this significant milestone, I have completed an extensive course from Google, supported by Digital Stack and led by Lucian G. and Diana Grosu-Șandru, PhD. This course encompassed 12 intensive sessions, delving into AI basics: problem-solving algorithms, knowledge representation, probabilistic reasoning, Python & OOP, Machine Learning, NLP, Deep Learning, Data Mining, and AI ethics. 🧠
This experience has significantly enhanced my understanding and has brought me a step closer to achieving my thesis objectives. I am eagerly waiting to dive even deeper into the AI world 🤖
#AIJourney#GoogleCourse#ThesisPreparation
📐 Just Completed: Introduction to Mathematical Thinking with Stanford University! 🎓🌟
Excited to share that I've successfully completed the "Introduction to Mathematical Thinking" course by Stanford University! 💡📊
Throughout this engaging course, I've delved into the fundamental principles of mathematical thinking, learning how to approach problems with clarity and rigor. From understanding the language of mathematics to developing logical reasoning and problem-solving skills, this course has provided me with a solid foundation for tackling mathematical challenges with confidence.
A heartfelt thank you to the esteemed instructors at Stanford University for delivering such an enriching and insightful learning experience. Your expertise and guidance have been invaluable in expanding my mathematical thinking skills.
Eager to apply the mathematical principles and techniques I've learned to solve real-world problems and drive innovation in my professional endeavors!
#Mathematics#StanfordUniversity#ContinuousLearning#ProblemSolving
Many individuals frequently ask me how to transition into fields like quantum computing. Here’s my advice:
1. Identify your interest area (not the technology itself): Choose an industry, field, or specific problem that you are passionate about solving. Your motivation will drive your learning and innovation.
2. Evaluate technological suitability: Assess whether quantum computing is currently or soon applicable to your chosen field. Understanding the technology's current and potential impact is crucial.
3. Start with accessible resources: Avoid diving straight into advanced textbooks like Nielsen and Chuang’s on quantum computation. Instead, begin with accessible resources on platforms like Coursera, Udemy, EdX, or even YouTube to build your foundational knowledge.
4. Engage with practical tools: Test your skills using popular Python SDKs such as Qiskit, PennyLane, Cirq, or other beginner-friendly packages. Hands-on experience is invaluable.
5. Formal education is optional: While a master’s degree or PhD can be beneficial, it is not a prerequisite for progress. The key is continuous learning and practical application.
6. Innovate with your knowledge: Apply a creative approach to what you learn. Go beyond classical tutorials and focus on solving specific problems. This will help you carve out a niche in your chosen field.
These steps are equally applicable to those exploring LLMs and neuromorphic approaches. Strive to become a specialist rather than a generalist. Often, people ask me about sensors in quantum or other areas of this technology, and I admit my focus is on quantum machine learning (QML), so I'm not fully aware of all that involves quantum. Choose your path, stay passionate, and focus on problem-solving.
#qml#datascience#quantum#llms#neuromoprhic
What an inspiring journey, Ruslan, Your dedication to continuous learning and your ability to balance such intensive coursework is truly commendable.