Ask Madhukar - Techbytes

- Designing Data-Intensive Applications by Martin Kleppmann Amazon: https://amzn.to/4dlfPed - Database Internals by Alex Petrov Amazon: https://amzn.to/3YI515e - System Design Interview (Volume 1) by Alex Xu Amazon:  https://amzn.to/3WJzwVV   - System Design Interview (Volume 2) by Alex Xu Amazon:  https://amzn.to/3M7zEtv   - Grokking the System Design Interview https://lnkd.in/ebEwFWbP - Grokking the Advanced System Design Interview https://lnkd.in/e_c2CWge - Donne Martin's System Design Primer https://github.com/krmadhukar/system-design-primer - Site Reliability Engineering: How Google Runs Production Systems https://lnkd.in/edYzQwXW - The Site Reliability Workbook: Practical Ways to Implement SRE https://lnkd.in/e9tKypna - Understanding Distributed Systems https://amzn.to/4fGzg2H - Fundamentals of Software Architecture - Mark Richards & Neal Ford https://amzn.to/3Xdozxv - Software Architecture: The Hard Parts - Mark Richards & Neal Ford https:...

  Free AI/ML LLM Fundamentals Course Save 1000s of dollars. Bookmark this and follow the curriculum below. You want to learn AI/ML LLM? I have curated the Best FREE AI/ML roadmap that covers essential knowledge about maths, Python, NNs with hands-on projects Learning. 🙏 Help me spread the free courses! Kindly like, repost and comment! ♻️ Google Courses https://www.cloudskillsboost.google/?qlcampaign=6y-in1-event-90 𝟭. 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀 𝗳𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 • Linear Algebra - 3Blue1Brown: https://lnkd.in/ejApha3z • Immersive Linear Algebra: https://lnkd.in/ekaUs4Wz • Linear Algebra - KA: https://lnkd.in/emCEHTq5 • Calculas - KA: https://lnkd.in/emCEHTq5 • Statistics and Probability - KA: https://lnkd.in/e6_SirMr 𝟮. 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 • Real Python: https://realpython.com • Learn Python - freecodecamp: https://lnkd.in/ejfBftNf • Python Data Science: https://lnkd.in/g4ZysfEe • ML for Everybody: https://lnkd.in/ehR6xaG...

1 Introduction Generative Al imp.i384100.net/5gNjVj 2. Generative AI with Large Language Models imp.i384100.net/k0qRez 2 a) React Fundamentals imp.i384100.net/9gYeRW 2 b) Angular: imp.i384100.net/eKWR9r 2 c) SEO: imp.i384100.net/xkGnW5 3. Generative Adversarial Networks (GANs) Specialization imp.i384100.net/DKNLPn 4. Introduction to Artificial Intelligence (AI) imp.i384100.net/QyQKoA 5. AI Engineering imp.i384100.net/9gYeRy 6. Natural Language Processing Specialization imp.i384100.net/rQPgZR 7. Deep Learning Specialization imp.i384100.net/jrL1k5 8. Generative AI for Data Scientists Specialization imp.i384100.net/k0qReN 9. IBM Data Science Professional Certificate imp.i384100.net/AWNK91 10. Introduction to Data Science imp.i384100.net/GmNDek 11. Learn SQL Basics for Data Science imp.i384100.net/Vm54E3 12. Excel for Business imp.i384100.net/g1EojB 13. Python for Everybody imp.i384100.net/B0MKrL 14. Machine Learning Specialization imp.i384100.net/WqkYnM 15. SQL for Data Science imp.i38410...

If I had to start learning Data Structures and Algorithms from scratch, I would begin with these 20 articles to get a head start: 1) 𝐓𝐢𝐦𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: https://lnkd.in/gWDD83fm 2) 𝐁𝐢𝐠-𝐎 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭: https://lnkd.in/gsaAWbSs 3) 𝐒𝐨𝐫𝐭𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬: https://lnkd.in/g9npW9JN 4) 𝐋𝐢𝐧𝐤𝐞𝐝 𝐋𝐢𝐬𝐭: https://lnkd.in/gXQux4zj 5) 𝐐𝐮𝐞𝐮𝐞: https://lnkd.in/gJaGSafT 6) 𝐒𝐭𝐚𝐜𝐤𝐬: https://lnkd.in/gBtqxeJH 7) 𝐇𝐚𝐬𝐡 𝐓𝐚𝐛𝐥𝐞𝐬: https://lnkd.in/gCfWr7Eg 8) 𝐇𝐞𝐚𝐩𝐬: https://lnkd.in/gS6SVF5A 9) 𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐨𝐧: https://lnkd.in/gQiasy8H 10) 𝐁𝐚𝐜𝐤𝐭𝐫𝐚𝐜𝐤𝐢𝐧𝐠: https://lnkd.in/g8Vge2p9 11) 𝐓𝐫𝐞𝐞: https://lnkd.in/gRfmJVDf 12) 𝐁𝐢𝐧𝐚𝐫𝐲 𝐒𝐞𝐚𝐫𝐜𝐡 𝐓𝐫𝐞𝐞: https://lnkd.in/g7QYyVWy 13) 𝐓𝐫𝐢𝐞𝐬: https://lnkd.in/gTp3n4CP 14) 𝐁𝐢𝐧𝐚𝐫𝐲 𝐒𝐞𝐚𝐫𝐜𝐡: https://lnkd.in/gKEm_qUK 15) 𝐆𝐫𝐞𝐞𝐝𝐲 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦: https://lnkd.in/gUMnuQ26 16) 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠: https://lnkd.in/gtXQsyXT 17) 𝐆𝐫𝐚𝐩𝐡 𝐓𝐡𝐞𝐨𝐫𝐲...

If I had to start learning System Design from scratch, I would begin with these 25 articles to get a head start: 1) Scalability: https://lnkd.in/gx-sPXZm 2) Horizontal vs Vertical Scaling: https://lnkd.in/gAH2e9du 3) Latency vs Throughput: https://lnkd.in/g_amhAtN 4) Load Balancing: https://lnkd.in/gQaa8sXK 5) Caching: https://lnkd.in/gC9piQbJ 6) ACID Transactions: https://lnkd.in/gMe2JqaF 7) SQL vs NoSQL: https://lnkd.in/g3WC_yxn 8) Database Indexes: https://lnkd.in/gCeshYVt 9) Database Sharding: https://lnkd.in/gMqqc6x9 10) Content Delivery Network (CDN): https://lnkd.in/gjJrEJeH 11) Strong vs Eventual Consistency: https://lnkd.in/gJ-uXQXZ 12) Batch Processing vs Stream Processing: https://lnkd.in/g4_MzM4s 13) Concurrency vs Parallelism: https://lnkd.in/gSKUm2Nh 14) Synchronous vs. asynchronous communications: https://lnkd.in/gC3F2nvr 15) Rest vs RPC: https://lnkd.in/gN__zcAB 16) CAP Theorem: https://lnkd.in/g3hmVamx 17) Reverse Proxy: https://lnkd.in/gFwWFDu8 18) Domain Name System...

  One of the golden rule of programming is : 💡 Don’t code today what you can’t debug tomorrow. Below some advices to improve yourself every day : 👉Master Your Tools: Become proficient in the programming languages, frameworks, and tools relevant to your field. 👉Problem-Solving Skills: Develop strong problem-solving skills to efficiently tackle coding challenges. 👉Debugging Proficiency: Sharpen your debugging skills to identify and fix issues quickly. 👉Algorithmic Understanding: Develop a strong understanding of algorithms and data structures for efficient problem-solving. 👉Code Readability: Write clean and readable code; it helps you and others understand and maintain it. 👉Time Management: Prioritize tasks, set deadlines, and manage your time effectively to stay productive. 👉Continuous Learning: Stay updated with industry trends, new technologies, and best practices to enhance your skills. 👉Testing: Embrace testing methodologies to ensure the reliability and correctness of ...

Regular Expressions List of meta characters: . ---> Any one character ? ---> Zero or one + ---> One or more * ---> zero or more ^ ---> at the beginning of the string $ ---> at the end of the string [abc] ---> any one of a b c {m} ---> 'm' times {m,n} ---> at least m times, at most n times | ---> or \ ---> escape sequence character \s ---> a space \d ---> a digit \w ---> a word \b ---> a word boundary examples: \d ---> a single digit number (0 to 9) \d\d ---> a two digit number (0 to 99) \d\d\d ---> a three digit number (000 to 999) NOTE: ?, +, *, {} are used as Quantifiers (to represent quantity) \d{3} ---> same as above \d{3,5} ---> either 3 digit or 5 digit number hell?o ---> helo | hello hell+o ---> hello | helllo | helllllo | ... hrll*o ---> helo | hello | helllo | helllllo | ... he(ll)+o ---> hello | hellllo | hellllllo | ... S = "hi hello how are hello" hello ---> Yes ^hello ---> No h...

As of Go 1.16, the   GO111MODULE   environment variable is treated as "on" by default, meaning Go expects to find a   go.mod   file, and no longer falls back to pre-module   GOPATH   behavior. If you want to go back to the pre-1.16 behavior, you now have to explicitly specify  GO111MODULE=auto , but you're far better off creating a  go.mod  file. See  https://golang.org/doc/go1.16#go-command  and  https://golang.org/ref/mod Source - https://stackoverflow.com/questions/67929883/go-error-go-go-mod-file-not-found-in-current-directory-or-any-parent-director