Here's how you can master the most in-demand Algorithms skills in the job market.

Làm ch? các ?i?u c? b?n, th?c s? trên m?ng nói và d?y quá nhi?u v? thu?t toán c? b?n r?i. Mình thì mu?n nó mang l?i nhi?u y ngh?a h?n là ch? ?? ?i ph?ng v?n. N?u ai tham gia l?p c?a mình h?i còn ? Samsung thì s? bi?t là mình thích d?y m?i ng??i b?n ch?t v?n ??. ?i t? t?i sao chúng ta dùng m?ng, t?i sao c?n vòng l?p. Làm sao li?t kê t?t c? các tr??ng h?p có th? x?y ra. Hi?u vét c?n (BruteForce) là gì và t?i sao nó ??a ra ???c k?t qu? ?úng? Làm sao t?o ???c t?t c? các tr??ng h?p có th? x?y ra, có nh?ng lúc vòng l?p là ?? nh?ng có khi c?n thêm ?? quy cho ti?n h?n. (Mình còn trêu là for là ?? linh ho?t s? l?n l?p, ?? quy là ?? linh ho?t s? l?n for) Cái n?n t?ng s? là "t? duy ?úng ??n" tr??c. B?n có ch?c duy?t ?? k?t qu? ch?a?

A few thoughts on this topic: 1. Learning mind: No matter how many experience you have, always position yourself as a learner. Learning is a process throughout the life. 2. Deep thinking mind: proactively learning, with guts to challenge. Think deep about the limitations, assumptions and gaps for products can help you think deep. 3. Focusing mind: AI/ML field is undertaking evolutionary change. Do not seek to learn for the sake of learning. Focus on things that are relevant to your current projects. 4. Doing mind: Doing in learning is the most efficient and efficient way for learning. It is the core mind connecting the above points for you to connecting learning with insights and real impact .

Diving straight into the deep end, mastering in-demand algorithm skills starts with getting your hands dirty. Here’s the game plan: - Grasp the basics: Arrays, linked lists, and the gang. They're your best friends. Know them inside out. - Sorting & Searching: Start with bubble sort and binary search. They might seem simple, but they teach you the mechanics of efficiency. - Code, then code some more: Pick a language. Any language. Implement these algorithms from scratch. Mistakes? Good. Learn from them. - Real-world problems: Once comfortable, challenge yourself with problems from platforms like LeetCode or HackerRank. - Community interaction: Join forums, discuss with peers. - Patience: Solid understanding beats rushing every time.

1. Basics First: Nail down the basics like sorting, searching, and data structures. Check out some online courses to get your feet wet. 2. Level Up: Once you're comfy with the basics, dive into advanced stuff like dynamic programming and graph algorithms. There are some great books and resources out there. 3. Practice: Hit up sites like LeetCode and HackerRank. Solve problems regularly – it’s the best way to get better. 4. Hands-On Practice: Work on projects. Whether it's a personal project or contributing to open source, applying what you learn is key. Tech evolves fast. Follow blogs, join newsletters, and keep up with the latest trends.

When teaching students data structures and algorithms, I find it the most useful to implement them in a simple language, like Python. Even though, some data structures are part of the language implementing them manually brings a real understanding.

??n ?o?n này thì là gi?ng nh? h?c k? n?ng c? b?n ?y. Nh?ng b?n nào ?ang ?n thi thu?t toán mà theo LeetCode Blind75 thì s? th?y mình h?c t? m?ng t?nh m?ng ??ng, hi?u hash là gì, bi?t c? ch? c?a stack/queue, thu?t toán tìm ki?m (binarysearch) r?i thu?t toán ?? th? (ch? y?u là BFS DFS). Xong các m?o liên quan ?? x? ly bit và 2 pointer, sliding window. H?c xong các th? ?ó thì g?p ?ng anh khó nh?n quy ho?ch ??ng (DP). N?u ?? h?c thì nhi?u l?m nh?ng t?p trung vào nh?ng ph?n ph?n bên trên ?? giúp gi?i quy?t ph?n l?n bài toán và t?o d?ng ???c m?t n?n t?ng t?t và m?t t? duy c? b?n r?i. Nh?ng th? l?p l?i thì s? ???c t?o ra c?ng th?c, quy lu?t. Hi?u b?n ch?t xong thì s? d? hi?u, phan tích và bóc tách v?n ??.

To any given problem, I would personally suggest to first think of it how do we approach it on paper, what steps do I need to follow to solutionize it. Then narrow it down further mathematically as well as computationally thinking of the right data structure to be used. Once we start solving good number of problems, we would get a good hand at identifying the pattern of it, it will be easier to come up with the appropriate DS to be applied. Leetcode has a very good mechanism of showing the related/similar problems on the problem that you just submit and solve.

Mastering the base case is the most critical aspect of pattern recognition. In software engineering, programming, and coding, many principles serve as the foundation of other, more advanced principles. As a result if we do not master the basics, we cannot move to advanced areas. Regarding interview prep and algorithm mastery, I utilize Codingbat, Codewars, and GeeksForGeeks for some simple problems. Start by solving the simplest possible questions and then move on to more difficult ones. Disclaimer: Only do Leetcode if you have a strong foundation. Leetcode is hard, and it takes practice to get there. I encourage Codingbat first, followed by GeeksForGeeks or Codewars

Study Common Patterns: - Learn patterns like sliding windows, two pointers, merge intervals, and cyclic sort. - Resources: "Cracking the Coding Interview" by Gayle Laakmann McDowell, Grokking the Coding Interview on Educative.io. Practice Regularly: - Focus on one pattern at a time, solving multiple problems using that pattern. - Use platforms like LeetCode, GeeksforGeeks, and InterviewBit to find pattern-based problems. Identify and Apply Patterns: - Learn to recognize patterns in problems and apply the right one. - Review and analyze solutions to understand the application of patterns and their complexities. Continuous Improvement: - Regular practice and engaging with coding communities (e.g., Stack Overflow, Reddit) to stay updated.

Algorithms often follow certain patterns. Familiarize yourself with common algorithmic patterns such as two-pointer, sliding window, divide and conquer, dynamic programming, greedy algorithms, and backtracking. Recognizing these patterns can help you approach problems more effectively.

Xem gi?i kh?ng sai, nh?ng xem xong mà kh?ng th?c hành r?i ??n lúc dùng t?i l?i kh?ng làm ???c thì sai l?m nhé. N?u vào m?t bài t?p kh?ng gi?i ???c thì c? dùng th?i gian suy ngh?, kh?ng ngh? ?c n?a thì xem gi?i, xem gi?i xong hi?u v?n ?? thì h?y th?c hành nó. Th?c s? thì tìm gi?i pháp là k? n?ng mà b?t k? l?p trình viên nào c?ng c?n có, tìm xong thì c?n tri?n khai ???c n?a. Ch? khi th?c s? b?t tay vào code m?i th?y m?t ??ng v?n ?? m?i mà lúc ngh? ch?a th?y có. Ngoài ra thì còn nên luy?n các k? n?ng clean code n?a nhé. Code sao cho d? maintain c?ng k d? ?au. Và trong thu?t toán có 1 cái g?i là ?? ph?c t?p tri?n khai, n?u mình làm t?t v? tri?n khai thì s? gì n?a

I believe that practicing coding is essential for continuous learning and staying updated with the latest advancements in our field. Regular coding practice helps you upskill and solidify your understanding of new concepts. It also provides hands-on experience, confirming the theoretical knowledge you gain from documents and resources. By consistently writing and debugging code, you not only improve your problem-solving skills but also gain confidence in your abilities. Embracing a routine of coding practice is a guaranteed way to ensure you're always at the top of your game and ready to tackle any challenge.

Mastering algorithms isn't just about tackling every challenge head-on; it's about strategic practice, a principle often overlooked. Here's a blueprint for success: - Diversify your problems. Don't just stick to what you know. Tackle different kinds of algorithmic puzzles - from sorting and searching to graph theory and dynamic programming. - Time yourself. Simulate real interview conditions. Start with a generous limit but gradually reduce this as you improve. - Reflect on solutions. After solving a problem, don't rush to the next. Take time to review your code. Could it be more efficient? Is there a better approach? - Embrace failure. Encountering problems you can't solve is normal. Use these moments as learning opportunities.

Plus on pratique plus les réflexions de résolution seront intuitifs peu importe le langage de programmation le plus important est le raisonnement algorithmique

- Study advanced patterns like divide and conquer, dynamic programming, and backtracking. - Resources: "Algorithms" by Robert Sedgewick, "The Algorithm Design Manual" by Steven Skiena. - Tackle problems of increasing difficulty on platforms like LeetCode, Codeforces, and HackerRank. - Participate in coding competitions (e.g., Google Code Jam, ACM ICPC). - Apply your knowledge by building projects that solve real-world problems. - Contribute to open-source projects on GitHub to gain practical experience. - Practice with mock interviews on Pramp, Interviewing.io. - Focus on explaining your thought process and optimizing your solutions. - Stay updated with new algorithms and techniques by following blogs, and YouTube channels.

être sensibilisé à la complexité permettra de résoudre des problèmes à travers des solutions prenant en considération un compromis entre temps d exécution et utilisation mémoire

If you ever need a reason why to learn Data structure and Algorithm this is a good one. If you find yourself that your code is making the application slow, try to identify all the places that you can improve it, It is very likely that you are using a Data structure that does not support pretty well the way that you are querying it. So always before implement some functionality to your application ask yourself, How am i going to query this data? so you can optimize that use case As we all know the famous phrase "Premature Optimization is the root of all evil" Begin with a Data structure simple enough to develop your feature, do not try to over complicate things at the beginning, and iterate on your solution once you identify bottle necks.

Time Complexity: -Big O Notation: Measure of an algorithm's worst-case performance in terms of the input size. -Example: O(n) for linear search, O(n^2) for bubble sort. Space Complexity: - Memory Usage: Measure of the amount of memory an algorithm uses concerning the input size. - Example: O(1) for constant space algorithms, O(n) for algorithms with linear space usage. - Helps evaluate algorithm efficiency and scalability. Guides in selecting the most suitable algorithm for a given problem. Analyzing Complexity: - Identify loops, recursive calls, and data structures. - Calculate the number of basic operations executed concerning input size. Resources: - Study materials like "Introduction to Algorithms" by Cormen.

Knowing how to analyze the time and space complexity of algorithms is essential. This helps you choose the most efficient algorithm for a given problem. Big O notation is a standard way to describe the complexity, and understanding it can greatly enhance your ability to write optimized code.

Aunque a veces se me pasa por alto la notación Big O en primera instancia, siempre procuro refactorizar mi código para mejorar su eficiencia. Ser consciente de la eficiencia de los algoritmos me ha permitido crear soluciones más escalables y robustas.

Algorithms, data structures and complexity is the building blocks of systems and software in general. But it is very important to remember that those are the core building blocks and learning how and where to apply those building blocks is just as important as understanding them. A good place to apply and understand those building blocks could be open-source communities where one could see and learn how those blocks are used in real world applications.

Apply algorithms to real-world problems. This could be through projects, contributions to open-source software, or at work. Real-world applications give you a deeper understanding of why and how algorithms are used, and help you see their impact.

Aplicar algoritmos en proyectos reales es la mejor manera de consolidar los conocimientos. Trabajar en proyectos reales y personales no solo da experiencia práctica, sino que también demuestra habilidades aplicadas en contextos prácticos. Además, enfrentar desafíos del mundo real proporciona una comprensión más profunda de las limitaciones y fortalezas de los algoritmos.

Finding problems to solve with what you’ve learned can be a valuable exercise. You’ll find out, that some use cases you initially thought would be a good exercise for your newly learned skills, will not be solved feasibly with what you’ve learned that far. This will force you to learn new algorithms and methods to solve various different real problems. You may even notice after some experience, that more simpler algorithms or their combinations can be more feasible for solving real world problems instead of the initial complex algorithm you’ve invested your time to master.

Th?c s? thì ki?n th?c thay ??i t?ng ngày. Các ki?n th?c n?n t?ng kh?ng thay ??i nh?ng các ?ng d?ng m?i c?a nó ???c t?o ra nhi?u v? cùng. Nh?ng viên g?ch và n?n t?ng ?ó ???c k?t h?p và t?o ra các c?ng c? ?? gi?i quy?t nh?ng v?n ?? m?i. Vi?c c?p nh?p các ki?n th?c và y t??ng m?i mang l?i r?t nhi?u l?i ích trong vi?c phát tri?n t? duy và có thêm cho mình nh?ng l?a ch?n v? c?ng c? ?? gi?i quy?t bài toán/v?n ??. Nói riêng v? l?p trình c? b?n th?i ch? ch?a c?n ?i sau vào các b?n chuyên làm AI/ML n?a. Nên h?y có cho mình m?t t? duy m? và c?p nh?p thêm nhi?u ki?n th?c n?a nhé.

- Subscribe to tech news websites, blogs, and newsletters (e.g., TechCrunch, Wired, Ars Technica). - Participate in forums like Stack Overflow, Reddit, and Quora to ask questions and learn from others. - Join professional groups and communities on platforms like LinkedIn and Facebook. - Register for webinars, virtual conferences, and workshops organized by industry experts and companies. - Follow YouTube channels and educational websites for free tutorials and instructional videos. - Participate in hackathons and coding challenges to apply your skills and learn from others. - Connect with professionals in your field through networking events, LinkedIn, and professional organizations.

El aprendizaje continuo es esencial en nuestro campo. Me mantengo al día siguiendo blogs, participando en foros y tomando cursos en línea. Esto no solo me mantiene actualizado, sino que también me motiva a seguir mejorando y aprendiendo.

In a world where technology is constantly evolving by creating new software or languages, the basics of programming remain unchanged. I find comfort in knowing that regardless of the language, I can still utilize the same skills from problem-solving to tackle any challenge I may face. In addition, constantly staying updated with algorithms can lead to job retention, higher-paying jobs, or finding a new career in the case of a bad market.

Whenever you learn an algorithm, go and try to implement it on some judge on some problem, so that your algo can be tested to be correctly implemented. And that implementation store in some github repository so that can be reusable at sometime letter

Thu?t toán ngoài vi?c ch? dùng ?? ph?ng v?n thì nó còn là m?t t? duy có th? ???c m? r?ng ra ?? có th? phan tích và áp d?ng vào nhi?u khía c?nh c?a m?t l?p trình viên n?a. T? duy b?n ch?t v?n ??, cách gi?m vùng tìm ki?m, t? duy hi?u n?ng... ?? th?c s? master ???c m?t ?i?u gì ?ó chúng ta c?n tìm ra cách ti?p c?n nó phù h?p v?i b?n than mình. Hi?u b?n than và t?i ?u ngay t? nh?ng ?i?u c? b?n, nh? nh?t

Mastering the most in-demand algorithm skills in the job market requires a focused and strategic approach. Here’s how you can achieve that: 1. Deep Learning and Machine Learning: Focus on classical and neural networks and practical implementation using TensorFlow and PyTorch. 2. Statistical Signal Processing: Learn noise reduction, signal detection, and parameter estimation techniques. 3. Advanced Algorithm Development: Engage in cutting-edge research and contribute to publications and patents. 4. Continuous Learning and Networking: Stay updated with advancements and join AI and signal processing communities.

1. Learn Core Algorithms: Master fundamental algorithms and data structures, such as sorting, searching, and graph algorithms. 2. Understand Machine Learning: Gain proficiency in machine learning algorithms, including supervised and unsupervised learning techniques. 3. Practice Coding: Enhance your coding skills through regular practice on platforms like LeetCode, HackerRank, and CodeSignal. 4. Stay Updated: Keep abreast of the latest trends and advancements in the field by following relevant publications, attending conferences, and taking online courses. 5. Work on Projects: Apply your skills to real-world projects, contributing to open-source repositories or developing personal projects to build a robust portfolio.

* Stay tuned with state-of-the-art academic orientation, by journal clubs, reading papers' routine and conferences. * Keep it simple- use an appropriate algorithms but try do not over-complicate your solutions. * Modularity- split your business-ligic to manageable parts, to achieve an explainabilty and in order to debug easily and futural improvements. * Expand your expertise- following software architecture principles and professional development process should be your guidance, a knowledge in production level tools such as as git, docker and k8s, make you a better algorithms developer.