¿Cuál es la mejor manera de hacer un seguimiento de tu progreso durante un desafío de algoritmo?

When I solve algorithm based questions, the initial goal I set for myself is to develop a brute-force solution first before trying to optimize. There will be many cases where you may try to create something more optimal and cannot solve it. I always like to have the brute force solution as a worst-case scenario, so that I have something to submit rather than nothing. Solving the brute force can also give you hints as to how to solve it the most optimal way, since you would consider from different angles how you could do it better, rather than creating an efficient solution with no prior context of what a working solution looks like.

Regularly log your solved problems, nothing the time taken and any insights gained. Maintain a problem-solving journal to reflect on strategies, mistakes, and optimizations. Set measurable goals, like solving a certain number of problems per week. Join online coding communities to discuss solutions and learn alternative approaches.

When tackling algorithm challenges, setting clear goals is crucial for effective progress tracking. Begin by defining specific objectives, such as mastering a particular data structure or solving a certain number of problems weekly. Break down larger goals into smaller manageable tasks, ensuring a steady learning curve. Maintain a log of your solutions and revisit them for reinforcement. Regularly assess your performance by measuring completion time, accuracy and understanding of underlying concepts. Establishing a systematic approach and adapting it ensures a structured and measurable path to improvement in algorithmic problem-solving.

Understand: Grasp the problem, e.g., reverse a string. Plan: Devise an approach, e.g., iterate backward. Code: Implement the plan, e.g., reversed_str = str[::-1]. Test: Verify with examples, like reversing "hello" to "olleh". Optimize: If time allows, consider improvements. Debug: Resolve issues using debugging tools or print statements. Edge Cases: Handle scenarios like empty strings. Time Management: Prioritize a functional solution, then optimize if time permits.

Learn how to evaluate the efficiency of an algorithm using concepts such as time and space complexity, asymptotic notation, and recurrence relations. This will help you compare different solutions and choose the best one for your problem. Practice implementing data structures and algorithms by hand, using your preferred programming language. This will help you improve your coding skills and become familiar with the syntax and features of the language. Review the solutions and explanations of other people who have solved the same problem. This will help you learn from their mistakes, insights, and techniques. You can also ask for feedback on your own solutions and learn how to improve them.

Like setting checkpoints in a race, start by defining clear, achievable goals for your algorithm challenge. Are you aiming to improve your problem-solving speed, tackle more complex problems, or learn a new programming language? Your goals will guide your progress and give you specific targets to aim for.

Consistently log your problem-solving journey by documenting each step, noting insights, and recording time spent. Use version control for code history, and periodically review and refactor your solutions for continuous improvement. Tracking progress fosters learning and enhances problem-solving skills.

Keeping the scientific approach with any kind of problems will help enough. Setting an iterative and structured process of simulating possible solutions with good amount of imagination and application of technical approches will simply lead to success.

Define clear goals, measure success with metrics like problem-solving efficiency, and set personal benchmarks for continuous improvement in challenges.

Define specific, measurable, and achievable goals before starting the challenge. Break down the challenge into smaller tasks or problems to solve.

Having some journal/notebook/spreadsheet to look back on and track your progress does wonders. Not only does it give you the tools to go back and iterate on past performance, but it serves as motivation! How awesome is it to finally crack a problem that's stumped you many times in the past? Personally, I use a tracker on Notion, tracking the name of the problem, perceived difficulty level, time taken to solve, and my notes. The things you specifically track are completely up to you, but track whatever metrics you think will motivate you. Whatever setup you use, the main thing is to just be consistent!

When I am preparing for interviews I use a google sheet where I sort different algorithm problems form Leetcode/Neetcode into different topics so I can better recognize patterns. For example, I would do 3 easies, 3 mediums, and 3 hards all for linked list problems and put them in a spreadsheet to track my progress. This not only helps me remember what questions I already solved, but helps me be able to come back to these questions when I want to refresh my knowledge next internship cycle. By sorting into topics I am better able to recognize patterns.

A notebook and a spreadsheet are both tools that can help you organize and analyze data, but they have different features and benefits. A notebook is a file that contains one or more worksheets, which are grids of rows and columns where you can enter data and perform calculations. A notebook is like a container that holds all your Excel files. You can use a notebook to store data from multiple sources, create charts and graphs, and apply formulas and functions to your data. A notebook is useful for tasks such as creating budgets, tracking expenses, managing inventory, and performing financial analysis.

Use a notebook or spreadsheet to track your challenge progress efficiently. Create templates with key details, employ visual cues for solution status, and regularly review to spot errors and areas for improvement.

Keep a journal or a digital spreadsheet as your logbook. Note down each problem you solve, the date, the difficulty level, and any specific algorithms or data structures used. This is like mapping your journey, allowing you to track your progress, patterns, and areas where you're most frequently challenged.

I find that using a notebook or spreadsheet is one of the simplest and most effective ways to track my progress during a challenge. I create a template with fields such as problem name, description, input, output, code, test cases, score, and comments. Employing various colors or symbols to denote solution status (solved, unsolved, partial, optimal, suboptimal) makes it easy to review my work, identify errors, compare solutions, and understand where improvements are needed. Personally, I prefer using a notebook to meticulously document each step of the process.

Some online tools and platforms that can help you with coding, testing, and debugging. There are many options available on the web, depending on your needs and preferences. Here are some of the best tools. JSBin: A simple JavaScript debugging tool that allows you to collaborate with other developers and share your code snippets. It supports pre-processors like Markdown, Jade, and SCSS. jsFiddle: A popular tool for testing codes online, especially for web development. It supports a wide range of JavaScript libraries and frameworks and provides an option to include external resources. It also has a collaboration feature for real-time code sharing.

Platforms like LeetCode, HackerRank, or CodeSignal offer built-in progress tracking. They provide statistics on your performance, such as the number of problems solved, success rate, and ranking among peers. These tools are like fitness trackers for your coding brain, offering a detailed view of your improvement over time.

Other than using online tools, please develop your own way of tracking the progress as you go and solve more problems. This way you can compete against yourself to see how you have improved over a period of time.

If you're solving algorithm challenges on platforms like LeetCode, HackerRank, or CodeSignal, these platforms often provide tools to track your progress. They may have dashboards, history, and statistics to help you analyze your performance.

So I will tell you my personal experience in this in college I used to participate in coding challenges hosted by various platform during the challenge I tried to give my 100 % during my challenges which is very important but whats more important is analysing your mistakes when the challenge is over, even if you successfully solved a question, see other successful submissions as well because those solutions might give you a perspective to solve a problem which you never thought of

After you finish a challenge, you should review your solutions and feedback to see what you did well and what you can improve. You should analyze your code for readability, efficiency, correctness, and elegance. You should also compare your solutions with the official or optimal solutions, or with the solutions of other participants. You should also read the feedback from the judges or the community, and see if they point out any errors, bugs, or inefficiencies in your code. You should also reflect on your experience and identify the challenges, difficulties, and learning points that you encountered.

After solving a problem, take time to review your solution. Compare it with others’ solutions or official solutions if available. Reflect on feedback or comments from peers or mentors. This review process is crucial for understanding different approaches and learning from them.

After finishing a challenge, I thoroughly review my solutions and comments to assess strengths and areas for improvement. I analyze code readability, efficiency, correctness, and elegance. Comparing with official or peer solutions provides valuable insights. Reading judge and community feedback helps identify errors and inefficiencies. Embracing criticism, I strive to enhance my skills. Reflecting on the experience, I pinpoint challenges and key learning points, ensuring a continuous improvement in my problem-solving abilities.

It is painful to see the cracks in our implementation but then we need to see it as an area of improvement and learn from the mistakes and gaps. Only when learn can we adapt and make changes to make the code more robust and long-term oriented.

Every misstep is a learning opportunity. When you encounter difficulties or make mistakes, delve deeper into that area. It could be a complex algorithm or a programming concept you're not familiar with. Understanding and overcoming these gaps is key to improving your overall performance in algorithm challenges.

Keep track of common mistakes or challenges you encounter. Reviewing and learning from these mistakes is crucial for improvement.

The key to tracking progress in a challenge is learning from mistakes and gaps. Rather than getting discouraged, I use them as motivation to practice more, study intensively, and seek feedback. Actively searching for resources covering relevant topics, I apply newfound knowledge to similar problems, aiming for more efficient solutions. This continuous cycle not only strengthens my skills but also builds resilience and perseverance in the face of challenges.

• Time Segmentation: Allocate time for understanding, planning, coding, and testing. • Problem Decomposition: Break down challenges into manageable sub-problems. • Code Comments: Explain your logic with comments for clarity. • Version Control (Git): Track changes and revert if needed. • Test Cases: Validate solutions with diverse test scenarios. • Learning Documentation: Summarize key takeaways in a learning log. • Code Reviews: Seek feedback for diverse perspectives. • Progress Journal: Maintain a log of challenges and solutions. • Online Platforms: Use platforms with progress tracking features. • Self-assessment: Regularly evaluate and improve strategies.

When you're working on a problem, it's not just about finding the answer. It's also about understanding how things work and getting better at solving problems. Think about how long your solutions take to run. This helps you see if they're efficient. Plan specific times to work on different parts of the problem. This way, you use your time well and don't spend too much time on one thing.

In a computer vision project focused on color analysis, I encountered a challenge. While implementing a sophisticated model to classify images based on color attributes, the lack of transparency in decision-making became apparent. To address this, I integrated color feature visualization techniques. This not only improved model acceptance but also provided valuable insights into how color features influenced classifications, underscoring the importance of interpretability in computer vision applications.

Balance is vital. Don’t focus only on speed or quantity. Quality of understanding is just as important. Also, participate in coding competitions or timed challenges to simulate real-world scenarios. This can help improve your problem-solving under pressure. Finally, remember to take breaks and avoid burnout – your brain needs rest to assimilate new knowledge and skills effectively.

Be flexible and willing to adapt your approach if a particular strategy is not working. Iterate on your solutions and incorporate feedback from practice. Maintain a positive mindset. Challenges are meant to be learning experiences, and progress may not always be linear. Embrace the learning process.