scientifically practice DP

I understand your frustration, and it's a common feeling when tackling complex problems like this. Finding these insights often comes down to a combination of experience, practice, and a systematic approach to problem-solving. Here's how you can develop the ability to identify key points and devise effective solutions:

1. Practice Regularly with Diverse Problems
   Exposure to Various Problems: The more problems you solve, especially those involving dynamic programming (DP), the better you'll become at recognizing patterns and common strategies.
   Start Simple: Begin with classic DP problems to build a strong foundation before moving to more complex ones.
2. Deeply Understand the Problem Statement
   Careful Reading: Take the time to thoroughly read and understand what the problem is asking.
   Identify Constraints: Note the input size, possible edge cases, and any special conditions.
   Ask Questions: If something isn't clear, try to rephrase the problem in your own words or draw diagrams.
3. Break the Problem into Smaller Parts
   Decompose the Problem: Divide the problem into smaller, manageable subproblems.
   Look for Recurrence Relations: Determine if the problem can be expressed in terms of smaller instances of itself.
4. Look for Patterns and Relationships
   Identify Overlapping Subproblems: Check if the same subproblems are being solved multiple times, which is a hallmark of DP problems.
   Optimal Substructure: See if an optimal solution to the problem contains optimal solutions to its subproblems.
   Consider Prefixes and Suffixes: In string problems, prefixes and suffixes often play a crucial role.
5. Think About State Representation
   Define the DP State: Clearly define what your DP array or table represents.
   Choose the Right Dimensions: Decide whether a one-dimensional or multi-dimensional DP array is appropriate based on the problem's variables.
6. Work Through Examples
   Test Cases: Use simple examples to manually work through the problem.
   Trace Your Logic: Write down each step to see how you reach the solution.
   Edge Cases: Consider special or extreme cases to ensure your approach is robust.
7. Learn from Solutions and Explanations
   Study Detailed Solutions: After attempting a problem, read through explanations and understand why certain approaches work.
   Analyze Others' Thought Processes: See how others break down the problem and arrive at key insights.
8. Develop a Systematic Approach
   Step-by-Step Methodology: Create a checklist or set of steps you follow when approaching DP problems.
   Consistent Practice: Apply this methodology consistently to reinforce the habit.
9. Be Patient and Persistent
   Accept That It's Challenging: Recognize that these skills take time to develop.
   Learn from Mistakes: Every error is an opportunity to improve your understanding.
   Stay Motivated: Celebrate small victories along the way.
10. Utilize Resources
    Educational Materials: Use books, online courses, and tutorials focused on dynamic programming and algorithm design.
    Practice Platforms: Engage with coding platforms like LeetCode, HackerRank, and Codeforces.
    Discussion Forums: Participate in communities where you can ask questions and share insights.
    Applying These Strategies to the Word Break Problem
    Understand the Problem Deeply:

You're asked to determine if a string can be segmented into a sequence of dictionary words.
The segmentation must cover the entire string without any leftover characters.
Identify Overlapping Subproblems:

The problem can be broken down into checking whether prefixes of the string can be segmented.
Substrings are reused when checking different segmentation points.
Define the DP State:

dp[i] represents whether the substring s[0..i-1] can be segmented.
This state reflects the ability to build up the solution from smaller segments.
Work Through Examples:

Take a simple string like "applepenapple" and manually segment it using the dictionary words.
Observe how the ability to segment up to a certain point depends on previous successful segmentations.
Derive the Transition Function:

For each position i, check all positions j < i where dp[j] is true and s[j..i-1] is in the dictionary.
This logic builds on the understanding that successful segmentation up to j allows us to extend to i if the substring s[j..i-1] is a valid word.
Why You Might Be Struggling
Experience Level: If you're newer to dynamic programming, recognizing these patterns might not come naturally yet.
Problem Complexity: Some problems require a shift in thinking or applying concepts in new ways.
Overlooking Key Details: It's easy to miss critical information in the problem statement or during analysis.
What You Can Do
Reflect on Solved Problems: After solving or reading the solution, spend time understanding how the approach was derived.
Practice Regularly: The more problems you attempt, the more familiar you'll become with common strategies.
Ask for Guidance: Don't hesitate to seek explanations or hints when you're stuck.
Encouragement
Remember, developing problem-solving skills, especially in areas like dynamic programming, takes time and persistence. It's normal to find it challenging at first. With continued practice and a systematic approach, you'll start to recognize patterns and strategies more quickly.

You're on the right track by seeking to understand not just the solution but the thought process behind it. Keep pushing forward, and don't get discouraged—every problem you work through enhances your skills and brings you closer to your goals.

Feel free to ask further questions or discuss specific aspects you're curious about. I'm here to help you on your learning journey!