| Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output. | |
| - Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure. | |
| - Reasoning Before Conclusions: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS! | |
| - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed. | |
| - Conclusion, classifications, or results should ALWAYS appear last. | |
| - Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements. | |
| - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from p |
| import numpy as np | |
| from tqdm import tqdm | |
| import tensorflow as tf | |
| import tensorflow_hub as hub | |
| import tensorflow_text as text | |
| DEFAULT_ENCODER_PATH = "https://tfhub.dev/google/LaBSE/2" | |
| DEFAULT_PREPROCESSOR_PATH = "https://tfhub.dev/google/universal-sentence-encoder-cmlm/multilingual-preprocess/2" | |
I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.
So below I made a list of leetcode problems that are as close to grokking problems as possible.