By the time the deadline arrived, my colleague had already optimized the AI model. He boasted about his Python skills, claiming he had built the best neural network. I hadn’t been so confident—my code was inefficient, and I wished I were more proficient in TensorFlow. If only I had trained the model better, its accuracy would have improved. She asked if I had debugged the algorithm properly. "Do you think switching to PyTorch would make the performance better?" I replied, "I would rather write cleaner code than rush development." The syntax was complex, and my colleague must have noticed my struggle. Had he been less skilled, he wouldn’t have debugged it so quickly. Now, the model runs accurately, but I still wish I had been a better coder. If I were more experienced, my projects would be more efficient. The machine learning developer told me, "You need to practice more." I knew she was right.
Team struggles with AI deadline
English
The team was concerned about the project deadline. They had trained an AI model, but its accuracy wasn’t good enough. My teammate had developed a framework in Python, but the algorithm was too slow. She wished she had optimized it earlier. If only they had more resources, they could have refactored the code faster. The manager asked, "Would deploying a new library improve the model’s performance?" He mustn’t have ignored the deep learning approach—now they had a problem.
I told him, "You should have solved this with better data." He objected, saying the dataset was fine. If he weren’t so stubborn, we would have succeeded. My teammate sighed, "If we had refactored the framework, the AI would run faster." The manager wished he had approved the changes sooner.
I looked at my code. It could be better. "May I try to optimize it?" I asked. He nodded. By tomorrow, we will have deployed the improved model. Would they be envious of my solution? Maybe. But accuracy mattered more.
Tight deadline coding struggle
English
The deadline was tight, and we hadn’t updated the library before importing it. My teammate asked, *Will we finish the script by the meeting?* I wasn’t sure. The function crashed unexpectedly, and we couldn’t debug it. She adjusted the parameters, but the model still wouldn’t run. *If only we had tested the dependencies earlier,* I thought. The code was complex, and the bug might be in the imported library. My colleague managed to fix part of it, but the solution wasn’t perfect. *We must reach a solution before the deadline,* she said. By then, the developer team had improved the script. *Should we adjust the virtual environment?* I asked. They said yes, but time was running out. The program finally succeeded—just before the crash happened again. *I’m not sure we can manage this,* I admitted. *If we had set the right parameters from the start, we wouldn’t be debugging now.* Her debugging solution helped, but the dependencies were still wrong. *You must have missed something,* she said. We reached the end of the day, exhausted. *The function is working,* my teammate announced. But could it handle the final test? Only time would tell.
Sample flashcards 20
የፓይዘን አገባብ→the Python syntax
ኮድ ማስወገድ→to debug code
የፓይዘን አገባብ እንዲህ የተወሳሰበ እንዳይሆን እመርጣለሁ።→I would rather the Python syntax not be so complex.
የአርቴፊሻል ኢንተለጀንስ ስልተ ቀመር→the AI algorithm
የአርቴፊሻል ኢንተለጀንስ ስልተ ቀመር የበለጠ ውጤታማ እንዲሆን ትመርጣለህ?→Would you rather the AI algorithm be more efficient?
ገንቢው የአርቴፊሻል ኢንተለጀንስ ስልተ ቀመር የበለጠ ትክክለኛ እንዲሆን ይመርጣል።→The developer would rather the AI algorithm be more accurate.
አንድ ነርቫ ኔትወርክ→a neural network
ሰራተኛዬ ፕሮጀክቶችን ከመቀየር በፊት አንድ ነርቫ ኔትወርክ ሠርቷል።→My colleague had built a neural network before switching projects.
የማሽን ትምህርት ሞዴል→a machine learning model
ከጊዜ ገደቡ በፊት በጣም �ሚዛናዊ የሆነ የማሽን ትምህርት ሞዴል አልሰለጠነም።→He hadn't trained a very accurate machine learning model before the deadline.
�ፋጥናውን ማመቻቸት→to optimize performance
እሷ አፈጻጸሙን እስከማመቻቸቷ ድረስ ሞዴሉ ተሻሽሏል።→By the time she optimized performance, the model had improved.
ሞዴል ማሰልጠን→to train a model
እኔ ሞዴሉን እስከማሰልጠን ድረስ ትክክለኛነቱ አልተሻሻለም።→The accuracy hadn't improved by the time I trained the model.
ስለ እራሱ መጉራራት→to boast of
እሷ እራሷን እንደ ምርጥ ፕሮግራመር አልገላገለችም።→She hadn't boasted of being the best programmer.
ውጤታማ ያልሆነው ኮድ→the inefficient code
ውጤታማ ያልሆነ ኮድ መጻፍ→to write inefficient code
ከውጤታማ ያልሆነ ኮድ ከመጻፍ በፊት እራሱን እንደ አዋቂ ኮደር ገልጧል?→Had he boasted of being a skilled coder before writing inefficient code?
በሆነ ነገር ብቁ መሆን→to be proficient in
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