機械学習構文150


1. **It is quite natural that different machine learning models produce different predictions.**  
2. **It seems that the model's loss will decrease soon after optimization.**  
3. **It is said that deep learning models have good memory for recognizing patterns.**  
4. **It never occurred to me that AI could generate realistic paintings.**  
5. **It doesn't matter if training takes time as long as the model learns well.**  
6. **It goes without saying that a larger dataset improves model performance.**  
7. **I found it hard to fine-tune a deep learning model.**  
8. **I took it for granted that a pre-trained model would work well on my dataset.**  
9. **It took the AI model several hours to complete training.**  
10. **It will cost a company a significant amount to develop a custom AI model.**  
11. **It has been five years since deep learning became mainstream.**  
12. **It will not be long before quantum computing influences machine learning.**  
13. **It is easy for neural networks to overfit when trained on small datasets.**  
14. **It is very kind of you to share your dataset for research.**  
15. **The experiment is to be conducted using a reinforcement learning algorithm.**  
16. **I was about to deploy the model when I found a bug.**  
17. **Data quality and feature engineering have to do with model accuracy.**  
18. **My model failed to generalize to unseen data.**  
19. **He sat in the front in order to analyze the model’s training process.**  
20. **He was kind enough to explain how backpropagation works.**  
21. **The dataset was too large for me to process on my laptop.**  
22. **I checked the model’s predictions, only to find they were biased.**  
23. **She couldn't help but be amazed by AI-generated images.**  
24. **You'll soon get used to training deep learning models.**  
25. **There's no telling how AI will evolve in the next decade.**  
26. **There is no point in using outdated models for modern tasks.**  
27. **Neural networks are worth studying for their versatility.**  
28. **I am in the habit of running machine learning experiments daily.**  
29. **He makes a point of validating his model with real-world data.**  
30. **Feeling frustrated, he adjusted the model's hyperparameters.**  
31. **Shocked by the results, she retrained the model.**  
32. **The training phase being complete, the model was deployed.**  
33. **All things considered, transfer learning is highly efficient.**  
34. **Judging by the performance metrics, the model is underfitting.**  
35. **The algorithm looks simple considering its efficiency.**  
36. **Geoffrey Hinton spent decades studying neural networks.**  
37. **She had great difficulty debugging the neural network.**  
38. **You just have to preprocess the data properly.**  
39. **I have yet to implement reinforcement learning in my project.**  
40. **He used to rely on traditional machine learning before switching to deep learning.**  
41. **When I started coding, I would often forget to optimize my models.**  
42. **He must have forgotten to normalize the dataset.**  
43. **The researcher may well be interested in explainable AI.**  
44. **Since cloud computing is available, we may as well train larger models.**  
45. **You had better validate your model before deploying it.**  
46. **I saw an AI model generating realistic text.**  
47. **She heard her name mentioned in a machine learning paper.**  
48. **My professor made me test the model with more data.**  
49. **My mentor forced me to document my AI experiments properly.**  
50. **I will have my assistant collect more training data.**  
51. **She got the chatbot to generate meaningful responses.**  
52. **Jane had her dataset corrupted due to a system crash.**


1. **The AI model helped me classify the images correctly.**  
2. **There are many data points plotted on the graph.**  
3. **Can you make yourself understood by the machine learning model?**  
4. **The training process kept me waiting for a long time.**  
5. **The AI was analyzing data with its parameters tuned.**  
6. **The optimizer helps those who fine-tune their models.**  
7. **I gave the model what little training data I had.**  
8. **Feature engineering is to a model what fuel is to a car.**  
9. **This model is not what it was before fine-tuning.**  
10. **Optimization is what deep learning is all about.**  
11. **Tell me what your AI experiment results were like.**  
12. **This algorithm is what we call an "adaptive learning system."**  
13. **This dataset is useful, and what is more, very diverse.**  
14. **I adjusted the parameters, which improved accuracy.**  
15. **She prefers decision trees, which preference I do not share.**  
16. **The model failed to converge, as is often the case with small datasets.**  
17. **A technique that I thought was outdated improved performance.**  
18. **This model is as efficient as any state-of-the-art algorithm.**  
19. **The new AI model is 25 times as fast as the previous version.**  
20. **My dataset is ten times the size of the benchmark dataset.**  
21. **This AI framework is as flexible as any deep learning library.**  
22. **Nothing is as important to a neural network as quality data.**  
23. **Model training is not so much a task as an iterative process.**  
24. **The accuracy of this model is higher than that of the baseline.**  
25. **The more data the model trains on, the better it performs.**  
26. **I trust this prediction all the more because the model is robust.**  
27. **This algorithm can no more generalize than a hard-coded rule can.**  
28. **Feature selection is no less important than model selection.**  
29. **This AI chip is no bigger than a coin.**  
30. **If the dataset were larger, the model would generalize better.**  
31. **If the model had more training epochs, it would have achieved better accuracy.**  
32. **If the dataset should contain noise, the model will fail to learn properly.**  
33. **If I were to build an AI system again, I would use a different architecture.**  
34. **If it were not for labeled data, supervised learning would be impossible.**  
35. **Without training data, a model cannot learn.**  
36. **I wish my model could learn faster.**  
37. **I would rather use PyTorch than TensorFlow.**  
38. **This model behaves as if it were fully trained.**  
39. **It's about time we evaluated the model on real-world data.**  
40. **A good data scientist would not ignore feature selection.**  
41. **Very few algorithms can achieve 100% accuracy.**  
42. **His model contains few, if any, misclassified samples.**  
43. **This AI is not a rule-based system but a deep learning model.**  
44. **This model is not only accurate but also computationally efficient.**  
45. **More training data is not necessarily better if it's noisy.**  
46. **I don’t use this model only because it's popular.**  
47. **We cannot be too careful when handling biased data.**  
48. **The AI never makes a prediction without uncertainty.**
1. **The model's prediction is far from accurate.**  
2. **Who knows what the AI will generate next?**  
3. **This algorithm would be the last one to misclassify data.**  
4. **The AI system knows better than to overfit on the training data.**  
5. **Every neuron adjusted its weights so that the network could minimize loss.**  
6. **The model was so complex that it took hours to train.**  
7. **It was such a large dataset that processing took a long time.**  
8. **Store backup data in case of a system failure.**  
9. **He saved the weights lest the training process fail.**  
10. **On finishing training, the model was tested on unseen data.**  
11. **As soon as the loss reached a threshold, training stopped.**  
12. **The optimizer had hardly adjusted parameters when convergence was achieved.**  
13. **No sooner had I tuned the hyperparameters than accuracy improved.**  
14. **We had not waited long before the model started performing well.**  
15. **It was last week that we deployed the AI model.**  
16. **What we need is high-quality training data, not just more samples.**  
17. **All you have to do is fine-tune the model.**  
18. **You can build whatever neural network architecture you want.**  
19. **Little did I expect the model to generalize so well.**  
20. **So large was the dataset that we needed distributed computing.**  
21. **It was not until more epochs were added that the model improved.**  
22. **"I use PyTorch." --- "So do I."**  
23. **"I don’t use TensorFlow." --- "Neither do I."**  
24. **It is true that deep learning requires large datasets, but preprocessing is also essential.**  
25. **Even if the model underfits, we will still deploy it for testing.**  
26. **Whether you fine-tune it or not, the pretrained model is already powerful.**  
27. **To reduce loss is difficult, if not impossible.**  
28. **I'm going to deploy the model today no matter what happens.**  
29. **Whoever trains the model, wherever they train it, the results matter.**  
30. **Confusing as the dataset was, the model learned meaningful patterns.**  
31. **This dataset remains biased, while the new one is more balanced.**  
32. **I tested two models. One used CNNs, and the other used transformers.**  
33. **Some researchers favor decision trees, while others prefer neural networks.**  
34. **Suppose that the training fails, how will you proceed?**  
35. **You can use this dataset as long as it remains unbiased.**  
36. **As far as we know, this is the best-performing model.**  
37. **As far as I am concerned, I prefer reinforcement learning.**  
38. **This AI won't generalize well unless it is trained on diverse data.**  
39. **The fact is that interpretability matters in AI.**  
40. **Chances are the model will fail in real-world scenarios.**  
41. **Let's retrain the model. After all, the loss is still high.**  
42. **Now that the model is trained, we can evaluate its performance.**  
43. **That misclassification cost the company significant revenue.**  
44. **A moment's analysis will reveal the underlying bias in the data.**  
45. **This algorithm will guide you through the optimization process.**  
46. **Data corruption prevented the model from being deployed.**  
47. **Hyperparameter tuning keeps the training process optimized.**  
48. **This loss function will make the model converge faster.**  
49. **The researcher believes that deep learning should be explainable and that interpretability is crucial.**  
50. **Batch normalization is effective when applied to deep networks.**