機械学習構文150

英語

Posted at 2025-03-19

It is quite natural that different machine learning models produce different predictions.
It seems that the model's loss will decrease soon after optimization.
It is said that deep learning models have good memory for recognizing patterns.
It never occurred to me that AI could generate realistic paintings.
It doesn't matter if training takes time as long as the model learns well.
It goes without saying that a larger dataset improves model performance.
I found it hard to fine-tune a deep learning model.
I took it for granted that a pre-trained model would work well on my dataset.
It took the AI model several hours to complete training.
It will cost a company a significant amount to develop a custom AI model.
It has been five years since deep learning became mainstream.
It will not be long before quantum computing influences machine learning.
It is easy for neural networks to overfit when trained on small datasets.
It is very kind of you to share your dataset for research.
The experiment is to be conducted using a reinforcement learning algorithm.
I was about to deploy the model when I found a bug.
Data quality and feature engineering have to do with model accuracy.
My model failed to generalize to unseen data.
He sat in the front in order to analyze the model’s training process.
He was kind enough to explain how backpropagation works.
The dataset was too large for me to process on my laptop.
I checked the model’s predictions, only to find they were biased.
She couldn't help but be amazed by AI-generated images.
You'll soon get used to training deep learning models.
There's no telling how AI will evolve in the next decade.
There is no point in using outdated models for modern tasks.
Neural networks are worth studying for their versatility.
I am in the habit of running machine learning experiments daily.
He makes a point of validating his model with real-world data.
Feeling frustrated, he adjusted the model's hyperparameters.
Shocked by the results, she retrained the model.
The training phase being complete, the model was deployed.
All things considered, transfer learning is highly efficient.
Judging by the performance metrics, the model is underfitting.
The algorithm looks simple considering its efficiency.
Geoffrey Hinton spent decades studying neural networks.
She had great difficulty debugging the neural network.
You just have to preprocess the data properly.
I have yet to implement reinforcement learning in my project.
He used to rely on traditional machine learning before switching to deep learning.
When I started coding, I would often forget to optimize my models.
He must have forgotten to normalize the dataset.
The researcher may well be interested in explainable AI.
Since cloud computing is available, we may as well train larger models.
You had better validate your model before deploying it.
I saw an AI model generating realistic text.
She heard her name mentioned in a machine learning paper.
My professor made me test the model with more data.
My mentor forced me to document my AI experiments properly.
I will have my assistant collect more training data.
She got the chatbot to generate meaningful responses.
Jane had her dataset corrupted due to a system crash.
The AI model helped me classify the images correctly.
There are many data points plotted on the graph.
Can you make yourself understood by the machine learning model?
The training process kept me waiting for a long time.
The AI was analyzing data with its parameters tuned.
The optimizer helps those who fine-tune their models.
I gave the model what little training data I had.
Feature engineering is to a model what fuel is to a car.
This model is not what it was before fine-tuning.
Optimization is what deep learning is all about.
Tell me what your AI experiment results were like.
This algorithm is what we call an "adaptive learning system."
This dataset is useful, and what is more, very diverse.
I adjusted the parameters, which improved accuracy.
She prefers decision trees, which preference I do not share.
The model failed to converge, as is often the case with small datasets.
A technique that I thought was outdated improved performance.
This model is as efficient as any state-of-the-art algorithm.
The new AI model is 25 times as fast as the previous version.
My dataset is ten times the size of the benchmark dataset.
This AI framework is as flexible as any deep learning library.
Nothing is as important to a neural network as quality data.
Model training is not so much a task as an iterative process.
The accuracy of this model is higher than that of the baseline.
The more data the model trains on, the better it performs.
I trust this prediction all the more because the model is robust.
This algorithm can no more generalize than a hard-coded rule can.
Feature selection is no less important than model selection.
This AI chip is no bigger than a coin.
If the dataset were larger, the model would generalize better.
If the model had more training epochs, it would have achieved better accuracy.
If the dataset should contain noise, the model will fail to learn properly.
If I were to build an AI system again, I would use a different architecture.
If it were not for labeled data, supervised learning would be impossible.
Without training data, a model cannot learn.
I wish my model could learn faster.
I would rather use PyTorch than TensorFlow.
This model behaves as if it were fully trained.
It's about time we evaluated the model on real-world data.
A good data scientist would not ignore feature selection.
Very few algorithms can achieve 100% accuracy.
His model contains few, if any, misclassified samples.
This AI is not a rule-based system but a deep learning model.
This model is not only accurate but also computationally efficient.
More training data is not necessarily better if it's noisy.
I don’t use this model only because it's popular.
We cannot be too careful when handling biased data.
The AI never makes a prediction without uncertainty.
The model's prediction is far from accurate.
Who knows what the AI will generate next?
This algorithm would be the last one to misclassify data.
The AI system knows better than to overfit on the training data.
Every neuron adjusted its weights so that the network could minimize loss.
The model was so complex that it took hours to train.
It was such a large dataset that processing took a long time.
Store backup data in case of a system failure.
He saved the weights lest the training process fail.
On finishing training, the model was tested on unseen data.
As soon as the loss reached a threshold, training stopped.
The optimizer had hardly adjusted parameters when convergence was achieved.
No sooner had I tuned the hyperparameters than accuracy improved.
We had not waited long before the model started performing well.
It was last week that we deployed the AI model.
What we need is high-quality training data, not just more samples.
All you have to do is fine-tune the model.
You can build whatever neural network architecture you want.
Little did I expect the model to generalize so well.
So large was the dataset that we needed distributed computing.
It was not until more epochs were added that the model improved.
"I use PyTorch." --- "So do I."
"I don’t use TensorFlow." --- "Neither do I."
It is true that deep learning requires large datasets, but preprocessing is also essential.
Even if the model underfits, we will still deploy it for testing.
Whether you fine-tune it or not, the pretrained model is already powerful.
To reduce loss is difficult, if not impossible.
I'm going to deploy the model today no matter what happens.
Whoever trains the model, wherever they train it, the results matter.
Confusing as the dataset was, the model learned meaningful patterns.
This dataset remains biased, while the new one is more balanced.
I tested two models. One used CNNs, and the other used transformers.
Some researchers favor decision trees, while others prefer neural networks.
Suppose that the training fails, how will you proceed?
You can use this dataset as long as it remains unbiased.
As far as we know, this is the best-performing model.
As far as I am concerned, I prefer reinforcement learning.
This AI won't generalize well unless it is trained on diverse data.
The fact is that interpretability matters in AI.
Chances are the model will fail in real-world scenarios.
Let's retrain the model. After all, the loss is still high.
Now that the model is trained, we can evaluate its performance.
That misclassification cost the company significant revenue.
A moment's analysis will reveal the underlying bias in the data.
This algorithm will guide you through the optimization process.
Data corruption prevented the model from being deployed.
Hyperparameter tuning keeps the training process optimized.
This loss function will make the model converge faster.
The researcher believes that deep learning should be explainable and that interpretability is crucial.
Batch normalization is effective when applied to deep networks.

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