设计官方网站,企业网站建设基本步骤,免版权费自建网站,网页ui设计模板用 Python 调用 GPT-3 API GPT-3 是去年由 Open AI 推出的语言机器学习模型。它因其能够写作、写歌、写诗#xff0c;甚至写代码而获得了广泛的媒体关注#xff01;该工具免费使用#xff0c;只需要注册一个电子邮件即可。 GPT-3 是一种叫 transformer 的机器学习模型。具体…用 Python 调用 GPT-3 API GPT-3 是去年由 Open AI 推出的语言机器学习模型。它因其能够写作、写歌、写诗甚至写代码而获得了广泛的媒体关注该工具免费使用只需要注册一个电子邮件即可。 GPT-3 是一种叫 transformer 的机器学习模型。具体来说它就是 Generative Pre-training Transformer因此叫做“GPT”。Transformer 架构使用自我注意和强化学习来模拟会话文本。通常它一次处理一个单词并使用前面的单词预测序列中的下一个单词。 GPT-3 具有广泛的应用场景涵盖科学、艺术和技术等所有领域。它可以用来回答有关科学和数学的基本问题。甚至可以准确回答研究生级别的数学和科学概念相关的问题。更令人惊讶的是我询问了一些与我的物理化学博士研究有关的问题它能够提供较好的解释。不过它也有其局限性。当我询问 GPT-3 有关物理化学中更新奇的研究方法时它无法提供明确的答案。因此在作为教育和研究的搜索引擎使用时应该谨慎使用 GPT-3。GPT-3 没有事实核查功能。随着事实核查功能的提高我可以想象 GPT-3 在研究生阶段甚至在研究领域将非常有用。 此外除了我个人的经验外我还看到了其他很多很酷的工具应用。例如一个开发人员使用 GPT-3 来编排完成复杂任务的云服务。其他用户使用 GPT-3 生成了工作的 python 和 SQL 脚本以及其他语言的程序。在艺术领域用户请 GPT-3 写一篇比较现代和当代艺术的文章。GPT-3 的潜在应用几乎在任何领域都是丰富的。 GPT-3 在回答有准确内容的基本问题方面表现得很好。例如它可以对光合作用做出相当不错的解释。它不能很好地回答关于光合作用的前沿研究问题例如它不能描述光合作用的机理和涉及的量子概念。它可以给出体面的回应但不太可能提供大多数研究问题的技术细节。同样GPT-3 可以编写一些简单的工作代码但是随着任务的复杂度增加生成的代码就越容易出错。它也不能生成政治观点、伦理价值观、投资建议、准确的新闻报道等通常是由人类生成的内容。 尽管 GPT-3 有其局限性但其广泛适用性令人印象深刻。我认为提出一些有趣的数据科学和机器学习提示以看看它们是否可以补充数据科学工作流程的部分是有趣的。 首先我们将根据一些简单的提示生成一些与数据科学有关的文本。一旦我们对该工具有了一些了解就可以询问一些可以帮助解决数据科学任务的问题。有几个有趣的数据科学和机器学习问题我们可以向 GPT-3 询问。例如是否可以使用 GPT-3 源自公开可用的数据集GPT-3 的训练数据有多少等。另一个有趣的应用是问题框架。 GPT-3 可以帮助用户构建良好的机器学习研究问题吗虽然它难以给出具体的技术答案但也许它可以很好地构建出未解决的研究问题。 另一个很酷的应用是使用 GPT-3 来决定用于特定应用程序的 ML 模型。这很好因为对于在线文献丰富的经过验证的技术它应该能够很好地帮助用户选择模型并解释为什么选定的模型最适合。最后我们可以尝试使用GPT-3 编写一些数据科学任务的 Python 代码。例如我们将看看是否可以使用它来编写生成特定用例的合成数据的代码。 注意GPT-3 API 的结果是不确定的。因此您获得的结果可能与此处显示的输出略有不同。此外由于 GPT-3 没有事实核查机制建议您对计划用于工作学校或个人项目的任何事实结果进行双重核查。 在这项工作中我将在 Deepnote 中编写代码它是一个协作数据科学笔记本使得运行可再现实验非常简单。 安装 GPT-3 首先让我们到 Deepnote 并创建一个新项目如果您还没有账户可以免费注册。 创建一个名为“GPT3”的项目以及该项目中的一个名为“GPT3_ds”的 notebook。 接下来我们在第一个单元中使用 pip 安装 OpenAI %pip install openai%pip install catboost 将密钥保存在 openAI 对象的 api_key 属性 import openaiopenai.api_key your-key 接下来就可以提问了比如问“什么是 Pandas 库”GP3 会给反馈 completion openai.Completion.create(enginetext-davinci-003, promptWhat is the pandas library?, max_tokens1000)print(completion.choices[0][text])# outputPandas is an open source software library written in Python for data manipulation and analysis. Pandas is widely used in data science, machine learning and many other fields. It provides high-level data structures and tools for handling and manipulating data, including data frames, series, plotting tools and more. 我们甚至可以询问更具体的问题例如“Pandas 的一些常见用途是什么”。它给出了合理的答案列出了数据整理、数据可视化、数据聚合和时间序列分析 completion openai.Completion.create(enginetext-davinci-003, promptwhat are some common Pandas use cases?, max_tokens240)print(completion.choices[0][text])# output1. Data Cleaning and Transformation2. Data Analysis and Exploration3. Time Series Analysis4. Data Visualization5. Statistical Modeling6. Predictive Modeling7. Machine Learning8. Web Scraping 询问“最常见的深度学习库是什么” #what are the most common deep learning libraries?completion openai.Completion.create(enginetext-davinci-003, promptwhat are the most common deep learning libraries?, max_tokens240)print(completion.choices[0][text])# output. TensorFlow 2. PyTorch 3. Keras 4. Caffe 5. CNTK 6. MXNet 7. Theano 8. Deeplearning4j 9. Gensim 10. LUNA 甚至可以询问“什么是深度神经网络” completion openai.Completion.create(enginetext-davinci-003, promptWhat is a deep neural network?, max_tokens1000)print(completion.choices[0][text])# outputA deep neural network (DNN) is a type of artificial neural network (ANN) with multiple layers of neurons between the input and output layers. DNNs are designed to learn complex non-linear relationships from data, and have been successfully applied in a wide range of areas such as image recognition, natural language processing, and financial forecasting. 使用 GPT-3 进行事实搜索应该谨慎进行因为没有任何事实核查机制。在大多数情况下如果您想获得关于数据科学和机器学习的众多在线博客和论坛中已知概念的表面理解GPT-3 应该能够很好地工作。 特征工程 GPT-3 的另一个有趣的应用案例是用作指导数据科学工作的辅助工具。例如GPT-3 能否给出一些改进模型性能的特征变换的想法让我们询问 GPT-3 completion openai.Completion.create(enginetext-davinci-003, promptgive some ideas on feature transformations that can improve model performance, max_tokens1000)print(completion.choices[0][text])# output1. Standardization/Normalization: A common feature transform used to ensure features are on the same scale, standardizing or normalizing variables can help limit the severity of outliers and improve the overall model performance.2. Feature Binning: Binning is a process of transforming numerical variables into categorical ones. This can be useful when working with variables that have too many levels and can have a significant effect on the model performance.3. Polynomial Expansion: When a nonlinear relationship is expected between features and the output variable, a polynomial expansion feature transformation can help improve model performance.4. Feature Selection: Removing redundant or irrelevant features from the dataset can help improve the model performance as these features may lead to overfitting.5. Ensemble: Combining different types of models (or different versions of the same model) can often improve performance due to their combined capabilities. 我们看到它给出了一些很好的特征变换建议以及每个变换的解释。 让我们看看是否可以更进一步。让它写一些 Python 代码 completion openai.Completion.create(enginetext-davinci-003, promptWrite example python code that performs data standardization, max_tokens1000)print(completion.choices[0][text])#output# Import the necessary librariesimport numpy as np# Define the data data np.array([[-3, 9, 0, 8], [ 4, 6, 5, 12], [20, 2, 3, 15]])# Calculate mean and standard deviationmean np.mean(data, axis0)std np.std(data, axis0)# Perform data standardizationstandardized_data (data - mean) / std# Print the resultsprint(standardized_data) 复制并粘贴到一个新单元格中并运行它 # Import the necessary librariesimport numpy as np# Define the data data np.array([[-3, 9, 0, 8], [ 4, 6, 5, 12], [20, 2, 3, 15]])# Calculate mean and standard deviationmean np.mean(data, axis0)std np.std(data, axis0)# Perform data standardizationstandardized_data (data - mean) / std# Print the resultsprint(standardized_data)# output[[-1.03881504 1.16247639 -1.29777137 -1.27872403] [-0.31164451 0.11624764 1.13554995 0.11624764] [ 1.35045955 -1.27872403 0.16222142 1.16247639]] 接下来让我们对特征标准化变换做同样的操作 completion openai.Completion.create(enginetext-davinci-003, promptWrite example python code that performs data normalization on fake data, max_tokens1000)print(completion.choices[0][text])# output# Normalizing data will rescale features in the range [0,1]data [3, 7, 10, 13] # Sample data# Calculate the maximum and minimum of the datamax_data max(data)min_data min(data)# Normalize the datanormalized_data [(x-min_data)/(max_data-min_data) for x in data]# Print first value to check print(normalized_data[0]) # Prints 0.2 执行返回的代码 # Normalizing data will rescale features in the range [0,1]data [3, 7, 10, 13] # Sample data# Calculate the maximum and minimum of the datamax_data max(data)min_data min(data)# Normalize the datanormalized_data [(x-min_data)/(max_data-min_data) for x in data]# Print first value to check print(normalized_data) 模型选择 另一个酷的应用是模型选择。让我们看看 GPT-3 是否可以指导我们选择机器学习模型。询问“如何选择时间序列模型” completion openai.Completion.create(enginetext-davinci-003, promptHow do i select a time series model?, max_tokens1000)print(completion.choices[0][text])# outputThe selection of a time series model depends on the type of data that is being analyzed. If there are long-term trends or cycles in the data, then an autoregressive integrated moving average (ARIMA) model or a exponential smoothing (ETS) model may be the best option. If the data are seasonal or there are outliers present, then a Holt-Winters model may be a better choice. If the data do not contain any long-term trends or seasonality, a Box-Jenkins model may be suitable. Regardless of the model selected, it is important to consider the type of data that is being modeled when selecting a time series model. 答案是合理的列出了平稳性、季节性、趋势以及模型性能评估的检查。让我们询问更具体的问题例如“何时使用 ARIMA vs Prophet” completion openai.Completion.create(enginetext-davinci-003, promptWhen to use ARIMA vs FB prophet?, max_tokens1000)print(completion.choices[0][text])# outputARIMA and FB Prophet are both used for time series forecasting. ARIMA is a linear model and is better for predicting long-term trends with stationary data. FB Prophet is a non-linear model and is better for predicting short-term trends with non-stationary data. So, you would use ARIMA when you are mainly concerned with predicting long-term trends, and you would use FB Prophet when you are mainly concerned with predicting short-term trends. 生成合成数据 能用 GPT-3 生成合成数据吗让我们看一看我们将生成一个众所周知的数据医院再入院和交易数据。 医院再入院数据 询问 GPT-3“编写 python 代码生成合成医院再入院数据”。 completion openai.Completion.create(enginetext-davinci-003, promptWrite example python code that generates synthetic healthcare readmission data stored in a dataframe, max_tokens1000)print(completion.choices[0][text])# outputimport pandas as pdimport numpy as np# Create Dataframe df pd.DataFrame(columns[Patient_ID, Age, Admission_Type, Readmitted])# Generate Datanp.random.seed(0)for i in range(10): admission_type np.random.choice([Urgent, Scheduled, Emergency]) patient_age np.random.randint(18, 80) readmission np.random.choice([0, 1]) df.loc[i] [i1, patient_age, admission_type, readmission]# Print Dataframe to Consoleprint(df) 执行此代码 import pandas as pdimport numpy as np# Create Dataframe df pd.DataFrame(columns[Patient_ID, Age, Admission_Type, Readmitted])# Generate Datanp.random.seed(0)for i in range(10): admission_type np.random.choice([Urgent, Scheduled, Emergency]) patient_age np.random.randint(18, 80) readmission np.random.choice([0, 1]) df.loc[i] [i1, patient_age, admission_type, readmission]# Print Dataframe to Consoledf 输出结果 让我们看看是否可以用这个合成数据构建一个分类模型预测重新入院的人并评估性能。 completion openai.Completion.create(enginetext-davinci-003, promptWrite example python code that generates synthetic healthcare readmission data stored in a dataframe. From this write code that builds a catboost model that predicts readmission outcomes. Also write code to calculate and print performance, max_tokens3000)print(completion.choices[0][text])# output metrics## Generate Synthetic Healthcare Readmission Dataimport pandas as pd import numpy as np # set the seed for reproducibility np.random.seed(1)# create dataframe df pd.DataFrame(np.random.randint(0, 100, size(100, 10)), columns[age,gender,length_of_stay,diagnosis,NIV,laboratory,past_hospitalizations,medications,bmi,readmission])# add labels to data frame df[age] np.random.randint(20, 80, size(100))df[gender] np.random.randint(1, 2, size(100))df[length_of_stay] np.random.randint(2, 14, size(100))df[diagnosis] np.random.randint(1, 5, size(100))df[NIV] np.random.randint(0, 2, size(100))df[laboratory] np.random.randint(1, 6, size(100))df[past_hospitalizations] np.random.randint(0, 10, size(100))df[medications] np.random.randint(1, 6, size(100))df[bmi] np.random.randint(18, 35, size(100))df[readmission] np.random.randint(0, 2, size(100))# print the dataframe print(df)## Build a CatBoost Modelfrom catboost import CatBoostClassifierfrom sklearn.metrics import confusion_matrix# separate X and yX df.iloc[:, 0:9]y df.iloc[:, 9]# initialize catboost classifier cat_clf CatBoostClassifier(iterations50, learning_rate0.3, depth8, eval_metricAccuracy, random_seed42)# fit the model cat_clf.fit(X, y)# predict valuesy_pred cat_clf.predict(X)# print confusion matrixconf_mat confusion_matrix(y, y_pred)print(conf_mat)## Calculate and Print Performance Metricsfrom sklearn.metrics import accuracy_score, precision_score, recall_score# calculate performance metricsacc accuracy_score(y, y_pred)precision precision_score(y, y_pred)recall recall_score(y, y_pred)# print performance metrics print(Accuracy: {}.format(acc))print(Precision: {}.format(precision))print(Recall: {}.format(recall)) 复制上面的代码并执行 ## Generate Synthetic Healthcare Readmission Dataimport pandas as pd import numpy as np # set the seed for reproducibility np.random.seed(1)# create dataframe df pd.DataFrame(np.random.randint(0, 100, size(100, 10)), columns[age,gender,length_of_stay,diagnosis,NIV,laboratory,past_hospitalizations,medications,bmi,readmission])# add labels to data frame df[age] np.random.randint(20, 80, size(100))df[gender] np.random.randint(1, 2, size(100))df[length_of_stay] np.random.randint(2, 14, size(100))df[diagnosis] np.random.randint(1, 5, size(100))df[NIV] np.random.randint(0, 2, size(100))df[laboratory] np.random.randint(1, 6, size(100))df[past_hospitalizations] np.random.randint(0, 10, size(100))df[medications] np.random.randint(1, 6, size(100))df[bmi] np.random.randint(18, 35, size(100))df[readmission] np.random.randint(0, 2, size(100))# print the dataframe print(df)## Build a CatBoost Modelfrom catboost import CatBoostClassifierfrom sklearn.metrics import confusion_matrix# separate X and yX df.iloc[:, 0:9]y df.iloc[:, 9]# initialize catboost classifier cat_clf CatBoostClassifier(iterations50, learning_rate0.3, depth8, eval_metricAccuracy, random_seed42)# fit the model cat_clf.fit(X, y)# predict valuesy_pred cat_clf.predict(X)# print confusion matrixconf_mat confusion_matrix(y, y_pred)print(conf_mat)## Calculate and Print Performance Metricsfrom sklearn.metrics import accuracy_score, precision_score, recall_score# calculate performance metricsacc accuracy_score(y, y_pred)precision precision_score(y, y_pred)recall recall_score(y, y_pred)# print performance metrics print(Accuracy: {}.format(acc))print(Precision: {}.format(precision))print(Recall: {}.format(recall))# output略 交易数据 询问 GPT-3“编写 Python 代码生成交易数据”。 completion openai.Completion.create(enginetext-davinci-003, promptWrite example python code that generates synthetic transaction data stored in a dataframe, max_tokens1000)print(completion.choices[0][text])# outputimport pandas as pd import numpy as np #create randomly generated customer datacustomer_id np.arange(1,101) customer_names [fJohn Doe {x} for x in range(1,101)] #create randomly generated transaction datatransaction_id np.arange(1,101)dates [f2020-07-{x} for x in range(1,101)]amounts np.random.randint(low1, high1000, size(100,)) #create dataframe with randomly generated datatransaction_data pd.DataFrame({Customer ID: customer_id, Customer Name: customer_names, Transaction ID: transaction_id, Date: dates, Amount: amounts})print(transaction_data) 拷贝代码并执行 import pandas as pd import numpy as np #create randomly generated customer datacustomer_id np.arange(1,101) customer_names [fJohn Doe {x} for x in range(1,101)] #create randomly generated transaction datatransaction_id np.arange(1,101)dates [f2020-07-{x} for x in range(1,101)]amounts np.random.randint(low1, high1000, size(100,)) #create dataframe with randomly generated datatransaction_data pd.DataFrame({Customer ID: customer_id, Customer Name: customer_names, Transaction ID: transaction_id, Date: dates, Amount: amounts})transaction_data 部分输出结果 现在有物品 ID、客户和购买金额。让我们看看是否可以更具体一点。再增加年龄、性别和邮政编码。 completion openai.Completion.create(enginetext-davinci-003, promptWrite example python code that generates synthetic transaction data stored in a dataframe. Include customer ID, transaction amount, item ID, item name, age, gender, and zipcode, max_tokens2000)print(completion.choices[0][text])# outputimport pandas as pdimport numpy as nprows [customer_ID, transaction_amnt, item_ID, item_name, age, gender, zipcode]data pd.DataFrame(columnsrows) for i in range(1,100): customer_ID int( np.random.uniform(100,600-100)) transaction_amnt np.random.uniform(1.25, 10.00) item_ID int( np.random.uniform(1,35)) item_name np.random.choice([phone, tablet, laptop, smartwatch]) age int( np.random.uniform(17,75)) gender np.random.choice([male, female]) zipcode np.random.choice([98101, 98200, 98469, 98801]) data.loc[i] [customer_ID, transaction_amnt, item_ID, item_name, age, gender, zipcode]print (data) 执行代码 import pandas as pdimport numpy as nprows [customer_ID, transaction_amnt, item_ID, item_name, age, gender, zipcode]data pd.DataFrame(columnsrows) for i in range(1,100): customer_ID int( np.random.uniform(100,600-100)) transaction_amnt np.random.uniform(1.25, 10.00) item_ID int( np.random.uniform(1,35)) item_name np.random.choice([phone, tablet, laptop, smartwatch]) age int( np.random.uniform(17,75)) gender np.random.choice([male, female]) zipcode np.random.choice([98101, 98200, 98469, 98801]) data.loc[i] [customer_ID, transaction_amnt, item_ID, item_name, age, gender, zipcode]data 部分输出结果 公共数据集的询问提示 另一种应用是询问 GPT-3 关于公共数据集。让我们询问 GPT-3 列出一些公共数据集 completion openai.Completion.create(enginetext-davinci-003, prompt list some good public datasets, max_tokens1000)print(completion.choices[0][text])# output1. US Census Data2. Enron Email Dataset3. Global Open Data Index4. Air Quality Monitoring Data5. New York City Taxi Trip Data6. IMF Data7. World Bank Open Data8. Google Books Ngrams Dataset9. Amazon Reviews Dataset10. UCI Machine Learning Repository 让我们看看是否可以找到根据 Apache 2.0 许可的公共数据。还询问源链接 completion openai.Completion.create(enginetext-davinci-003, prompt list some good public datasets under apache 2.0 license. provide links to their source, max_tokens1000, temperature0)print(completion.choices[0][text])# output1. OpenStreetMap: https://www.openstreetmap.org/2. US Census Data: https://www.census.gov/data.html3. Google Books Ngrams: https://aws.amazon.com/datasets/google-books-ngrams/4. Wikipedia: https://dumps.wikimedia.org/enwiki/5. US Government Spending Data: https://www.usaspending.gov/6. World Bank Open Data: https://data.worldbank.org/7. Common Crawl: http://commoncrawl.org/8. Open Images: https://storage.googleapis.com/openimages/web/index.html9. OpenFlights: https://openflights.org/data.html10. GDELT: http://data.gdeltproject.org/ 虽然并不是所有这些链接都是正确的但它在寻找源链接方面做得相当不错。Google Ngrams、Common Crawl和 NASA 数据都相当出色。如果不提供数据的确切位置在大多数情况下它提供了一个可以找到数据的网页链接。 再请求对数据进行描述。请注意虽然结果可能重叠但它们在每次运行时略有不同。据我所知结果并不总是可以相同的 completion openai.Completion.create(enginetext-davinci-003, prompt list some good public datasets under apache 2.0 license. provide links to their source and descriptions, max_tokens1000, temperature0)print(completion.choices[0][text])# output1. OpenStreetMap: OpenStreetMap is a free, editable map of the world, created and maintained by volunteers and available for use under an open license. It contains millions of data points, including roads, buildings, and points of interest. Source: https://www.openstreetmap.org/2. Google Books Ngrams: Google Books Ngrams is a dataset of over 5 million books from Google Books, spanning from 1500 to 2008. It contains word counts for each year, allowing researchers to track the usage of words over time. Source: https://aws.amazon.com/datasets/google-books-ngrams/3. Wikipedia: Wikipedia is a free, open-source encyclopedia with millions of articles in hundreds of languages. It is available for use under the Creative Commons Attribution-ShareAlike license. Source: https://www.wikipedia.org/4. Common Crawl: Common Crawl is a large-scale web crawl that collects data from over 5 billion webpages. It is available for use under the Apache 2.0 license. Source: https://commoncrawl.org/5. Open Images Dataset: The Open Images Dataset is a collection of 9 million images annotated with labels spanning over 6000 categories. It is available for use under the Apache 2.0 license. Source: https://storage.googleapis.com/openimages/web/index.html 机器学习问题整理 最后一个示例将看看 GPT-3 是否可以帮助我们整理机器学习问题。 询问用例 虽然 GPT-3 中的数据仅截至2021年但它仍然可以帮助我们构建与今天仍然相关的 ML 用例。让我们询问“社交媒体中有哪些新兴的机器学习用例”: completion openai.Completion.create(enginetext-davinci-003, promptWhat are some emerging machine learning use-cases in social media?, max_tokens1000, temperature0)print(completion.choices[0][text])# output1. Automated Content Curation: Automatically curating content from social media platforms to create personalized content feeds for users.2. Sentiment Analysis: Analyzing user sentiment from social media posts to gain insights into customer opinions and preferences.3. Social Media Monitoring: Using machine learning algorithms to monitor social media conversations and detect potential issues or trends.4. Social Media Advertising: Leveraging machine learning to optimize social media advertising campaigns and target the right audience.5. Social Media Recommendations: Using machine learning to recommend content to users based on their interests and preferences. 我们也可以看到在医疗领域的回复 completion openai.Completion.create(enginetext-davinci-003, promptWhat are some emerging machine learning use-cases in healthcare?, max_tokens1000, temperature0)print(completion.choices[0][text])# output1. Automated Diagnosis: Machine learning algorithms can be used to analyze patient data and medical images to detect and diagnose diseases.2. Personalized Medicine: Machine learning algorithms can be used to analyze patient data and medical images to create personalized treatment plans for each patient.3. Drug Discovery: Machine learning algorithms can be used to analyze large datasets of chemical compounds to identify potential new drugs.4. Clinical Decision Support: Machine learning algorithms can be used to analyze patient data and medical images to provide clinicians with real-time decision support.5. Predictive Analytics: Machine learning algorithms can be used to analyze patient data and medical images to predict future health outcomes. 询问研究项目 现在让我们看看它是否可以提出一些研究问题。让我们询问“使用深度学习进行图像检测有哪些好的研究项目” completion openai.Completion.create(enginetext-davinci-003, promptWhat are some good research questions on using deep learning for image detection?, max_tokens1000, temperature0)print(completion.choices[0][text])# output1. What are the most effective deep learning architectures for image detection?2. How can deep learning be used to improve the accuracy of image detection?3. What are the most effective methods for training deep learning models for image detection?4. How can deep learning be used to detect objects in images with varying levels of complexity?5. How can deep learning be used to detect objects in images with varying levels of illumination?6. How can deep learning be used to detect objects in images with varying levels of noise?7. How can deep learning be used to detect objects in images with varying levels of resolution?8. How can deep learning be used to detect objects in images with varying levels of occlusion?9. How can deep learning be used to detect objects in images with varying levels of background clutter?10. How can deep learning be used to detect objects in images with varying levels of rotation? 再问一下 NLP 方向 completion openai.Completion.create(enginetext-davinci-003, promptWhat are some good research questions related to NLP transformer models?, max_tokens1000, temperature0)print(completion.choices[0][text])# output1. How can transformer models be used to improve the accuracy of natural language processing tasks?2. What are the most effective methods for training transformer models for natural language processing tasks?3. How can transformer models be used to improve the efficiency of natural language processing tasks?4. What are the most effective methods for optimizing transformer models for natural language processing tasks?5. How can transformer models be used to improve the interpretability of natural language processing tasks?6. What are the most effective methods for deploying transformer models for natural language processing tasks?7. How can transformer models be used to improve the scalability of natural language processing tasks?8. What are the most effective methods for combining transformer models with other natural language processing techniques?9. How can transformer models be used to improve the robustness of natural language processing tasks?10. What are the most effective methods for evaluating transformer models for natural language processing tasks? 本文所有代码都发布在 GitHub 上。 原文链接https://medium.datadriveninvestor.com/mastering-chatgpt-in-python-a53814e834b0 本文由 mdnice 多平台发布
