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Hace 11 horas · DNN methods are implemented using the scikit-learn python library. The precision, recall, and F1 score outcomes for PI using DNN with various weight optimizations are displayed in Fig. ...
Hace 11 horas · Most discussions about Python classes that I recall having read seem to assume a common understanding of what they are, or at best provide descriptions rather than definitions. I think what I want know is this: Suppose I'm going to give you an object X and ask whether it's a class or not. Yes or no. You need to come up with a test before seeing X.
Hace 11 horas · This could involve measuring accuracy, precision, recall, or other evaluation criteria. Deployment: Implement the models into production systems or decision-making processes. This step may involve integrating the models with existing software infrastructure and monitoring their performance over time.
Hace 11 horas · In my previous post we built Prompt Injection Detector by training a LogisticRegression classifier on embedding of SPML Chatbot Prompt Injection Dataset.Today, we will look at how we can fine-tune an embedding model and then use LogisticRegression classifier. I learnt this technique from Chatper 11 of Hands-On Large Language Models book.I am enjoying this book.
Hace 11 horas · Personally, I've never been a big fan of Spam—or any canned meat for that matter. And although I grew up about an hour's drive from Spam Boulevard in the Northern Iowa town of Mason City, I can't recall my mother—or grandmother—ever opening a can of meat for breakfast, lunch or dinner and yelling, “Come and get it!”
Hace 11 horas · As the number of European Union (EU) visitors grows, implementing novel border control solutions, such as mobile devices for passenger identification for land and sea border control, becomes paramount to ensure the convenience and safety of passengers and officers. However, these devices, handling sensitive personal data, become attractive targets for malicious actors seeking to misuse or ...
Hace 11 horas · Image based detection and classification of skin cancer has been employed extensively in a number of studies with the help of CNN models. Paul Zhu [] implemented a skin cancer detection and classification into 9 types of skin cancers using the VGG-16 and VGG-19 models.The model was trained on 6,687 images of different types of skin cancers and achieved a maximum training accuracy of 92.90%.
