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Data Annotation and Labeling Tools: Tools like Labelbox, RectLabel, and SuperAnnotate assist in the annotation and labeling of training data for machine learning tasks. https://www.knowledgedetective.com/. [more]
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Chatbot Development Frameworks: Frameworks like Rasa, Dialogflow, and Microsoft Bot Framework aid in the development of conversational agents and chatbots. https://www.knowledgedetective.com/. [more]
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Automated Machine Learning (AutoML) Platforms: AutoML tools like Google Cloud AutoML, H2O.ai, and DataRobot automate the process of building and optimizing machine learning models, making it easier for non-experts to create AI solutions. https://www.knowledgedetective.com/. [more]
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Computer Vision Libraries: Tools like OpenCV and TensorFlow's Object Detection API enable tasks related to image and video analysis, object detection, and image recognition. https://www.knowledgedetective.com/. [more]
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Natural Language Processing (NLP) Tools: NLP tools such as NLTK, SpaCy, and Stanford CoreNLP offer functionalities for processing and understanding human language. https://www.knowledgedetective.com/. [more]
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Machine Learning Frameworks: Tools like TensorFlow, PyTorch, and scikit-learn provide libraries and APIs for building and training machine learning models. https://www.knowledgedetective.com/. [more]
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https://www.precisionbusinessinsights.com/market-reports/medical-plastics-market The global medical plastics market size was valued at USD 51,064.4 million in 2022 and is poised to grow at a significant CAGR of 7.9% during the forecast period 2023-29. It also includes market size and projection estimations for each of the five major regions from 2023 to 2029. The research report includes historical data, trending features, and market growth estimates for the future.. [more] -
5. Chat GPT's major function is to behave as a conversational agent, participating in interactive and dynamic interactions with users. It can react to inquiries, explain things, provide suggestions, and even collaborate on creative writing. Chat GPT evolves in real time as OpenAI fine-tunes and refines its models in response to user input and ongoing research. https://www.knowledgedetective.com/. [more]
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4. Using a big dataset, the training procedure includes predicting the next word in a phrase given the preceding terms. Chat GPT learns to create writing with fluency, syntax, and grasp of many topics by iteratively refining its predictions across several training stages. https://www.knowledgedetective.com/. [more]
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3. Chat GPT was trained using a vast quantity of heterogeneous text data from the internet, allowing it to learn a wide range of information and linguistic patterns. As a result, the model is able to create replies that are contextually appropriate, logical, and frequently indistinguishable from human-generated writing. https://www.knowledgedetective.com/. [more]
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2. Deep learning, especially a sort of neural network known as a transformer, is the core technology underpinning Chat GPT. Natural language processing problems have been transformed by transformers, which successfully capture the links between words and provide logical and contextually relevant replies. https://www.knowledgedetective.com/. [more]
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